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116 results on '"Alexeev V"'

1. Tweets versus broadsheets: Sentiment impact on stock markets around the world

Author: Gan, B, Alexeev, V, Yeung, D, Gan, B, Alexeev, V, and Yeung, D
Abstract: We contrast sentiment derived from social and news media to investigate its impact across 14 international markets. We find that heightened media sentiment during nontrading periods significantly affects the next day's opening returns even after accounting for the previous-day activity. Markedly, only the US market exhibits strong reactions to social media, whereas other markets are more responsive to the news. We find that most variability in overnight returns is explained by sentiment aggregated 3 h before markets open. Our findings suggest that the overnight sentiment does not simply subsume previous-day market activity but contains additional information that helps improve predictability in return forecasting models.
Published: 2024

2. Nonstandard Errors

Author: MENKVELD, AJ, DREBER, A, HOLZMEISTER, F, HUBER, J, JOHANNESSON, M, KIRCHLER, M, NEUSÜß, S, RAZEN, M, WEITZEL, U, ABAD‐DÍAZ, D, ABUDY, M, MANO, N, MARCHAL, A, MARTINEAU, C, MAZZOLA, F, MELOSO, D, MI, MG, MIHET, R, MOHAN, V, MOINAS, S, ANGEL, JJ, GEMAYEL, R, MOORE, D, MU, L, MURAVYEV, D, MURPHY, D, NESZVEDA, G, NEUMEIER, C, NIELSSON, U, NIMALENDRAN, M, NOLTE, S, NORDEN, LL, GERRITSEN, D, AVETIKIAN, AT, O'NEILL, P, OBAID, K, ØDEGAARD, BA, ÖSTBERG, P, PAGNOTTA, E, PAINTER, M, PALAN, S, PALIT, IJ, PARK, A, GIL‐BAZO, J, PASCUAL, R, BACH, A, PASQUARIELLO, P, PASTOR, L, PATEL, V, PATTON, AJ, PEARSON, ND, PELIZZON, L, PELLI, M, PELSTER, M, GILDER, D, PÉRIGNON, C, PFIFFER, C, BAIDOO, E, PHILIP, R, PLÍHAL, T, PRAKASH, P, PRESS, O, PRODROMOU, T, PROKOPCZUK, M, PUTNINS, T, GLOSTEN, LR, QIAN, Y, RAIZADA, G, RAKOWSKI, D, BAKALLI, G, RANALDO, A, REGIS, L, REITZ, S, RENAULT, T, RENJIE, RW, RENO, R, GOMEZ, T, RIDDIOUGH, SJ, RINNE, K, RINTAMÄKI, P, RIORDAN, R, BAO, L, RITTMANNSBERGER, T, LONGARELA, IR, ROESCH, D, ROGNONE, L, ROSEMAN, B, GORBENKO, A, ROŞU, I, ROY, S, RUDOLF, N, RUSH, SR, RZAYEV, K, BARBON, A, RZEŹNIK, AA, SANFORD, A, SANKARAN, H, SARKAR, A, GRAMMIG, J, SARNO, L, SCAILLET, O, SCHARNOWSKI, S, SCHENK‐HOPPÉ, KR, SCHERTLER, A, SCHNEIDER, M, BASHCHENKO, O, SCHROEDER, F, SCHÜRHOFF, N, SCHUSTER, P, GRÉGOIRE, V, SCHWARZ, MA, SEASHOLES, MS, SEEGER, NJ, SHACHAR, O, SHKILKO, A, SHUI, J, SIKIC, M, BINDRA, PC, SIMION, G, SMALES, LA, GÜÇBILMEZ, U, SÖDERLIND, P, SOJLI, E, SOKOLOV, K, SÖNKSEN, J, SPOKEVICIUTE, L, STEFANOVA, D, SUBRAHMANYAM, MG, SZASZI, B, BJØNNES, GH, TALAVERA, O, ADRIAN, T, TANG, Y, TAYLOR, N, THAM, W, THEISSEN, E, THIMME, J, TONKS, I, TRAN, H, TRAPIN, L, TROLLE, AB, BLACK, JR, HAGSTRÖMER, B, VADUVA, MA, VALENTE, G, VAN NESS, RA, VASQUEZ, A, VEROUSIS, T, VERWIJMEREN, P, VILHELMSSON, A, VILKOV, G, VLADIMIROV, V, VOGEL, S, HAMBUCKERS, J, BLACK, BS, VOIGT, S, WAGNER, W, WALTHER, T, WEISS, P, VAN DER WEL, M, WERNER, IM, WESTERHOLM, PJ, WESTHEIDE, C, WIKA, HC, HAPNES, E, WIPPLINGER, E, BOGOEV, D, WOLF, M, WOLFF, CCP, WOLK, L, WONG, W, WRAMPELMEYER, J, WU, Z, XIA, S, XIU, D, HARRIS, JH, XU, K, XU, C, CORREA, SB, YADAV, PK, YAGÜE, J, YAN, C, YANG, A, YOO, W, YU, W, YU, Y, HARRIS, L, YU, S, YUESHEN, BZ, YUFEROVA, D, BONDARENKO, O, ZAMOJSKI, M, ZAREEI, A, ZEISBERGER, SM, ZHANG, L, ZHANG, SS, ZHANG, X, HARTMANN, S, ZHAO, L, ZHONG, Z, ZHOU, ZI, ZHOU, C, BOS, CS, ZHU, XS, ZOICAN, M, ZWINKELS, R, BOSCH‐ROSA, C, BOURI, E, HASSE, J, BROWNLEES, C, CALAMIA, A, CAO, VN, CAPELLE‐BLANCARD, G, ROMERO, LMC, CAPORIN, M, CARRION, A, CASKURLU, T, CHAKRABARTY, B, CHEN, J, HAUTSCH, N, CHERNOV, M, CHEUNG, W, CHINCARINI, LB, CHORDIA, T, CHOW, S, CLAPHAM, B, COLLIARD, J, COMERTON‐FORDE, C, CURRAN, E, DAO, T, HE, X, DARE, W, DAVIES, RJ, DE BLASIS, R, DE NARD, GF, DECLERCK, F, DEEV, O, DEGRYSE, H, DEKU, SY, DESAGRE, C, VAN DIJK, MA, HEATH, D, DIM, C, DIMPFL, T, DONG, YJ, DRUMMOND, PA, DUDDA, T, DUEVSKI, T, DUMITRESCU, A, DYAKOV, T, DYHRBERG, AH, DZIELIŃSKI, M, AIT‐SAHALIA, Y, EKSI, A, KALAK, IE, ELLEN, ST, EUGSTER, N, EVANS, MDD, FARRELL, M, FELEZ‐VINAS, E, FERRARA, G, FERROUHI, EM, FLORI, A, HEDIGER, S, FLUHARTY‐JAIDEE, JT, FOLEY, SDV, FONG, KYL, FOUCAULT, T, FRANUS, T, FRANZONI, F, FRIJNS, B, FRÖMMEL, M, FU, SM, FÜLLBRUNN, SC, HENDERSHOTT, T, GAN, B, GAO, G, GEHRIG, TP, HIBBERT, AM, HJALMARSSON, E, HOELSCHER, SA, HOFFMANN, P, HOLDEN, CW, HORENSTEIN, AR, HUANG, W, HUANG, D, AKMANSOY, O, HURLIN, C, ILCZUK, K, IVASHCHENKO, A, IYER, SR, JAHANSHAHLOO, H, JALKH, N, JONES, CM, JURKATIS, S, JYLHÄ, P, KAECK, AT, ALCOCK, JT, KAISER, G, KARAM, A, KARMAZIENE, E, KASSNER, B, KAUSTIA, M, KAZAK, E, KEARNEY, F, VAN KERVEL, V, KHAN, SA, KHOMYN, MK, ALEXEEV, V, KLEIN, T, KLEIN, O, KLOS, A, KOETTER, M, KOLOKOLOV, A, KORAJCZYK, RA, KOZHAN, R, KRAHNEN, JP, KUHLE, P, KWAN, A, ALOOSH, A, LAJAUNIE, Q, LAM, FYEC, LAMBERT, M, LANGLOIS, H, LAUSEN, J, LAUTER, T, LEIPPOLD, M, LEVIN, V, LI, Y, LI, H, AMATO, L, LIEW, CY, LINDNER, T, LINTON, O, LIU, J, LIU, A, LLORENTE, G, LOF, M, LOHR, A, LONGSTAFF, F, LOPEZ‐LIRA, A, AMAYA, D, MANKAD, S, MENKVELD, AJ, DREBER, A, HOLZMEISTER, F, HUBER, J, JOHANNESSON, M, KIRCHLER, M, NEUSÜß, S, RAZEN, M, WEITZEL, U, ABAD‐DÍAZ, D, ABUDY, M, MANO, N, MARCHAL, A, MARTINEAU, C, MAZZOLA, F, MELOSO, D, MI, MG, MIHET, R, MOHAN, V, MOINAS, S, ANGEL, JJ, GEMAYEL, R, MOORE, D, MU, L, MURAVYEV, D, MURPHY, D, NESZVEDA, G, NEUMEIER, C, NIELSSON, U, NIMALENDRAN, M, NOLTE, S, NORDEN, LL, GERRITSEN, D, AVETIKIAN, AT, O'NEILL, P, OBAID, K, ØDEGAARD, BA, ÖSTBERG, P, PAGNOTTA, E, PAINTER, M, PALAN, S, PALIT, IJ, PARK, A, GIL‐BAZO, J, PASCUAL, R, BACH, A, PASQUARIELLO, P, PASTOR, L, PATEL, V, PATTON, AJ, PEARSON, ND, PELIZZON, L, PELLI, M, PELSTER, M, GILDER, D, PÉRIGNON, C, PFIFFER, C, BAIDOO, E, PHILIP, R, PLÍHAL, T, PRAKASH, P, PRESS, O, PRODROMOU, T, PROKOPCZUK, M, PUTNINS, T, GLOSTEN, LR, QIAN, Y, RAIZADA, G, RAKOWSKI, D, BAKALLI, G, RANALDO, A, REGIS, L, REITZ, S, RENAULT, T, RENJIE, RW, RENO, R, GOMEZ, T, RIDDIOUGH, SJ, RINNE, K, RINTAMÄKI, P, RIORDAN, R, BAO, L, RITTMANNSBERGER, T, LONGARELA, IR, ROESCH, D, ROGNONE, L, ROSEMAN, B, GORBENKO, A, ROŞU, I, ROY, S, RUDOLF, N, RUSH, SR, RZAYEV, K, BARBON, A, RZEŹNIK, AA, SANFORD, A, SANKARAN, H, SARKAR, A, GRAMMIG, J, SARNO, L, SCAILLET, O, SCHARNOWSKI, S, SCHENK‐HOPPÉ, KR, SCHERTLER, A, SCHNEIDER, M, BASHCHENKO, O, SCHROEDER, F, SCHÜRHOFF, N, SCHUSTER, P, GRÉGOIRE, V, SCHWARZ, MA, SEASHOLES, MS, SEEGER, NJ, SHACHAR, O, SHKILKO, A, SHUI, J, SIKIC, M, BINDRA, PC, SIMION, G, SMALES, LA, GÜÇBILMEZ, U, SÖDERLIND, P, SOJLI, E, SOKOLOV, K, SÖNKSEN, J, SPOKEVICIUTE, L, STEFANOVA, D, SUBRAHMANYAM, MG, SZASZI, B, BJØNNES, GH, TALAVERA, O, ADRIAN, T, TANG, Y, TAYLOR, N, THAM, W, THEISSEN, E, THIMME, J, TONKS, I, TRAN, H, TRAPIN, L, TROLLE, AB, BLACK, JR, HAGSTRÖMER, B, VADUVA, MA, VALENTE, G, VAN NESS, RA, VASQUEZ, A, VEROUSIS, T, VERWIJMEREN, P, VILHELMSSON, A, VILKOV, G, VLADIMIROV, V, VOGEL, S, HAMBUCKERS, J, BLACK, BS, VOIGT, S, WAGNER, W, WALTHER, T, WEISS, P, VAN DER WEL, M, WERNER, IM, WESTERHOLM, PJ, WESTHEIDE, C, WIKA, HC, HAPNES, E, WIPPLINGER, E, BOGOEV, D, WOLF, M, WOLFF, CCP, WOLK, L, WONG, W, WRAMPELMEYER, J, WU, Z, XIA, S, XIU, D, HARRIS, JH, XU, K, XU, C, CORREA, SB, YADAV, PK, YAGÜE, J, YAN, C, YANG, A, YOO, W, YU, W, YU, Y, HARRIS, L, YU, S, YUESHEN, BZ, YUFEROVA, D, BONDARENKO, O, ZAMOJSKI, M, ZAREEI, A, ZEISBERGER, SM, ZHANG, L, ZHANG, SS, ZHANG, X, HARTMANN, S, ZHAO, L, ZHONG, Z, ZHOU, ZI, ZHOU, C, BOS, CS, ZHU, XS, ZOICAN, M, ZWINKELS, R, BOSCH‐ROSA, C, BOURI, E, HASSE, J, BROWNLEES, C, CALAMIA, A, CAO, VN, CAPELLE‐BLANCARD, G, ROMERO, LMC, CAPORIN, M, CARRION, A, CASKURLU, T, CHAKRABARTY, B, CHEN, J, HAUTSCH, N, CHERNOV, M, CHEUNG, W, CHINCARINI, LB, CHORDIA, T, CHOW, S, CLAPHAM, B, COLLIARD, J, COMERTON‐FORDE, C, CURRAN, E, DAO, T, HE, X, DARE, W, DAVIES, RJ, DE BLASIS, R, DE NARD, GF, DECLERCK, F, DEEV, O, DEGRYSE, H, DEKU, SY, DESAGRE, C, VAN DIJK, MA, HEATH, D, DIM, C, DIMPFL, T, DONG, YJ, DRUMMOND, PA, DUDDA, T, DUEVSKI, T, DUMITRESCU, A, DYAKOV, T, DYHRBERG, AH, DZIELIŃSKI, M, AIT‐SAHALIA, Y, EKSI, A, KALAK, IE, ELLEN, ST, EUGSTER, N, EVANS, MDD, FARRELL, M, FELEZ‐VINAS, E, FERRARA, G, FERROUHI, EM, FLORI, A, HEDIGER, S, FLUHARTY‐JAIDEE, JT, FOLEY, SDV, FONG, KYL, FOUCAULT, T, FRANUS, T, FRANZONI, F, FRIJNS, B, FRÖMMEL, M, FU, SM, FÜLLBRUNN, SC, HENDERSHOTT, T, GAN, B, GAO, G, GEHRIG, TP, HIBBERT, AM, HJALMARSSON, E, HOELSCHER, SA, HOFFMANN, P, HOLDEN, CW, HORENSTEIN, AR, HUANG, W, HUANG, D, AKMANSOY, O, HURLIN, C, ILCZUK, K, IVASHCHENKO, A, IYER, SR, JAHANSHAHLOO, H, JALKH, N, JONES, CM, JURKATIS, S, JYLHÄ, P, KAECK, AT, ALCOCK, JT, KAISER, G, KARAM, A, KARMAZIENE, E, KASSNER, B, KAUSTIA, M, KAZAK, E, KEARNEY, F, VAN KERVEL, V, KHAN, SA, KHOMYN, MK, ALEXEEV, V, KLEIN, T, KLEIN, O, KLOS, A, KOETTER, M, KOLOKOLOV, A, KORAJCZYK, RA, KOZHAN, R, KRAHNEN, JP, KUHLE, P, KWAN, A, ALOOSH, A, LAJAUNIE, Q, LAM, FYEC, LAMBERT, M, LANGLOIS, H, LAUSEN, J, LAUTER, T, LEIPPOLD, M, LEVIN, V, LI, Y, LI, H, AMATO, L, LIEW, CY, LINDNER, T, LINTON, O, LIU, J, LIU, A, LLORENTE, G, LOF, M, LOHR, A, LONGSTAFF, F, LOPEZ‐LIRA, A, AMAYA, D, and MANKAD, S
Abstract: ABSTRACTIn statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
Published: 2024

3. Non-Standard Errors

Author: Menkveld, AJ, Dreber, A, Holzmeister, F, Huber, J, Johanneson, M, Kirchler, M, Razen, M, Weitzel, U, Abad, D, Abudy, MM, Adrian, T, Gregoire, V, Hagströmer, B, Hambuckers, J, Hapnes, E, Harris, JH, Harris, L, Hartmann, S, Hasse, J-B, Hautsch, N, He, X-ZT, Ait-Sahalia, Y, Heath, D, Hediger, S, Hendershott, TJ, Hibbert, AM, Hjalmarsson, E, Hoelscher, S, Hoffmann, P, Holden, CW, Horenstein, AR, Huang, W, Akmansoy, O, Huang, D, Hurlin, C, Ivashchenko, A, Iyer, SR, Jahanshahloo, H, Jalkh, N, Jones, CM, Jurkatis, S, Jylha, P, Kaeck, A, Alcock, J, Kaiser, G, Karam, A, Karmaziene, E, Kassner, B, Kaustia, M, Kazak, E, Kearney, F, van Kervel, V, Khan, S, Khomyn, M, Alexeev, V, Klein, T, Klein, O, Klos, A, Koetter, M, Krahnen, JP, Kolokolov, A, Korajczyk, RA, Kozhan, R, Kwan, A, Lajaunie, Q, Aloosh, A, Lam, FYEC, Lambert, M, Langlois, H, Lausen, J, Lauter, T, Leippold, M, Levin, V, Li, Y, Li, MH, Liew, CY, Amato, L, Lindner, T, Linton, OB, Liu, J, Liu, A, Llorente-Alvarez, J-G, Lof, M, Lohr, A, Longstaff, FA, Lopez-Lira, A, Mankad, S, Amaya, D, Mano, N, Marchal, A, Martineau, C, Mazzola, F, Meloso, DC, Mihet, R, Mohan, V, Moinas, S, Moore, D, Mu, L, Angel, JJ, Muravyev, D, Murphy, D, Neszveda, G, Neumeier, C, Nielsson, U, Nimalendran, M, Nolte, S, Nordén, LL, O'Neill, P, Obaid, K, Bach, A, Ødegaard, BA, Östberg, P, Painter, M, Palan, S, Palit, I, Park, A, Pascual Gascó, R, Pasquariello, P, Pastor, L, Patel, V, Baidoo, E, Patton, AJ, Pearson, ND, Pelizzon, L, Pelster, M, Pérignon, C, Pfiffer, C, Philip, R, Plíhal, T, Prakash, P, Press, O-A, Bakalli, G, Prodromou, T, Putnins, TJ, Raizada, G, Rakowski, DA, Ranaldo, A, Regis, L, Reitz, S, Renault, T, Wang, R, Renò, R, Barbon, A, Riddiough, S, Rinne, K, Rintamäki, P, Riordan, R, RITTMANNSBERGER, T, Rodríguez Longarela, I, Rösch, D, Rognone, L, Roseman, B, Rosu, I, Bashchenko, O, Roy, S, Rudolf, N, Rush, S, Rzayev, K, Rzeźnik, A, Sanford, A, Sankaran, H, Sarkar, A, Sarno, L, Scaillet, O, Bindra, PC, Scharnowski, S, Schenk-Hoppé, KR, Schertler, A, Schneider, M, Schroeder, F, Schürhoff, N, Schuster, P, Schwarz, MA, Seasholes, MS, Seeger, N, Bjonnes, GH, Shachar, O, Shkilko, A, Shui, J, Sikic, M, Simion, G, Smales, LA, Söderlind, P, Sojli, E, Sokolov, K, Spokeviciute, L, Black, JR, Stefanova, D, Subrahmanyam, MG, Neusüss, S, Szaszi, B, Talavera, O, Tang, Y, Taylor, N, Tham, WW, Theissen, E, Thimme, J, Black, BS, Tonks, I, Tran, H, Trapin, L, Trolle, AB, Vaduva, M, Valente, G, Van Ness, RA, Vasquez, A, Verousis, T, Verwijmeren, P, Bohorquez, S, Vilhelmsson, A, Vilkov, G, Vladimirov, V, Vogel, S, Voigt, S, Wagner, W, Walther, T, Weiss, P, van der Wel, M, Werner, IM, Bondarenko, O, Westerholm, PJ, Westheide, C, Wipplinger, E, Wolf, M, Wolff, CCP, Wolk, L, Wong, WK, Wrampelmeyer, J, Wu, Z-X, Xia, S, Bos, CS, Xiu, D, Xu, K, Xu, C, Yadav, PK, Yagüe, J, Yan, C, Yang, A, Yoo, W, Yu, W, Yu, S, Bosch-Rosa, C, Yueshen, BZ, Yuferova, D, Zamojski, M, Zareei, A, Zeisberger, S, Zhang, S, Zhang, X, Zhong, Z, Zhou, ZI, Zhou, C, Bouri, E, Zhu, X, Zoican, M, Zwinkels, RCJ, Chen, J, Duevski, T, Gao, G, Gemayel, R, Gilder, D, Kuhle, P, Pagnotta, E, Brownlees, CT, Pelli, M, Sönksen, J, Zhang, L, Ilczuk, K, Bogoev, D, Qian, Y, Wika, HC, Yu, Y, Zhao, L, Mi, M, Calamia, A, Bao, L, Cao, VN, Capelle-Blancard, G, Capera, L, Caporin, M, Carrion, A, Caskurlu, T, Chakrabarty, B, Chernov, M, Cheung, WMY, Chincarini, LB, Chordia, T, Chow, SC, Clapham, B, Colliard, J-E, Comerton-Forde, C, Curran, E, Dao, T, Dare, W, Davies, RJ, De Blasis, R, De Nard, G, Declerck, F, Deev, O, Degryse, H, Deku, S, Desagre, C, Van Dijk, MA, Dim, C, Dimpfl, T, Dong, YJ, Drummond, P, Dudda, T, Dumitrescu, A, Dyakov, T, Dyhrberg, AH, Dzieliński, M, Eksi, A, El Kalak, I, ter Ellen, S, Eugster, N, Evans, MDD, Farrell, M, Félez-Viñas, E, Ferrara, G, FERROUHI, EM, Flori, A, Fluharty-Jaidee, J, Foley, S, Fong, KYL, Foucault, T, Franus, T, Franzoni, FA, Frijns, B, Frömmel, M, Fu, S, Füllbrunn, S, Gan, B, Gehrig, T, Gerritsen, D, Gil-Bazo, J, Glosten, LR, Gomez, T, Gorbenko, A, Güçbilmez, U, Grammig, J, Menkveld, AJ, Dreber, A, Holzmeister, F, Huber, J, Johanneson, M, Kirchler, M, Razen, M, Weitzel, U, Abad, D, Abudy, MM, Adrian, T, Gregoire, V, Hagströmer, B, Hambuckers, J, Hapnes, E, Harris, JH, Harris, L, Hartmann, S, Hasse, J-B, Hautsch, N, He, X-ZT, Ait-Sahalia, Y, Heath, D, Hediger, S, Hendershott, TJ, Hibbert, AM, Hjalmarsson, E, Hoelscher, S, Hoffmann, P, Holden, CW, Horenstein, AR, Huang, W, Akmansoy, O, Huang, D, Hurlin, C, Ivashchenko, A, Iyer, SR, Jahanshahloo, H, Jalkh, N, Jones, CM, Jurkatis, S, Jylha, P, Kaeck, A, Alcock, J, Kaiser, G, Karam, A, Karmaziene, E, Kassner, B, Kaustia, M, Kazak, E, Kearney, F, van Kervel, V, Khan, S, Khomyn, M, Alexeev, V, Klein, T, Klein, O, Klos, A, Koetter, M, Krahnen, JP, Kolokolov, A, Korajczyk, RA, Kozhan, R, Kwan, A, Lajaunie, Q, Aloosh, A, Lam, FYEC, Lambert, M, Langlois, H, Lausen, J, Lauter, T, Leippold, M, Levin, V, Li, Y, Li, MH, Liew, CY, Amato, L, Lindner, T, Linton, OB, Liu, J, Liu, A, Llorente-Alvarez, J-G, Lof, M, Lohr, A, Longstaff, FA, Lopez-Lira, A, Mankad, S, Amaya, D, Mano, N, Marchal, A, Martineau, C, Mazzola, F, Meloso, DC, Mihet, R, Mohan, V, Moinas, S, Moore, D, Mu, L, Angel, JJ, Muravyev, D, Murphy, D, Neszveda, G, Neumeier, C, Nielsson, U, Nimalendran, M, Nolte, S, Nordén, LL, O'Neill, P, Obaid, K, Bach, A, Ødegaard, BA, Östberg, P, Painter, M, Palan, S, Palit, I, Park, A, Pascual Gascó, R, Pasquariello, P, Pastor, L, Patel, V, Baidoo, E, Patton, AJ, Pearson, ND, Pelizzon, L, Pelster, M, Pérignon, C, Pfiffer, C, Philip, R, Plíhal, T, Prakash, P, Press, O-A, Bakalli, G, Prodromou, T, Putnins, TJ, Raizada, G, Rakowski, DA, Ranaldo, A, Regis, L, Reitz, S, Renault, T, Wang, R, Renò, R, Barbon, A, Riddiough, S, Rinne, K, Rintamäki, P, Riordan, R, RITTMANNSBERGER, T, Rodríguez Longarela, I, Rösch, D, Rognone, L, Roseman, B, Rosu, I, Bashchenko, O, Roy, S, Rudolf, N, Rush, S, Rzayev, K, Rzeźnik, A, Sanford, A, Sankaran, H, Sarkar, A, Sarno, L, Scaillet, O, Bindra, PC, Scharnowski, S, Schenk-Hoppé, KR, Schertler, A, Schneider, M, Schroeder, F, Schürhoff, N, Schuster, P, Schwarz, MA, Seasholes, MS, Seeger, N, Bjonnes, GH, Shachar, O, Shkilko, A, Shui, J, Sikic, M, Simion, G, Smales, LA, Söderlind, P, Sojli, E, Sokolov, K, Spokeviciute, L, Black, JR, Stefanova, D, Subrahmanyam, MG, Neusüss, S, Szaszi, B, Talavera, O, Tang, Y, Taylor, N, Tham, WW, Theissen, E, Thimme, J, Black, BS, Tonks, I, Tran, H, Trapin, L, Trolle, AB, Vaduva, M, Valente, G, Van Ness, RA, Vasquez, A, Verousis, T, Verwijmeren, P, Bohorquez, S, Vilhelmsson, A, Vilkov, G, Vladimirov, V, Vogel, S, Voigt, S, Wagner, W, Walther, T, Weiss, P, van der Wel, M, Werner, IM, Bondarenko, O, Westerholm, PJ, Westheide, C, Wipplinger, E, Wolf, M, Wolff, CCP, Wolk, L, Wong, WK, Wrampelmeyer, J, Wu, Z-X, Xia, S, Bos, CS, Xiu, D, Xu, K, Xu, C, Yadav, PK, Yagüe, J, Yan, C, Yang, A, Yoo, W, Yu, W, Yu, S, Bosch-Rosa, C, Yueshen, BZ, Yuferova, D, Zamojski, M, Zareei, A, Zeisberger, S, Zhang, S, Zhang, X, Zhong, Z, Zhou, ZI, Zhou, C, Bouri, E, Zhu, X, Zoican, M, Zwinkels, RCJ, Chen, J, Duevski, T, Gao, G, Gemayel, R, Gilder, D, Kuhle, P, Pagnotta, E, Brownlees, CT, Pelli, M, Sönksen, J, Zhang, L, Ilczuk, K, Bogoev, D, Qian, Y, Wika, HC, Yu, Y, Zhao, L, Mi, M, Calamia, A, Bao, L, Cao, VN, Capelle-Blancard, G, Capera, L, Caporin, M, Carrion, A, Caskurlu, T, Chakrabarty, B, Chernov, M, Cheung, WMY, Chincarini, LB, Chordia, T, Chow, SC, Clapham, B, Colliard, J-E, Comerton-Forde, C, Curran, E, Dao, T, Dare, W, Davies, RJ, De Blasis, R, De Nard, G, Declerck, F, Deev, O, Degryse, H, Deku, S, Desagre, C, Van Dijk, MA, Dim, C, Dimpfl, T, Dong, YJ, Drummond, P, Dudda, T, Dumitrescu, A, Dyakov, T, Dyhrberg, AH, Dzieliński, M, Eksi, A, El Kalak, I, ter Ellen, S, Eugster, N, Evans, MDD, Farrell, M, Félez-Viñas, E, Ferrara, G, FERROUHI, EM, Flori, A, Fluharty-Jaidee, J, Foley, S, Fong, KYL, Foucault, T, Franus, T, Franzoni, FA, Frijns, B, Frömmel, M, Fu, S, Füllbrunn, S, Gan, B, Gehrig, T, Gerritsen, D, Gil-Bazo, J, Glosten, LR, Gomez, T, Gorbenko, A, Güçbilmez, U, and Grammig, J
Published: 2024

4. Non-Standard Errors

Author: Menkveld, A.J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Razen, M., Weitzel, G.U., Abad, D., Abudy, M., Adrian, T., Ait-Sahalia, Y., Akmansoy, O., Alcock, J., Alexeev, V., Aloosh, A., Amato, L., Amaya, D., Angel, J., Bach, A., Baidoo, E., Bakalli, G., Barbon, A., Bashchenko, O., Bindra, P.C., Bjonnes, G.H., Black, J., Black, B.S., Bohorquez, S., Bondarenko, O., Bos, C.S., Bosch-Rosa, C., Bouri, E., Brownlees, C.T., Calamia, A., Cao, V.N., Capelle-Blancard, G., Capera, L., Caporin, M., Carrion, A., Caskurlu, T., Chakrabarty, B., Chernov, M., Cheung, W.M., Chincarini, L.B., Chordia, T., Chow, S.C., Clapham, B., Colliard, J.-E., Comerton-Forde, C., Curran, E., Dao, T., Dare, W., Davies, R.J., Blasis, R. De, Nard, G. De, DeClerck, F., Deev, O., Degryse, H., Deku, S., Desagre, C., Dijk, M.A van, Dim, C., Dimpfl, T., Dong, Y., Drummond, P., Dudda, T., Dumitrescu, A., Dyakov, T., Dyhrberg, A.H., Dzieliński, M., Eksi, A., Kalak, I. El, Ellen, S. ter, Eugster, N., Evans, M.D.D., Farrell, M., Félez-Viñas, E., Ferrara, G., Ferrouhi, E.M., Flori, A., Fluharty-Jaidee, J., Foley, S., Fong, K.Y.L., Foucault, T., Franus, T., Franzoni, F.A., Frijns, B., Frömmel, M., Fu, S., Füllbrunn, S.C., Gan, B., Gehrig, T., Gerritsen, D., Gil-Bazo, J., Glosten, L.R., Gomez, T., Gorbenko, A., Güçbilmez, U., Grammig, J., Gregoire, V., Hagströmer, B., Hambuckers, J., Hapnes, E., Harris, J.H., Harris, L., Hartmann, S., Hasse, J.-B., Hautsch, N., He, X., Heath, D., Hediger, S., Hendershott, T., Hibbert, A.M., Hjalmarsson, E., Hoelscher, S.A., Hoffmann, P., Holden, C.W., Horenstein, A.R., Huang, W., Huang, D., Hurlin, C., Ivashchenko, A., Iyer, S.R., Jahanshahloo, H., Jalkh, N., Jones, C.M., Jurkatis, S., Jylha, P., Kaeck, A., Kaiser, G., Karam, A., Karmaziene, E., Kassner, B., Kaustia, M., Kazak, E., Kearney, F., Kervel, V. van, Khan, S., Khomyn, M., Klein, T., Klein, O., Klos, A., Koetter, M., Krahnen, J.P., Kolokolov, A., Korajczyk, R.A., Kozhan, R., Kwan, A., Lajaunie, Q., Lam, F.E.C., Lambert, M., Langlois, H., Lausen, J., Lauter, T., Leippold, M., Levin, V., Li, Y., Li, H., Liew, C.Y., Lindner, T., Linton, O.B., Liu, J., Liu, A., Llorente, G., Lof, M., Lohr, A., Longstaff, F.A., Lopez-Lira, A., Mankad, S., Mano, N., Marchal, A., Martineau, C., Mazzola, F., Meloso, D., Mihet, R., Mohan, V., Moinas, S., Moore, D., Mu, L., Muravyev, D., Murphy, D., Neszveda, G., Neumeier, C., Nielsson, U., Nimalendran, M., Nolte, S., Norden, L.L., O'Neill, P., Obaid, K., Ødegaard, B.A., Östberg, P., Painter, M., Palan, S., Palit, I., Park, A., Pascual, R., Pasquariello, P., Pastor, L., Patel, V., Patton, A.J., Pearson, N.D., Pelizzon, L., Pelster, M., Pérignon, C., Pfiffer, C., Philip, R., Plíhal, T., Prakash, P., Press, O.-A., Prodromou, T., Putniņš, T.J., Raizada, G., Rakowski, D.A., Ranaldo, A., Regis, L., Reitz, S., Renault, T., Renjie, R.W., Renò, R., Riddiough, S., Rinne, K., Rintamäki, P., Riordan, R., Rittmannsberger, T., Rodríguez-Longarela, I., Rösch, D., Rognone, L., Roseman, B., Rosu, I., Roy, S., Rudolf, N., Rush, S., Rzayev, K., Rzeźnik, A., Sanford, A., Sankaran, H., Sarkar, A., Sarno, L., Scaillet, O., Scharnowski, S., Schenk-Hoppé, K.R., Schertler, A., Schneider, M., Schroeder, F., Schürhoff, N., Schuster, P., Schwarz, M.A., Seasholes, M.S., Seeger, N., Shachar, O., Shkilko, A., Shui, J., Sikic, M., Simion, G., Smales, L.A., Söderlind, P., Sojli, E., Sokolov, K., Spokeviciute, L., Stefanova, D., Subrahmanyam, M.G., Neusüss, S., Szaszi, B., Talavera, O., Tang, Y., Taylor, N., Tham, W.W., Theissen, E., Thimme, J., Tonks, I., Tran, H., Trapin, L., Trolle, A.B., Valente, G., Ness, R.A. Van, Vasquez, A., Verousis, T., Verwijmeren, P., Vilhelmsson, A., Vilkov, G., Vladimirov, V., Vogel, S., Voigt, S., Wagner, W., Walther, T., Weiss, P., Wel, M. van der, Werner, I.M., Westerholm, P.J., Westheide, C., Wipplinger, E., Wolf, M., Wolff, C.C.P., Wolk, L., Wong, W.-K., Wrampelmeyer, J., Xia, S., Xiu, D., Xu, K., Xu, C., Yadav, P.K., Yagüe, J., Yan, C., Yang, A., Yoo, W., Yu, W., Yu, S., Yueshen, B.Z., Yuferova, D., Zamojski, M., Zareei, A., Zeisberger, S.M., Zhang, S.S., Zhang, X., Zhong, Z., Zhou, Z.I., Zhou, C., Zhu, X.S., Zoican, M., Zwinkels, R.C.J., Chen, J., Duevski, T., Gao, G., Gemayel, R., Gilder, D. de, Kuhle, P., Pagnotta, E., Pelli, M., Sönksen, J., Zhang, L., Ilczuk, K., Bogoev, D., Qian, Y., Wika, H.C., Yu, Y., Zhao, L., Mi, M., Bao, L., Vaduva, A., Prokopczuk, P., Avetikian, A., Wu, Z.-X., Menkveld, A.J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Razen, M., Weitzel, G.U., Abad, D., Abudy, M., Adrian, T., Ait-Sahalia, Y., Akmansoy, O., Alcock, J., Alexeev, V., Aloosh, A., Amato, L., Amaya, D., Angel, J., Bach, A., Baidoo, E., Bakalli, G., Barbon, A., Bashchenko, O., Bindra, P.C., Bjonnes, G.H., Black, J., Black, B.S., Bohorquez, S., Bondarenko, O., Bos, C.S., Bosch-Rosa, C., Bouri, E., Brownlees, C.T., Calamia, A., Cao, V.N., Capelle-Blancard, G., Capera, L., Caporin, M., Carrion, A., Caskurlu, T., Chakrabarty, B., Chernov, M., Cheung, W.M., Chincarini, L.B., Chordia, T., Chow, S.C., Clapham, B., Colliard, J.-E., Comerton-Forde, C., Curran, E., Dao, T., Dare, W., Davies, R.J., Blasis, R. De, Nard, G. De, DeClerck, F., Deev, O., Degryse, H., Deku, S., Desagre, C., Dijk, M.A van, Dim, C., Dimpfl, T., Dong, Y., Drummond, P., Dudda, T., Dumitrescu, A., Dyakov, T., Dyhrberg, A.H., Dzieliński, M., Eksi, A., Kalak, I. El, Ellen, S. ter, Eugster, N., Evans, M.D.D., Farrell, M., Félez-Viñas, E., Ferrara, G., Ferrouhi, E.M., Flori, A., Fluharty-Jaidee, J., Foley, S., Fong, K.Y.L., Foucault, T., Franus, T., Franzoni, F.A., Frijns, B., Frömmel, M., Fu, S., Füllbrunn, S.C., Gan, B., Gehrig, T., Gerritsen, D., Gil-Bazo, J., Glosten, L.R., Gomez, T., Gorbenko, A., Güçbilmez, U., Grammig, J., Gregoire, V., Hagströmer, B., Hambuckers, J., Hapnes, E., Harris, J.H., Harris, L., Hartmann, S., Hasse, J.-B., Hautsch, N., He, X., Heath, D., Hediger, S., Hendershott, T., Hibbert, A.M., Hjalmarsson, E., Hoelscher, S.A., Hoffmann, P., Holden, C.W., Horenstein, A.R., Huang, W., Huang, D., Hurlin, C., Ivashchenko, A., Iyer, S.R., Jahanshahloo, H., Jalkh, N., Jones, C.M., Jurkatis, S., Jylha, P., Kaeck, A., Kaiser, G., Karam, A., Karmaziene, E., Kassner, B., Kaustia, M., Kazak, E., Kearney, F., Kervel, V. van, Khan, S., Khomyn, M., Klein, T., Klein, O., Klos, A., Koetter, M., Krahnen, J.P., Kolokolov, A., Korajczyk, R.A., Kozhan, R., Kwan, A., Lajaunie, Q., Lam, F.E.C., Lambert, M., Langlois, H., Lausen, J., Lauter, T., Leippold, M., Levin, V., Li, Y., Li, H., Liew, C.Y., Lindner, T., Linton, O.B., Liu, J., Liu, A., Llorente, G., Lof, M., Lohr, A., Longstaff, F.A., Lopez-Lira, A., Mankad, S., Mano, N., Marchal, A., Martineau, C., Mazzola, F., Meloso, D., Mihet, R., Mohan, V., Moinas, S., Moore, D., Mu, L., Muravyev, D., Murphy, D., Neszveda, G., Neumeier, C., Nielsson, U., Nimalendran, M., Nolte, S., Norden, L.L., O'Neill, P., Obaid, K., Ødegaard, B.A., Östberg, P., Painter, M., Palan, S., Palit, I., Park, A., Pascual, R., Pasquariello, P., Pastor, L., Patel, V., Patton, A.J., Pearson, N.D., Pelizzon, L., Pelster, M., Pérignon, C., Pfiffer, C., Philip, R., Plíhal, T., Prakash, P., Press, O.-A., Prodromou, T., Putniņš, T.J., Raizada, G., Rakowski, D.A., Ranaldo, A., Regis, L., Reitz, S., Renault, T., Renjie, R.W., Renò, R., Riddiough, S., Rinne, K., Rintamäki, P., Riordan, R., Rittmannsberger, T., Rodríguez-Longarela, I., Rösch, D., Rognone, L., Roseman, B., Rosu, I., Roy, S., Rudolf, N., Rush, S., Rzayev, K., Rzeźnik, A., Sanford, A., Sankaran, H., Sarkar, A., Sarno, L., Scaillet, O., Scharnowski, S., Schenk-Hoppé, K.R., Schertler, A., Schneider, M., Schroeder, F., Schürhoff, N., Schuster, P., Schwarz, M.A., Seasholes, M.S., Seeger, N., Shachar, O., Shkilko, A., Shui, J., Sikic, M., Simion, G., Smales, L.A., Söderlind, P., Sojli, E., Sokolov, K., Spokeviciute, L., Stefanova, D., Subrahmanyam, M.G., Neusüss, S., Szaszi, B., Talavera, O., Tang, Y., Taylor, N., Tham, W.W., Theissen, E., Thimme, J., Tonks, I., Tran, H., Trapin, L., Trolle, A.B., Valente, G., Ness, R.A. Van, Vasquez, A., Verousis, T., Verwijmeren, P., Vilhelmsson, A., Vilkov, G., Vladimirov, V., Vogel, S., Voigt, S., Wagner, W., Walther, T., Weiss, P., Wel, M. van der, Werner, I.M., Westerholm, P.J., Westheide, C., Wipplinger, E., Wolf, M., Wolff, C.C.P., Wolk, L., Wong, W.-K., Wrampelmeyer, J., Xia, S., Xiu, D., Xu, K., Xu, C., Yadav, P.K., Yagüe, J., Yan, C., Yang, A., Yoo, W., Yu, W., Yu, S., Yueshen, B.Z., Yuferova, D., Zamojski, M., Zareei, A., Zeisberger, S.M., Zhang, S.S., Zhang, X., Zhong, Z., Zhou, Z.I., Zhou, C., Zhu, X.S., Zoican, M., Zwinkels, R.C.J., Chen, J., Duevski, T., Gao, G., Gemayel, R., Gilder, D. de, Kuhle, P., Pagnotta, E., Pelli, M., Sönksen, J., Zhang, L., Ilczuk, K., Bogoev, D., Qian, Y., Wika, H.C., Yu, Y., Zhao, L., Mi, M., Bao, L., Vaduva, A., Prokopczuk, P., Avetikian, A., and Wu, Z.-X.
Abstract: 31 mei 2023, Contains fulltext : 240328.pdf (Author’s version preprint ) (Open Access), In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Published: 2023

5. Integrated Variance of Irregularly Spaced High-Frequency Data: A State Space Approach Based on Pre-Averaging

Author: Alexeev, V, Chen, J, Ignatieva, K, Alexeev, V, Chen, J, and Ignatieva, K
Abstract: We propose a new state space model to estimate the Integrated Variance (IV) in the presence of microstructure noise. Applying the pre-averaging sampling scheme to the irregularly spaced high-frequency data, we derive equidistant efficient price approximations to calculate the noise-contaminated realised variance (NCRV), which is used as an IV estimator. The theoretical properties of the new volatility estimator are illustrated and compared with those of the realised volatility. We highlight the robustness of the new estimator to market microstructure noise (MMN). The pre-averaging sampling effectively eliminates the influence of the MMN component on the NCRV series. The empirical illustration features the EUR/USD exchange rate and provides evidence of a superior performance in volatility forecasting at very high sampling frequencies.
Published: 2023

6. Non-Standard Errors

Author: Menkveld, A.J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Razen, M., Weitzel, G.U., Abad, D., Abudy, M., Adrian, T., Ait-Sahalia, Y., Akmansoy, O., Alcock, J., Alexeev, V., Aloosh, A., Amato, L., Amaya, D., Angel, J., Bach, A., Baidoo, E., Bakalli, G., Barbon, A., Bashchenko, O., Bindra, P.C., Bjonnes, G.H., Black, J., Black, B.S., Bohorquez, S., Bondarenko, O., Bos, C.S., Bosch-Rosa, C., Bouri, E., Brownlees, C.T., Calamia, A., Cao, V.N., Capelle-Blancard, G., Capera, L., Caporin, M., Carrion, A., Caskurlu, T., Chakrabarty, B., Chernov, M., Cheung, W.M., Chincarini, L.B., Chordia, T., Chow, S.C., Clapham, B., Colliard, J.-E., Comerton-Forde, C., Curran, E., Dao, T., Dare, W., Davies, R.J., Blasis, R. De, Nard, G. De, DeClerck, F., Deev, O., Degryse, H., Deku, S., Desagre, C., Dijk, M.A van, Dim, C., Dimpfl, T., Dong, Y., Drummond, P., Dudda, T., Dumitrescu, A., Dyakov, T., Dyhrberg, A.H., Dzieliński, M., Eksi, A., Kalak, I. El, Ellen, S. ter, Eugster, N., Evans, M.D.D., Farrell, M., Félez-Viñas, E., Ferrara, G., Ferrouhi, E.M., Flori, A., Fluharty-Jaidee, J., Foley, S., Fong, K.Y.L., Foucault, T., Franus, T., Franzoni, F.A., Frijns, B., Frömmel, M., Fu, S., Füllbrunn, S.C., Gan, B., Gehrig, T., Gerritsen, D., Gil-Bazo, J., Glosten, L.R., Gomez, T., Gorbenko, A., Güçbilmez, U., Grammig, J., Gregoire, V., Hagströmer, B., Hambuckers, J., Hapnes, E., Harris, J.H., Harris, L., Hartmann, S., Hasse, J.-B., Hautsch, N., He, X., Heath, D., Hediger, S., Hendershott, T., Hibbert, A.M., Hjalmarsson, E., Hoelscher, S.A., Hoffmann, P., Holden, C.W., Horenstein, A.R., Huang, W., Huang, D., Hurlin, C., Ivashchenko, A., Iyer, S.R., Jahanshahloo, H., Jalkh, N., Jones, C.M., Jurkatis, S., Jylha, P., Kaeck, A., Kaiser, G., Karam, A., Karmaziene, E., Kassner, B., Kaustia, M., Kazak, E., Kearney, F., Kervel, V. van, Khan, S., Khomyn, M., Klein, T., Klein, O., Klos, A., Koetter, M., Krahnen, J.P., Kolokolov, A., Korajczyk, R.A., Kozhan, R., Kwan, A., Lajaunie, Q., Lam, F.E.C., Lambert, M., Langlois, H., Lausen, J., Lauter, T., Leippold, M., Levin, V., Li, Y., Li, H., Liew, C.Y., Lindner, T., Linton, O.B., Liu, J., Liu, A., Llorente, G., Lof, M., Lohr, A., Longstaff, F.A., Lopez-Lira, A., Mankad, S., Mano, N., Marchal, A., Martineau, C., Mazzola, F., Meloso, D., Mihet, R., Mohan, V., Moinas, S., Moore, D., Mu, L., Muravyev, D., Murphy, D., Neszveda, G., Neumeier, C., Nielsson, U., Nimalendran, M., Nolte, S., Norden, L.L., O'Neill, P., Obaid, K., Ødegaard, B.A., Östberg, P., Painter, M., Palan, S., Palit, I., Park, A., Pascual, R., Pasquariello, P., Pastor, L., Patel, V., Patton, A.J., Pearson, N.D., Pelizzon, L., Pelster, M., Pérignon, C., Pfiffer, C., Philip, R., Plíhal, T., Prakash, P., Press, O.-A., Prodromou, T., Putniņš, T.J., Raizada, G., Rakowski, D.A., Ranaldo, A., Regis, L., Reitz, S., Renault, T., Renjie, R.W., Renò, R., Riddiough, S., Rinne, K., Rintamäki, P., Riordan, R., Rittmannsberger, T., Rodríguez-Longarela, I., Rösch, D., Rognone, L., Roseman, B., Rosu, I., Roy, S., Rudolf, N., Rush, S., Rzayev, K., Rzeźnik, A., Sanford, A., Sankaran, H., Sarkar, A., Sarno, L., Scaillet, O., Scharnowski, S., Schenk-Hoppé, K.R., Schertler, A., Schneider, M., Schroeder, F., Schürhoff, N., Schuster, P., Schwarz, M.A., Seasholes, M.S., Seeger, N., Shachar, O., Shkilko, A., Shui, J., Sikic, M., Simion, G., Smales, L.A., Söderlind, P., Sojli, E., Sokolov, K., Spokeviciute, L., Stefanova, D., Subrahmanyam, M.G., Neusüss, S., Szaszi, B., Talavera, O., Tang, Y., Taylor, N., Tham, W.W., Theissen, E., Thimme, J., Tonks, I., Tran, H., Trapin, L., Trolle, A.B., Valente, G., Ness, R.A. Van, Vasquez, A., Verousis, T., Verwijmeren, P., Vilhelmsson, A., Vilkov, G., Vladimirov, V., Vogel, S., Voigt, S., Wagner, W., Walther, T., Weiss, P., Wel, M. van der, Werner, I.M., Westerholm, P.J., Westheide, C., Wipplinger, E., Wolf, M., Wolff, C.C.P., Wolk, L., Wong, W.-K., Wrampelmeyer, J., Xia, S., Xiu, D., Xu, K., Xu, C., Yadav, P.K., Yagüe, J., Yan, C., Yang, A., Yoo, W., Yu, W., Yu, S., Yueshen, B.Z., Yuferova, D., Zamojski, M., Zareei, A., Zeisberger, S.M., Zhang, S.S., Zhang, X., Zhong, Z., Zhou, Z.I., Zhou, C., Zhu, X.S., Zoican, M., Zwinkels, R.C.J., Chen, J., Duevski, T., Gao, G., Gemayel, R., Gilder, D. de, Kuhle, P., Pagnotta, E., Pelli, M., Sönksen, J., Zhang, L., Ilczuk, K., Bogoev, D., Qian, Y., Wika, H.C., Yu, Y., Zhao, L., Mi, M., Bao, L., Vaduva, A., Prokopczuk, P., Avetikian, A., Wu, Z.-X., Menkveld, A.J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Razen, M., Weitzel, G.U., Abad, D., Abudy, M., Adrian, T., Ait-Sahalia, Y., Akmansoy, O., Alcock, J., Alexeev, V., Aloosh, A., Amato, L., Amaya, D., Angel, J., Bach, A., Baidoo, E., Bakalli, G., Barbon, A., Bashchenko, O., Bindra, P.C., Bjonnes, G.H., Black, J., Black, B.S., Bohorquez, S., Bondarenko, O., Bos, C.S., Bosch-Rosa, C., Bouri, E., Brownlees, C.T., Calamia, A., Cao, V.N., Capelle-Blancard, G., Capera, L., Caporin, M., Carrion, A., Caskurlu, T., Chakrabarty, B., Chernov, M., Cheung, W.M., Chincarini, L.B., Chordia, T., Chow, S.C., Clapham, B., Colliard, J.-E., Comerton-Forde, C., Curran, E., Dao, T., Dare, W., Davies, R.J., Blasis, R. De, Nard, G. De, DeClerck, F., Deev, O., Degryse, H., Deku, S., Desagre, C., Dijk, M.A van, Dim, C., Dimpfl, T., Dong, Y., Drummond, P., Dudda, T., Dumitrescu, A., Dyakov, T., Dyhrberg, A.H., Dzieliński, M., Eksi, A., Kalak, I. El, Ellen, S. ter, Eugster, N., Evans, M.D.D., Farrell, M., Félez-Viñas, E., Ferrara, G., Ferrouhi, E.M., Flori, A., Fluharty-Jaidee, J., Foley, S., Fong, K.Y.L., Foucault, T., Franus, T., Franzoni, F.A., Frijns, B., Frömmel, M., Fu, S., Füllbrunn, S.C., Gan, B., Gehrig, T., Gerritsen, D., Gil-Bazo, J., Glosten, L.R., Gomez, T., Gorbenko, A., Güçbilmez, U., Grammig, J., Gregoire, V., Hagströmer, B., Hambuckers, J., Hapnes, E., Harris, J.H., Harris, L., Hartmann, S., Hasse, J.-B., Hautsch, N., He, X., Heath, D., Hediger, S., Hendershott, T., Hibbert, A.M., Hjalmarsson, E., Hoelscher, S.A., Hoffmann, P., Holden, C.W., Horenstein, A.R., Huang, W., Huang, D., Hurlin, C., Ivashchenko, A., Iyer, S.R., Jahanshahloo, H., Jalkh, N., Jones, C.M., Jurkatis, S., Jylha, P., Kaeck, A., Kaiser, G., Karam, A., Karmaziene, E., Kassner, B., Kaustia, M., Kazak, E., Kearney, F., Kervel, V. van, Khan, S., Khomyn, M., Klein, T., Klein, O., Klos, A., Koetter, M., Krahnen, J.P., Kolokolov, A., Korajczyk, R.A., Kozhan, R., Kwan, A., Lajaunie, Q., Lam, F.E.C., Lambert, M., Langlois, H., Lausen, J., Lauter, T., Leippold, M., Levin, V., Li, Y., Li, H., Liew, C.Y., Lindner, T., Linton, O.B., Liu, J., Liu, A., Llorente, G., Lof, M., Lohr, A., Longstaff, F.A., Lopez-Lira, A., Mankad, S., Mano, N., Marchal, A., Martineau, C., Mazzola, F., Meloso, D., Mihet, R., Mohan, V., Moinas, S., Moore, D., Mu, L., Muravyev, D., Murphy, D., Neszveda, G., Neumeier, C., Nielsson, U., Nimalendran, M., Nolte, S., Norden, L.L., O'Neill, P., Obaid, K., Ødegaard, B.A., Östberg, P., Painter, M., Palan, S., Palit, I., Park, A., Pascual, R., Pasquariello, P., Pastor, L., Patel, V., Patton, A.J., Pearson, N.D., Pelizzon, L., Pelster, M., Pérignon, C., Pfiffer, C., Philip, R., Plíhal, T., Prakash, P., Press, O.-A., Prodromou, T., Putniņš, T.J., Raizada, G., Rakowski, D.A., Ranaldo, A., Regis, L., Reitz, S., Renault, T., Renjie, R.W., Renò, R., Riddiough, S., Rinne, K., Rintamäki, P., Riordan, R., Rittmannsberger, T., Rodríguez-Longarela, I., Rösch, D., Rognone, L., Roseman, B., Rosu, I., Roy, S., Rudolf, N., Rush, S., Rzayev, K., Rzeźnik, A., Sanford, A., Sankaran, H., Sarkar, A., Sarno, L., Scaillet, O., Scharnowski, S., Schenk-Hoppé, K.R., Schertler, A., Schneider, M., Schroeder, F., Schürhoff, N., Schuster, P., Schwarz, M.A., Seasholes, M.S., Seeger, N., Shachar, O., Shkilko, A., Shui, J., Sikic, M., Simion, G., Smales, L.A., Söderlind, P., Sojli, E., Sokolov, K., Spokeviciute, L., Stefanova, D., Subrahmanyam, M.G., Neusüss, S., Szaszi, B., Talavera, O., Tang, Y., Taylor, N., Tham, W.W., Theissen, E., Thimme, J., Tonks, I., Tran, H., Trapin, L., Trolle, A.B., Valente, G., Ness, R.A. Van, Vasquez, A., Verousis, T., Verwijmeren, P., Vilhelmsson, A., Vilkov, G., Vladimirov, V., Vogel, S., Voigt, S., Wagner, W., Walther, T., Weiss, P., Wel, M. van der, Werner, I.M., Westerholm, P.J., Westheide, C., Wipplinger, E., Wolf, M., Wolff, C.C.P., Wolk, L., Wong, W.-K., Wrampelmeyer, J., Xia, S., Xiu, D., Xu, K., Xu, C., Yadav, P.K., Yagüe, J., Yan, C., Yang, A., Yoo, W., Yu, W., Yu, S., Yueshen, B.Z., Yuferova, D., Zamojski, M., Zareei, A., Zeisberger, S.M., Zhang, S.S., Zhang, X., Zhong, Z., Zhou, Z.I., Zhou, C., Zhu, X.S., Zoican, M., Zwinkels, R.C.J., Chen, J., Duevski, T., Gao, G., Gemayel, R., Gilder, D. de, Kuhle, P., Pagnotta, E., Pelli, M., Sönksen, J., Zhang, L., Ilczuk, K., Bogoev, D., Qian, Y., Wika, H.C., Yu, Y., Zhao, L., Mi, M., Bao, L., Vaduva, A., Prokopczuk, P., Avetikian, A., and Wu, Z.-X.
Abstract: Item does not contain fulltext, In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Published: 2022

7. Non-Standard Errors

Author: Menkveld, A.J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Razen, M., Weitzel, G.U., Abad, D., Abudy, M., Adrian, T., Ait-Sahalia, Y., Akmansoy, O., Alcock, J., Alexeev, V., Aloosh, A., Amato, L., Amaya, D., Angel, J., Bach, A., Baidoo, E., Bakalli, G., Barbon, A., Bashchenko, O., Bindra, P.C., Bjonnes, G.H., Black, J., Black, B.S., Bohorquez, S., Bondarenko, O., Bos, C.S., Bosch-Rosa, C., Bouri, E., Brownlees, C.T., Calamia, A., Cao, V.N., Capelle-Blancard, G., Capera, L., Caporin, M., Carrion, A., Caskurlu, T., Chakrabarty, B., Chernov, M., Cheung, W.M., Chincarini, L.B., Chordia, T., Chow, S.C., Clapham, B., Colliard, J.-E., Comerton-Forde, C., Curran, E., Dao, T., Dare, W., Davies, R.J., Blasis, R. De, Nard, G. De, DeClerck, F., Deev, O., Degryse, H., Deku, S., Desagre, C., Dijk, M.A van, Dim, C., Dimpfl, T., Dong, Y., Drummond, P., Dudda, T., Dumitrescu, A., Dyakov, T., Dyhrberg, A.H., Dzieliński, M., Eksi, A., Kalak, I. El, Ellen, S. ter, Eugster, N., Evans, M.D.D., Farrell, M., Félez-Viñas, E., Ferrara, G., Ferrouhi, E.M., Flori, A., Fluharty-Jaidee, J., Foley, S., Fong, K.Y.L., Foucault, T., Franus, T., Franzoni, F.A., Frijns, B., Frömmel, M., Fu, S., Füllbrunn, S.C., Gan, B., Gehrig, T., Gerritsen, D., Gil-Bazo, J., Glosten, L.R., Gomez, T., Gorbenko, A., Güçbilmez, U., Grammig, J., Gregoire, V., Hagströmer, B., Hambuckers, J., Hapnes, E., Harris, J.H., Harris, L., Hartmann, S., Hasse, J.-B., Hautsch, N., He, X., Heath, D., Hediger, S., Hendershott, T., Hibbert, A.M., Hjalmarsson, E., Hoelscher, S.A., Hoffmann, P., Holden, C.W., Horenstein, A.R., Huang, W., Huang, D., Hurlin, C., Ivashchenko, A., Iyer, S.R., Jahanshahloo, H., Jalkh, N., Jones, C.M., Jurkatis, S., Jylha, P., Kaeck, A., Kaiser, G., Karam, A., Karmaziene, E., Kassner, B., Kaustia, M., Kazak, E., Kearney, F., Kervel, V. van, Khan, S., Khomyn, M., Klein, T., Klein, O., Klos, A., Koetter, M., Krahnen, J.P., Kolokolov, A., Korajczyk, R.A., Kozhan, R., Kwan, A., Lajaunie, Q., Lam, F.E.C., Lambert, M., Langlois, H., Lausen, J., Lauter, T., Leippold, M., Levin, V., Li, Y., Li, H., Liew, C.Y., Lindner, T., Linton, O.B., Liu, J., Liu, A., Llorente, G., Lof, M., Lohr, A., Longstaff, F.A., Lopez-Lira, A., Mankad, S., Mano, N., Marchal, A., Martineau, C., Mazzola, F., Meloso, D., Mihet, R., Mohan, V., Moinas, S., Moore, D., Mu, L., Muravyev, D., Murphy, D., Neszveda, G., Neumeier, C., Nielsson, U., Nimalendran, M., Nolte, S., Norden, L.L., O'Neill, P., Obaid, K., Ødegaard, B.A., Östberg, P., Painter, M., Palan, S., Palit, I., Park, A., Pascual, R., Pasquariello, P., Pastor, L., Patel, V., Patton, A.J., Pearson, N.D., Pelizzon, L., Pelster, M., Pérignon, C., Pfiffer, C., Philip, R., Plíhal, T., Prakash, P., Press, O.-A., Prodromou, T., Putniņš, T.J., Raizada, G., Rakowski, D.A., Ranaldo, A., Regis, L., Reitz, S., Renault, T., Renjie, R.W., Renò, R., Riddiough, S., Rinne, K., Rintamäki, P., Riordan, R., Rittmannsberger, T., Rodríguez-Longarela, I., Rösch, D., Rognone, L., Roseman, B., Rosu, I., Roy, S., Rudolf, N., Rush, S., Rzayev, K., Rzeźnik, A., Sanford, A., Sankaran, H., Sarkar, A., Sarno, L., Scaillet, O., Scharnowski, S., Schenk-Hoppé, K.R., Schertler, A., Schneider, M., Schroeder, F., Schürhoff, N., Schuster, P., Schwarz, M.A., Seasholes, M.S., Seeger, N., Shachar, O., Shkilko, A., Shui, J., Sikic, M., Simion, G., Smales, L.A., Söderlind, P., Sojli, E., Sokolov, K., Spokeviciute, L., Stefanova, D., Subrahmanyam, M.G., Neusüss, S., Szaszi, B., Talavera, O., Tang, Y., Taylor, N., Tham, W.W., Theissen, E., Thimme, J., Tonks, I., Tran, H., Trapin, L., Trolle, A.B., Valente, G., Ness, R.A. Van, Vasquez, A., Verousis, T., Verwijmeren, P., Vilhelmsson, A., Vilkov, G., Vladimirov, V., Vogel, S., Voigt, S., Wagner, W., Walther, T., Weiss, P., Wel, M. van der, Werner, I.M., Westerholm, P.J., Westheide, C., Wipplinger, E., Wolf, M., Wolff, C.C.P., Wolk, L., Wong, W.-K., Wrampelmeyer, J., Xia, S., Xiu, D., Xu, K., Xu, C., Yadav, P.K., Yagüe, J., Yan, C., Yang, A., Yoo, W., Yu, W., Yu, S., Yueshen, B.Z., Yuferova, D., Zamojski, M., Zareei, A., Zeisberger, S.M., Zhang, S.S., Zhang, X., Zhong, Z., Zhou, Z.I., Zhou, C., Zhu, X.S., Zoican, M., Zwinkels, R.C.J., Chen, J., Duevski, T., Gao, G., Gemayel, R., Gilder, D. de, Kuhle, P., Pagnotta, E., Pelli, M., Sönksen, J., Zhang, L., Ilczuk, K., Bogoev, D., Qian, Y., Wika, H.C., Yu, Y., Zhao, L., Mi, M., Bao, L., Vaduva, A., Prokopczuk, P., Avetikian, A., Wu, Z.-X., Menkveld, A.J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Razen, M., Weitzel, G.U., Abad, D., Abudy, M., Adrian, T., Ait-Sahalia, Y., Akmansoy, O., Alcock, J., Alexeev, V., Aloosh, A., Amato, L., Amaya, D., Angel, J., Bach, A., Baidoo, E., Bakalli, G., Barbon, A., Bashchenko, O., Bindra, P.C., Bjonnes, G.H., Black, J., Black, B.S., Bohorquez, S., Bondarenko, O., Bos, C.S., Bosch-Rosa, C., Bouri, E., Brownlees, C.T., Calamia, A., Cao, V.N., Capelle-Blancard, G., Capera, L., Caporin, M., Carrion, A., Caskurlu, T., Chakrabarty, B., Chernov, M., Cheung, W.M., Chincarini, L.B., Chordia, T., Chow, S.C., Clapham, B., Colliard, J.-E., Comerton-Forde, C., Curran, E., Dao, T., Dare, W., Davies, R.J., Blasis, R. De, Nard, G. De, DeClerck, F., Deev, O., Degryse, H., Deku, S., Desagre, C., Dijk, M.A van, Dim, C., Dimpfl, T., Dong, Y., Drummond, P., Dudda, T., Dumitrescu, A., Dyakov, T., Dyhrberg, A.H., Dzieliński, M., Eksi, A., Kalak, I. El, Ellen, S. ter, Eugster, N., Evans, M.D.D., Farrell, M., Félez-Viñas, E., Ferrara, G., Ferrouhi, E.M., Flori, A., Fluharty-Jaidee, J., Foley, S., Fong, K.Y.L., Foucault, T., Franus, T., Franzoni, F.A., Frijns, B., Frömmel, M., Fu, S., Füllbrunn, S.C., Gan, B., Gehrig, T., Gerritsen, D., Gil-Bazo, J., Glosten, L.R., Gomez, T., Gorbenko, A., Güçbilmez, U., Grammig, J., Gregoire, V., Hagströmer, B., Hambuckers, J., Hapnes, E., Harris, J.H., Harris, L., Hartmann, S., Hasse, J.-B., Hautsch, N., He, X., Heath, D., Hediger, S., Hendershott, T., Hibbert, A.M., Hjalmarsson, E., Hoelscher, S.A., Hoffmann, P., Holden, C.W., Horenstein, A.R., Huang, W., Huang, D., Hurlin, C., Ivashchenko, A., Iyer, S.R., Jahanshahloo, H., Jalkh, N., Jones, C.M., Jurkatis, S., Jylha, P., Kaeck, A., Kaiser, G., Karam, A., Karmaziene, E., Kassner, B., Kaustia, M., Kazak, E., Kearney, F., Kervel, V. van, Khan, S., Khomyn, M., Klein, T., Klein, O., Klos, A., Koetter, M., Krahnen, J.P., Kolokolov, A., Korajczyk, R.A., Kozhan, R., Kwan, A., Lajaunie, Q., Lam, F.E.C., Lambert, M., Langlois, H., Lausen, J., Lauter, T., Leippold, M., Levin, V., Li, Y., Li, H., Liew, C.Y., Lindner, T., Linton, O.B., Liu, J., Liu, A., Llorente, G., Lof, M., Lohr, A., Longstaff, F.A., Lopez-Lira, A., Mankad, S., Mano, N., Marchal, A., Martineau, C., Mazzola, F., Meloso, D., Mihet, R., Mohan, V., Moinas, S., Moore, D., Mu, L., Muravyev, D., Murphy, D., Neszveda, G., Neumeier, C., Nielsson, U., Nimalendran, M., Nolte, S., Norden, L.L., O'Neill, P., Obaid, K., Ødegaard, B.A., Östberg, P., Painter, M., Palan, S., Palit, I., Park, A., Pascual, R., Pasquariello, P., Pastor, L., Patel, V., Patton, A.J., Pearson, N.D., Pelizzon, L., Pelster, M., Pérignon, C., Pfiffer, C., Philip, R., Plíhal, T., Prakash, P., Press, O.-A., Prodromou, T., Putniņš, T.J., Raizada, G., Rakowski, D.A., Ranaldo, A., Regis, L., Reitz, S., Renault, T., Renjie, R.W., Renò, R., Riddiough, S., Rinne, K., Rintamäki, P., Riordan, R., Rittmannsberger, T., Rodríguez-Longarela, I., Rösch, D., Rognone, L., Roseman, B., Rosu, I., Roy, S., Rudolf, N., Rush, S., Rzayev, K., Rzeźnik, A., Sanford, A., Sankaran, H., Sarkar, A., Sarno, L., Scaillet, O., Scharnowski, S., Schenk-Hoppé, K.R., Schertler, A., Schneider, M., Schroeder, F., Schürhoff, N., Schuster, P., Schwarz, M.A., Seasholes, M.S., Seeger, N., Shachar, O., Shkilko, A., Shui, J., Sikic, M., Simion, G., Smales, L.A., Söderlind, P., Sojli, E., Sokolov, K., Spokeviciute, L., Stefanova, D., Subrahmanyam, M.G., Neusüss, S., Szaszi, B., Talavera, O., Tang, Y., Taylor, N., Tham, W.W., Theissen, E., Thimme, J., Tonks, I., Tran, H., Trapin, L., Trolle, A.B., Valente, G., Ness, R.A. Van, Vasquez, A., Verousis, T., Verwijmeren, P., Vilhelmsson, A., Vilkov, G., Vladimirov, V., Vogel, S., Voigt, S., Wagner, W., Walther, T., Weiss, P., Wel, M. van der, Werner, I.M., Westerholm, P.J., Westheide, C., Wipplinger, E., Wolf, M., Wolff, C.C.P., Wolk, L., Wong, W.-K., Wrampelmeyer, J., Xia, S., Xiu, D., Xu, K., Xu, C., Yadav, P.K., Yagüe, J., Yan, C., Yang, A., Yoo, W., Yu, W., Yu, S., Yueshen, B.Z., Yuferova, D., Zamojski, M., Zareei, A., Zeisberger, S.M., Zhang, S.S., Zhang, X., Zhong, Z., Zhou, Z.I., Zhou, C., Zhu, X.S., Zoican, M., Zwinkels, R.C.J., Chen, J., Duevski, T., Gao, G., Gemayel, R., Gilder, D. de, Kuhle, P., Pagnotta, E., Pelli, M., Sönksen, J., Zhang, L., Ilczuk, K., Bogoev, D., Qian, Y., Wika, H.C., Yu, Y., Zhao, L., Mi, M., Bao, L., Vaduva, A., Prokopczuk, P., Avetikian, A., and Wu, Z.-X.
Abstract: Item does not contain fulltext, In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Published: 2022

8. Agreement of Analytical and Simulation-Based Estimates of the Required Land Depth in Climate Models

Author: Steinert, N. J., González Rouco, Jesús Fidel, Melo Aguilar, C. A., García Pereira, Félix, García Bustamante, Elena, Vrese, P., Alexeev, V., Jungclaus, J. H., Lorenz, S. J., Hagemann, S., Steinert, N. J., González Rouco, Jesús Fidel, Melo Aguilar, C. A., García Pereira, Félix, García Bustamante, Elena, Vrese, P., Alexeev, V., Jungclaus, J. H., Lorenz, S. J., and Hagemann, S.
Abstract: This work was supported by the projects IlModelS, project no. CGL2014-726 59644-R and GReatModelS, project no. RTI2018-102305-B-C21. The work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bm1026. Vladimir Alexeev was supported by the Interdisciplinary Research for Arctic Coastal Environments (InteRFACE) project through the Department of Energy, Office of Science, Biological and Environmental Research Program's Regional and Global Model Analysis program, and by NOAA project NA18OAR4590417. We also wish to thank Veronika Gayler for technical support on JSBACH and Christian Reick for helpful comments and discussion., Previous analytical and simulation-based analyses suggest that deeper land surface models are needed to realistically simulate the terrestrial thermal state in climate models, with implications for land-atmosphere interactions. Analytical approaches mainly focused on the subsurface propagation of harmonics such as the annual temperature signal, and a direct comparison with climate-change model output has been elusive. This study addresses the propagation of a harmonic pulse fitted to represent the timescale and amplitude of anthropogenic warming. Its comparison to land model simulations with stepwise increased bottom boundary depth leads to an agreement between the simulation-based and analytical frameworks for long-term climate trends. Any depth increase gradually decreases the relative error in the subsurface thermodynamics, and a minimum depth of 170 m is recommended to simulate the ground climate adequately. The approach provides an accurate estimate of the required land-model depth for climate-change simulations and assesses the relative bias in insufficiently deep land models. Plain Language Summary Many current-generation climate models have land components that are too shallow. Under climate change conditions, the long-term warming trend at the surface propagates deeper into the ground than the commonly used 3-10 m. Shallow models alter the terrestrial heat storage and distribution of temperatures in the subsurface, influencing the simulated land-atmosphere interactions. Previous studies focusing on annual timescales suggest that deeper models are required to match subsurface-temperature observations and the classic analytical heat conduction solution. However, for a systematic investigation of land-model deepening in the frame of anthropogenic climate change, the classic analytical solution is inaccurate because it does not mimic the timescale and amplitude of the simulated warming trend. This study intends to bridge the gap between analytical and simulatio, Ministerio de Economía y Competitividad (MINECO), Scientific Steering Committee (WLA), Interdisciplinary Research for Arctic Coastal Environments (InteRFACE) project through the Department of Energy, Office of Science, Biological and Environmental Research Program's Regional and Global Model Analysis program, NOAA project, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub
Published: 2021

9. Biases in variance of decomposed portfolio returns

Author: Alexeev, V, Ignatieva, K, Alexeev, V, and Ignatieva, K
Abstract: Significant portfolio variance biases arise when contrasting multiperiod portfolio returns based on the assumption of fixed continuously rebalanced portfolio weights as opposed to buy-and-hold weights. Empirical evidence obtained using S&P 500 constituents from 2003 to 2011 demonstrates that, compared with a buy-and-hold assumption, applying fixed weights led to decreased estimates of portfolio volatilities during 2003, 2005 and 2010, but caused a significant increase in volatility estimates in the more turbulent 2008 and 2011. This discrepancy distorts assessments of portfolio risk-adjusted performance when inappropriate weight assumptions are employed. Consequently, these variance biases have effect on statistical inference in factor models and may result in erroneous portfolio size recommendations for adequate diversification.
Published: 2021

10. This title is unavailable for guests, please login to see more information.

Author: Alexeev, V. A., Cai, L., Alexeev, V. A., and Cai, L.
Published: 2020

11. Biases in variance of decomposed portfolio returns

Author: Alexeev, V, Ignatieva, K, Alexeev, V, and Ignatieva, K
Abstract: Significant portfolio variance biases arise when contrasting multiperiod portfolio returns based on the assumption of fixed continuously rebalanced portfolio weights as opposed to buy‐and‐hold weights. Empirical evidence obtained using S&P 500 constituents from 2003 to 2011 demonstrates that, compared with a buy‐and‐hold assumption, applying fixed weights led to decreased estimates of portfolio volatilities during 2003, 2005 and 2010, but caused a significant increase in volatility estimates in the more turbulent 2008 and 2011. This discrepancy distorts assessments of portfolio risk‐adjusted performance when inappropriate weight assumptions are employed. Consequently, these variance biases have effect on statistical inference in factor models and may result in erroneous portfolio size recommendations for adequate diversification.
Published: 2020

12. Biases in Variance of Decomposed Portfolio Returns

Author: Alexeev, V, Ignatieva, KM, Alexeev, V, and Ignatieva, KM
Published: 2020

13. Dependence Modelling in Insurance via Copulas with Skewed Generalised Hyperbolic Marginals

Author: Alexeev, V, Ignatieva, K, Liyanage, T, Alexeev, V, Ignatieva, K, and Liyanage, T
Abstract: AbstractThis paper investigates dependence among insurance claims arising from different lines of business (LoBs). Using bivariate and multivariate portfolios of losses from different LoBs, we analyse the ability of various copulas in conjunction with skewed generalised hyperbolic (GH) marginals to capture the dependence structure between individual insurance risks forming an aggregate risk of the loss portfolio. The general form skewed GH distribution is shown to provide the best fit to univariate loss data. When modelling dependency between LoBs using one-parameter and mixture copula models, we favour models that are capable of generating upper tail dependence, that is, when several LoBs have a strong tendency to exhibit extreme losses simultaneously. We compare the selected models in their ability to quantify risks of multivariate portfolios. By performing an extensive investigation of the in- and out-of-sample Value-at-Risk (VaR) forecasts by analysing VaR exceptions (i.e. observations of realised portfolio value that are greater than the estimated VaR), we demonstrate that the selected models allow to reliably quantify portfolio risk. Our results provide valuable insights with regards to the nature of dependence and fulfils one of the primary objectives of the general insurance providers aiming at assessing total risk of an aggregate portfolio of losses when LoBs are correlated.
Published: 2020

14. Biases in Variance of Decomposed Portfolio Returns

Author: Alexeev, V, Ignatieva, KM, Alexeev, V, and Ignatieva, KM
Published: 2020

15. Modelling Financial Contagion Using High Frequency Data

Author: Yao, W, Dungey, M, Alexeev, V, Yao, W, Dungey, M, and Alexeev, V
Published: 2020

16. Sensitivity to sentiment: News vs social media

Author: Gan, B, Alexeev, V, Bird, R, Yeung, D, Gan, B, Alexeev, V, Bird, R, and Yeung, D
Abstract: © 2019 We explore the rapidly changing social and news media landscape that is responsible for the dissemination of information vital to the efficient functioning of the financial markets. Using the sheer volume of social and news media activity, commonly known as buzz, we document three distinct regimes. We find that between 2011 and 2013 the news media coverage stimulates activity in social media. This is followed by a transition period of two-way causality. From 2016, however, changes in levels of social media activity seem to lead and generate news coverage volumes. We uncover similar evolution of lead-lag pattern between sentiment measures constructed from the tonality contained in textual data from social and news media posts. We discover that market variables exert stronger impact on investor sentiment than the other way around. We also find that return responses to social media sentiment almost doubled after the transition period, while return responses to news-based sentiment almost halved to its pre-transition level. The linkage between volatility and sentiment is much more persistent than that between returns and sentiment. Overall, our results suggest that social media is becoming the dominant media source.
Published: 2020

17. Dependence Modelling in Insurance via Copulas with Skewed Generalised Hyperbolic Marginals

Author: Alexeev, V, Ignatieva, K, Liyanage, T, Alexeev, V, Ignatieva, K, and Liyanage, T
Abstract: © 2019 Walter de Gruyter GmbH, Berlin/Boston. This paper investigates dependence among insurance claims arising from different lines of business (LoBs). Using bivariate and multivariate portfolios of losses from different LoBs, we analyse the ability of various copulas in conjunction with skewed generalised hyperbolic (GH) marginals to capture the dependence structure between individual insurance risks forming an aggregate risk of the loss portfolio. The general form skewed GH distribution is shown to provide the best fit to univariate loss data. When modelling dependency between LoBs using one-parameter and mixture copula models, we favour models that are capable of generating upper tail dependence, that is, when several LoBs have a strong tendency to exhibit extreme losses simultaneously. We compare the selected models in their ability to quantify risks of multivariate portfolios. By performing an extensive investigation of the in- and out-of-sample Value-at-Risk (VaR) forecasts by analysing VaR exceptions (i.e. observations of realised portfolio value that are greater than the estimated VaR), we demonstrate that the selected models allow to reliably quantify portfolio risk. Our results provide valuable insights with regards to the nature of dependence and fulfils one of the primary objectives of the general insurance providers aiming at assessing total risk of an aggregate portfolio of losses when LoBs are correlated.
Published: 2020

18. Biases in variance of decomposed portfolio returns

Author: Alexeev, V, Ignatieva, K, Alexeev, V, and Ignatieva, K
Abstract: Significant portfolio variance biases arise when contrasting multiperiod portfolio returns based on the assumption of fixed continuously rebalanced portfolio weights as opposed to buy‐and‐hold weights. Empirical evidence obtained using S&P 500 constituents from 2003 to 2011 demonstrates that, compared with a buy‐and‐hold assumption, applying fixed weights led to decreased estimates of portfolio volatilities during 2003, 2005 and 2010, but caused a significant increase in volatility estimates in the more turbulent 2008 and 2011. This discrepancy distorts assessments of portfolio risk‐adjusted performance when inappropriate weight assumptions are employed. Consequently, these variance biases have effect on statistical inference in factor models and may result in erroneous portfolio size recommendations for adequate diversification.
Published: 2020

19. Modelling Financial Contagion Using High Frequency Data

Author: Yao, W, Dungey, M, Alexeev, V, Yao, W, Dungey, M, and Alexeev, V
Published: 2020

20. Sensitivity to sentiment: News vs social media

Author: Gan, B, Alexeev, V, Bird, R, Yeung, D, Gan, B, Alexeev, V, Bird, R, and Yeung, D
Abstract: © 2019 We explore the rapidly changing social and news media landscape that is responsible for the dissemination of information vital to the efficient functioning of the financial markets. Using the sheer volume of social and news media activity, commonly known as buzz, we document three distinct regimes. We find that between 2011 and 2013 the news media coverage stimulates activity in social media. This is followed by a transition period of two-way causality. From 2016, however, changes in levels of social media activity seem to lead and generate news coverage volumes. We uncover similar evolution of lead-lag pattern between sentiment measures constructed from the tonality contained in textual data from social and news media posts. We discover that market variables exert stronger impact on investor sentiment than the other way around. We also find that return responses to social media sentiment almost doubled after the transition period, while return responses to news-based sentiment almost halved to its pre-transition level. The linkage between volatility and sentiment is much more persistent than that between returns and sentiment. Overall, our results suggest that social media is becoming the dominant media source.
Published: 2020

21. Dependence Modelling in Insurance via Copulas with Skewed Generalised Hyperbolic Marginals

Author: Alexeev, V, Ignatieva, K, Liyanage, T, Alexeev, V, Ignatieva, K, and Liyanage, T
Abstract: © 2019 Walter de Gruyter GmbH, Berlin/Boston. This paper investigates dependence among insurance claims arising from different lines of business (LoBs). Using bivariate and multivariate portfolios of losses from different LoBs, we analyse the ability of various copulas in conjunction with skewed generalised hyperbolic (GH) marginals to capture the dependence structure between individual insurance risks forming an aggregate risk of the loss portfolio. The general form skewed GH distribution is shown to provide the best fit to univariate loss data. When modelling dependency between LoBs using one-parameter and mixture copula models, we favour models that are capable of generating upper tail dependence, that is, when several LoBs have a strong tendency to exhibit extreme losses simultaneously. We compare the selected models in their ability to quantify risks of multivariate portfolios. By performing an extensive investigation of the in- and out-of-sample Value-at-Risk (VaR) forecasts by analysing VaR exceptions (i.e. observations of realised portfolio value that are greater than the estimated VaR), we demonstrate that the selected models allow to reliably quantify portfolio risk. Our results provide valuable insights with regards to the nature of dependence and fulfils one of the primary objectives of the general insurance providers aiming at assessing total risk of an aggregate portfolio of losses when LoBs are correlated.
Published: 2020

22. An emerging international network focused on permafrost coastal systems in transition

Author: Jones, Benjamin M., Tweedie, Craig E., Xiao, Ming, Alexeev, V. A., Baranskaya, Alisa, Belova, N., Bristol, Emily, Bull, Diana L., Guangqing, Chi, Dallimore, Scott, Erikson, Li H., Farquharson, Louise M., Flanary, Chris, Frederick, Jennifer, Fuchs, Matthias, Gibbs, Ann, Graybill, Jessica, Grigoriev, Mikhail N., Grosse, Guido, Günther, Frank, Halvorsen, Kathleen E., Isaev, Vladimir, Irrgang, Anna, Iwahana, Go, Jensen, Anne M., Jones, Craig Alexander, Kanevskiy, Mikhail, Kasper, Jeremy, Kinsman, Nicole, Kokin, Osip, Kroon, Aart, Lantuit, Hugues, Lantz, Trevor C., Liljedahl, Anna K., Lubbad, Raed, Maio, Christopher, Maslakov, Alexey, McClelland, James W, Mota, Alejandro, Nitze, Ingmar, Novikova, Anna V., Ogorodov, Stanislav A., Overbeck, J., Overduin, Pier Paul, Petrov, Andrey N., Richmond, Bruce, Romanovsky, Vladimir E., Rowland, Joel, Sachs, Torsten, Schuur, Edward. A. G., Shabanova, Natalya N., Sinitsyn, Anatoly, Shiklomanov, Nikolay, Streletskiy, Dmitry, Strzelecki, Matt C., Swanson, David, Thomas, Matthew A., Jones, Benjamin M., Tweedie, Craig E., Xiao, Ming, Alexeev, V. A., Baranskaya, Alisa, Belova, N., Bristol, Emily, Bull, Diana L., Guangqing, Chi, Dallimore, Scott, Erikson, Li H., Farquharson, Louise M., Flanary, Chris, Frederick, Jennifer, Fuchs, Matthias, Gibbs, Ann, Graybill, Jessica, Grigoriev, Mikhail N., Grosse, Guido, Günther, Frank, Halvorsen, Kathleen E., Isaev, Vladimir, Irrgang, Anna, Iwahana, Go, Jensen, Anne M., Jones, Craig Alexander, Kanevskiy, Mikhail, Kasper, Jeremy, Kinsman, Nicole, Kokin, Osip, Kroon, Aart, Lantuit, Hugues, Lantz, Trevor C., Liljedahl, Anna K., Lubbad, Raed, Maio, Christopher, Maslakov, Alexey, McClelland, James W, Mota, Alejandro, Nitze, Ingmar, Novikova, Anna V., Ogorodov, Stanislav A., Overbeck, J., Overduin, Pier Paul, Petrov, Andrey N., Richmond, Bruce, Romanovsky, Vladimir E., Rowland, Joel, Sachs, Torsten, Schuur, Edward. A. G., Shabanova, Natalya N., Sinitsyn, Anatoly, Shiklomanov, Nikolay, Streletskiy, Dmitry, Strzelecki, Matt C., Swanson, David, and Thomas, Matthew A.
Abstract: Perennially frozen ground and sea ice are key constituents of permafrost coastal systems, and their presence is the primary difference between temperate and high-latitude coastal processes. These systems are some of the most rapidly changing landscapes on Earth and, in the Arctic, are representative of the challenges being faced at the intersection between natural and anthropogenic systems. Permafrost thaw, in combination with increasing sea level and decreasing sea-ice cover, exposes arctic coastal and nearshore areas to rapid environmental and social changes. Based on decadal timescales, observations in the Arctic indicate an increase in permafrost coastal bluff erosion and storm surge flooding of low-lying ice-rich permafrost terrain. However, circum-arctic observations remain limited and the factors responsible for the apparent increase in arctic coastal dynamics are poorly constrained. A better understanding of permafrost coastal systems and how they are responding to changes in the Arctic is important since a high proportion of Arctic residents live on or near coastlines, and many derive their livelihood from terrestrial and nearshore marine resources. An expanding industrial, scientific, and commercial presence in the Arctic Ocean will also require advanced knowledge about permafrost coastlines as terrestrial access points. Since the issues involved span political, cultural, geographical, and disciplinary borders, an international network focused on permafrost coastal systems in transition is needed. An integrative network focused on permafrost coastal systems is required to realize and address the scale and complexity of the processes, dynamics, and responses of this system to physical, ecological, and social change. A primary focus of such an effort would be guided by the fact that the issues and impacts associated with permafrost coastal systems in transition are far greater than any single institution or discipline is capable of addressing alone. Future p
Published: 2019

23. An emerging international network focused on permafrost coastal systems in transition

Author: Jones, Benjamin M., Tweedie, Craig E., Xiao, Ming, Alexeev, V. A., Baranskaya, Alisa, Belova, N., Bristol, Emily, Bull, Diana L., Guangqing, Chi, Dallimore, Scott, Erikson, Li H., Farquharson, Louise M., Flanary, Chris, Frederick, Jennifer, Fuchs, Matthias, Gibbs, Ann, Graybill, Jessica, Grigoriev, Mikhail N., Grosse, Guido, Günther, Frank, Halvorsen, Kathleen E., Isaev, Vladimir, Irrgang, Anna, Iwahana, Go, Jensen, Anne M., Jones, Craig Alexander, Kanevskiy, Mikhail, Kasper, Jeremy, Kinsman, Nicole, Kokin, Osip, Kroon, Aart, Lantuit, Hugues, Lantz, Trevor C., Liljedahl, Anna K., Lubbad, Raed, Maio, Christopher, Maslakov, Alexey, McClelland, James W, Mota, Alejandro, Nitze, Ingmar, Novikova, Anna V., Ogorodov, Stanislav A., Overbeck, J., Overduin, Pier Paul, Petrov, Andrey N., Richmond, Bruce, Romanovsky, Vladimir E., Rowland, Joel, Sachs, Torsten, Schuur, Edward. A. G., Shabanova, Natalya N., Sinitsyn, Anatoly, Shiklomanov, Nikolay, Streletskiy, Dmitry, Strzelecki, Matt C., Swanson, David, Thomas, Matthew A., Jones, Benjamin M., Tweedie, Craig E., Xiao, Ming, Alexeev, V. A., Baranskaya, Alisa, Belova, N., Bristol, Emily, Bull, Diana L., Guangqing, Chi, Dallimore, Scott, Erikson, Li H., Farquharson, Louise M., Flanary, Chris, Frederick, Jennifer, Fuchs, Matthias, Gibbs, Ann, Graybill, Jessica, Grigoriev, Mikhail N., Grosse, Guido, Günther, Frank, Halvorsen, Kathleen E., Isaev, Vladimir, Irrgang, Anna, Iwahana, Go, Jensen, Anne M., Jones, Craig Alexander, Kanevskiy, Mikhail, Kasper, Jeremy, Kinsman, Nicole, Kokin, Osip, Kroon, Aart, Lantuit, Hugues, Lantz, Trevor C., Liljedahl, Anna K., Lubbad, Raed, Maio, Christopher, Maslakov, Alexey, McClelland, James W, Mota, Alejandro, Nitze, Ingmar, Novikova, Anna V., Ogorodov, Stanislav A., Overbeck, J., Overduin, Pier Paul, Petrov, Andrey N., Richmond, Bruce, Romanovsky, Vladimir E., Rowland, Joel, Sachs, Torsten, Schuur, Edward. A. G., Shabanova, Natalya N., Sinitsyn, Anatoly, Shiklomanov, Nikolay, Streletskiy, Dmitry, Strzelecki, Matt C., Swanson, David, and Thomas, Matthew A.
Abstract: Perennially frozen ground and sea ice are key constituents of permafrost coastal systems, and their presence is the primary difference between temperate and high-latitude coastal processes. These systems are some of the most rapidly changing landscapes on Earth and, in the Arctic, are representative of the challenges being faced at the intersection between natural and anthropogenic systems. Permafrost thaw, in combination with increasing sea level and decreasing sea-ice cover, exposes arctic coastal and nearshore areas to rapid environmental and social changes. Based on decadal timescales, observations in the Arctic indicate an increase in permafrost coastal bluff erosion and storm surge flooding of low-lying ice-rich permafrost terrain. However, circum-arctic observations remain limited and the factors responsible for the apparent increase in arctic coastal dynamics are poorly constrained. A better understanding of permafrost coastal systems and how they are responding to changes in the Arctic is important since a high proportion of Arctic residents live on or near coastlines, and many derive their livelihood from terrestrial and nearshore marine resources. An expanding industrial, scientific, and commercial presence in the Arctic Ocean will also require advanced knowledge about permafrost coastlines as terrestrial access points. Since the issues involved span political, cultural, geographical, and disciplinary borders, an international network focused on permafrost coastal systems in transition is needed. An integrative network focused on permafrost coastal systems is required to realize and address the scale and complexity of the processes, dynamics, and responses of this system to physical, ecological, and social change. A primary focus of such an effort would be guided by the fact that the issues and impacts associated with permafrost coastal systems in transition are far greater than any single institution or discipline is capable of addressing alone. Future p
Published: 2019

24. Asymmetric jump beta estimation with implications for portfolio risk management

Author: Alexeev, V, Urga, G, Yao, W, Alexeev, V, Urga, G, and Yao, W
Abstract: © 2019 We evaluate the impact of extreme market shifts on equity portfolios and study the difference in negative and positive reactions to market jumps with implications for portfolio risk management. Employing high-frequency data for the constituents of the S&P500 index over the period 2 January 2003 to 30 December 2017, we investigate to what extent the portfolio exposure to the downside and upside jumps can be mitigated. We contrast the risk exposure of individual stocks with those of the portfolios as the number of holdings increases. Varying the jump identification threshold, we show that the number of holdings required to stabilise portfolios’ sensitivities to negative jumps is higher than when positive jumps are considered and that the asymmetry is more prominent for more extreme events. Ignoring this asymmetry results in under-diversification of portfolios and increases exposure to sudden extreme negative market shifts.
Published: 2019

25. Predictive blends: Fundamental Indexing meets Markowitz

Author: Pysarenko, S, Alexeev, V, Tapon, F, Pysarenko, S, Alexeev, V, and Tapon, F
Abstract: © 2019 Elsevier B.V. When constructing a portfolio of stocks, do you turn a blind eye to the firms’ future outlooks based on careful consideration of companies’ fundamentals, or do you ignore the stocks’ correlation structures which ensure the best diversification? The Fundamental Indexing (FI) and Markowitz mean-variance optimization (MVO) approaches are complementary but, until now, have been considered separately in the portfolio choice literature. Using data on S&P 500 constituents, we evaluate a novel portfolio construction technique that utilizes the benefits of both approaches. Relying on the idea of forecast averaging, we propose to blend the two previously mentioned techniques to provide investors with a clear binocular vision. The out-of-sample results of the blended portfolios attest to their superior performance when compared to common market benchmarks, and to portfolios constructed solely based on the FI or MVO methods. In pursuit of the optimal blend between the two distinct portfolio construction techniques, MVO and FI, we find that the ratio of market capitalization to GDP, being a leading indicator for an overpriced market, demonstrates remarkably advantageous properties. Our superior results cannot be explained by classic asset pricing models.
Published: 2019

26. Asymmetric jump beta estimation with implications for portfolio risk management

Author: Alexeev, V, Urga, G, Yao, W, Alexeev, V, Urga, G, and Yao, W
Abstract: © 2019 We evaluate the impact of extreme market shifts on equity portfolios and study the difference in negative and positive reactions to market jumps with implications for portfolio risk management. Employing high-frequency data for the constituents of the S&P500 index over the period 2 January 2003 to 30 December 2017, we investigate to what extent the portfolio exposure to the downside and upside jumps can be mitigated. We contrast the risk exposure of individual stocks with those of the portfolios as the number of holdings increases. Varying the jump identification threshold, we show that the number of holdings required to stabilise portfolios’ sensitivities to negative jumps is higher than when positive jumps are considered and that the asymmetry is more prominent for more extreme events. Ignoring this asymmetry results in under-diversification of portfolios and increases exposure to sudden extreme negative market shifts.
Published: 2019

27. Predictive blends: Fundamental Indexing meets Markowitz

Author: Pysarenko, S, Alexeev, V, Tapon, F, Pysarenko, S, Alexeev, V, and Tapon, F
Abstract: © 2019 Elsevier B.V. When constructing a portfolio of stocks, do you turn a blind eye to the firms’ future outlooks based on careful consideration of companies’ fundamentals, or do you ignore the stocks’ correlation structures which ensure the best diversification? The Fundamental Indexing (FI) and Markowitz mean-variance optimization (MVO) approaches are complementary but, until now, have been considered separately in the portfolio choice literature. Using data on S&P 500 constituents, we evaluate a novel portfolio construction technique that utilizes the benefits of both approaches. Relying on the idea of forecast averaging, we propose to blend the two previously mentioned techniques to provide investors with a clear binocular vision. The out-of-sample results of the blended portfolios attest to their superior performance when compared to common market benchmarks, and to portfolios constructed solely based on the FI or MVO methods. In pursuit of the optimal blend between the two distinct portfolio construction techniques, MVO and FI, we find that the ratio of market capitalization to GDP, being a leading indicator for an overpriced market, demonstrates remarkably advantageous properties. Our superior results cannot be explained by classic asset pricing models.
Published: 2019

28. Exchange rate risk exposure and the value of European firms

Author: Parlapiano, F, Alexeev, V, Dungey, M, Parlapiano, F, Alexeev, V, and Dungey, M
Abstract: This paper presents a new assessment of the exposure of European firms to exchange rate fluctuations which takes into account the potential common drivers of exchange rates and equity market conditions. Using monthly data for European firms from 1999 to 2011, we assess the impact of unexpected fluctuations in the USD, JPY, GBP and CHF against the Euro, and show that the proportion of firms subject to exchange rate risk is considerably larger when estimation accounts for potential common drivers and firm-specific factors than otherwise. Firm exposure to exchange rate risk is affected by the level of international involvement, industry, firm size and country of origin. European firms with largely domestic operations reveal the greatest vulnerability to unexpected exchange rate movements, suggesting an opportunity to improve risk management for these companies.
Published: 2017

29. Exchange rate risk exposure and the value of European firms

Author: Parlapiano, F, Alexeev, V, Dungey, M, Parlapiano, F, Alexeev, V, and Dungey, M
Abstract: This paper presents a new assessment of the exposure of European firms to exchange rate fluctuations which takes into account the potential common drivers of exchange rates and equity market conditions. Using monthly data for European firms from 1999 to 2011, we assess the impact of unexpected fluctuations in the USD, JPY, GBP and CHF against the Euro, and show that the proportion of firms subject to exchange rate risk is considerably larger when estimation accounts for potential common drivers and firm-specific factors than otherwise. Firm exposure to exchange rate risk is affected by the level of international involvement, industry, firm size and country of origin. European firms with largely domestic operations reveal the greatest vulnerability to unexpected exchange rate movements, suggesting an opportunity to improve risk management for these companies.
Published: 2017

30. Time-varying continuous and jump betas: The role of firm characteristics and periods of stress

Author: Alexeev, V, Dungey, M, Yao, W, Alexeev, V, Dungey, M, and Yao, W
Abstract: © 2016 Using high frequency data we decompose the time-varying beta for stocks into beta for continuous systematic risk and beta for discontinuous systematic risk. Estimated discontinuous betas for S&P500 constituents over 2003-2011 generally exceed the corresponding continuous betas. Smaller stocks are more sensitive to discontinuities than their larger counterparts, and during periods of financial distress, high leverage stocks are more exposed to systematic risk. Higher credit ratings and lower volatility are each associated with smaller betas. Industry effects are also apparent. We use the estimates to show that discontinuous risk carries a significantly positive premium, but continuous risk does not.
Published: 2017

31. Time-varying continuous and jump betas: The role of firm characteristics and periods of stress

Author: Alexeev, V, Dungey, M, Yao, W, Alexeev, V, Dungey, M, and Yao, W
Abstract: © 2016 Using high frequency data we decompose the time-varying beta for stocks into beta for continuous systematic risk and beta for discontinuous systematic risk. Estimated discontinuous betas for S&P500 constituents over 2003-2011 generally exceed the corresponding continuous betas. Smaller stocks are more sensitive to discontinuities than their larger counterparts, and during periods of financial distress, high leverage stocks are more exposed to systematic risk. Higher credit ratings and lower volatility are each associated with smaller betas. Industry effects are also apparent. We use the estimates to show that discontinuous risk carries a significantly positive premium, but continuous risk does not.
Published: 2017

32. Polar amplification as a preferred response in an idealized aquaplanet GCM

Author: Langen, Peter Lang, Alexeev, V. A., Langen, Peter Lang, and Alexeev, V. A.
Abstract: Udgivelsesdato: August
Published: 2007

33. Polar amplification as a preferred response in an idealized aquaplanet GCM

Author: Langen, Peter Lang, Alexeev, V. A., Langen, Peter Lang, and Alexeev, V. A.
Abstract: Udgivelsesdato: August
Published: 2007

34. Continuous and jump betas: Implications for portfolio diversification

Author: Alexeev, V, Dungey, M, Yao, W, Alexeev, V, Dungey, M, and Yao, W
Abstract: © 2016 by the authors; licensee MDPI, Basel, Switzerland. Using high-frequency data, we decompose the time-varying beta for stocks into beta for continuous systematic risk and beta for discontinuous systematic risk. Estimated discontinuous betas for S&P500 constituents between 2003 and 2011 generally exceed the corresponding continuous betas. We demonstrate how continuous and discontinuous betas decrease with portfolio diversification. Using an equiweighted broad market index, we assess the speed of convergence of continuous and discontinuous betas in portfolios of stocks as the number of holdings increase. We show that discontinuous risk dissipates faster with fewer stocks in a portfolio compared to its continuous counterpart.
Published: 2016

35. Continuous and jump betas: Implications for portfolio diversification

Author: Alexeev, V, Dungey, M, Yao, W, Alexeev, V, Dungey, M, and Yao, W
Abstract: © 2016 by the authors; licensee MDPI, Basel, Switzerland. Using high-frequency data, we decompose the time-varying beta for stocks into beta for continuous systematic risk and beta for discontinuous systematic risk. Estimated discontinuous betas for S&P500 constituents between 2003 and 2011 generally exceed the corresponding continuous betas. We demonstrate how continuous and discontinuous betas decrease with portfolio diversification. Using an equiweighted broad market index, we assess the speed of convergence of continuous and discontinuous betas in portfolios of stocks as the number of holdings increase. We show that discontinuous risk dissipates faster with fewer stocks in a portfolio compared to its continuous counterpart.
Published: 2016

36. Equity portfolio diversification with high frequency data

Author: Alexeev, V, Dungey, M, Alexeev, V, and Dungey, M
Abstract: © 2014 Taylor & Francis. Investors wishing to achieve a particular level of diversification may be misled on how many stocks to hold in a portfolio by assessing the portfolio risk at different data frequencies. High frequency intradaily data provide better estimates of volatility, which translate to more accurate assessment of portfolio risk. Using 5-min, daily and weekly data on S&P500 constituents for the period from 2003 to 2011, we find that for an average investor wishing to diversify away 85% (90%) of the risk, equally weighted portfolios of 7 (10) stocks will suffice, irrespective of the data frequency used or the time period considered. However, to assure investors of a desired level of diversification 90% of the time (in contrast to on average), using low frequency data results in an exaggerated number of stocks in a portfolio when compared with the recommendation based on 5-min data. This difference is magnified during periods when financial markets are in distress, as much as doubling during the 2007–2009 financial crisis.
Published: 2015

37. Equity portfolio diversification with high frequency data

Author: Alexeev, V, Dungey, M, Alexeev, V, and Dungey, M
Abstract: © 2014 Taylor & Francis. Investors wishing to achieve a particular level of diversification may be misled on how many stocks to hold in a portfolio by assessing the portfolio risk at different data frequencies. High frequency intradaily data provide better estimates of volatility, which translate to more accurate assessment of portfolio risk. Using 5-min, daily and weekly data on S&P500 constituents for the period from 2003 to 2011, we find that for an average investor wishing to diversify away 85% (90%) of the risk, equally weighted portfolios of 7 (10) stocks will suffice, irrespective of the data frequency used or the time period considered. However, to assure investors of a desired level of diversification 90% of the time (in contrast to on average), using low frequency data results in an exaggerated number of stocks in a portfolio when compared with the recommendation based on 5-min data. This difference is magnified during periods when financial markets are in distress, as much as doubling during the 2007–2009 financial crisis.
Published: 2015

38. The number of stocks in your portfolio should be larger than you think: diversification evidence from five developed markets

Author: Alexeev, V, Tapon, F, Alexeev, V, and Tapon, F
Published: 2014

39. Diversification, Canadian Style: How many stocks are enough for diversifying Canadian institutional portfolios?

Author: Alexeev, V, Tapon, F, Alexeev, V, and Tapon, F
Abstract: Portfolio risk is a function of the number of stocks held in portfolios. We simulate portfolios using daily observations for all traded and delisted equities in Canada from 1975 to 2011 and we calculate several measures of risk, including heavy-tailed to account for black swan events. For each risk measure, we calculate the average number of portfolio holdings and the upper limits of these holdings to assure investors of a specific reduction in diversifiable risk. In contrast to previous literature that suggests 10-15 stocks are enough to provide adequate diversification for an average investor, we find that in fact more than 50 stocks are needed to achieve the same level of diversification most of the time instead of on average.
Published: 2014

40. The number of stocks in your portfolio should be larger than you think: diversification evidence from five developed markets

Author: Alexeev, V, Tapon, F, Alexeev, V, and Tapon, F
Published: 2014

41. Diversification, Canadian Style: How many stocks are enough for diversifying Canadian institutional portfolios?

Author: Alexeev, V, Tapon, F, Alexeev, V, and Tapon, F
Abstract: Portfolio risk is a function of the number of stocks held in portfolios. We simulate portfolios using daily observations for all traded and delisted equities in Canada from 1975 to 2011 and we calculate several measures of risk, including heavy-tailed to account for black swan events. For each risk measure, we calculate the average number of portfolio holdings and the upper limits of these holdings to assure investors of a specific reduction in diversifiable risk. In contrast to previous literature that suggests 10-15 stocks are enough to provide adequate diversification for an average investor, we find that in fact more than 50 stocks are needed to achieve the same level of diversification most of the time instead of on average.
Published: 2014

42. What Australian investors need to know: To Diversity their portfolios

Author: Alexeev, V, Tapon, F, Alexeev, V, and Tapon, F
Abstract: An ASIC survey in 2008 showed that Australian investors do not diversify their portfolios sufficiently, with the average investor holding only 2.19 securities. To study this issue, we simulate portfolios using daily observations for all traded and delisted equities in Australia between 1975 and 2011. We calculate two measures of risk, including heavy tailed distributions to account for extreme events. For each risk measure, we recommend the number of portfolio holdings that result in a 90 per cent reduction in diversifiable risk for an average and a more conservative investor. We find that, on average, 24 to 30 stocks are sufficient to attain a well-diversified portfolio.
Published: 2013

43. What Australian investors need to know: To Diversity their portfolios

Author: Alexeev, V, Tapon, F, Alexeev, V, and Tapon, F
Abstract: An ASIC survey in 2008 showed that Australian investors do not diversify their portfolios sufficiently, with the average investor holding only 2.19 securities. To study this issue, we simulate portfolios using daily observations for all traded and delisted equities in Australia between 1975 and 2011. We calculate two measures of risk, including heavy tailed distributions to account for extreme events. For each risk measure, we recommend the number of portfolio holdings that result in a 90 per cent reduction in diversifiable risk for an average and a more conservative investor. We find that, on average, 24 to 30 stocks are sufficient to attain a well-diversified portfolio.
Published: 2013

44. The wind-driven current amplification over the Eastern flank of St.Anna Trough: observational data and results of numerical modeling

Author: Kirillov, S., Dmitrenko, I., Ivanov, V., Aksenov, Y., Alexeev, V., Kirillov, S., Dmitrenko, I., Ivanov, V., Aksenov, Y., and Alexeev, V.
Published: 2012

45. The wind-driven current amplification over the Eastern flank of St.Anna Trough: observational data and results of numerical modeling

Author: Kirillov, S., Dmitrenko, I., Ivanov, V., Aksenov, Y., Alexeev, V., Kirillov, S., Dmitrenko, I., Ivanov, V., Aksenov, Y., and Alexeev, V.
Published: 2012

46. Localized level crossing random walk test robust to the presence of structural breaks

Author: Alexeev, V, Maynard, A, Alexeev, V, and Maynard, A
Abstract: A modified version of the nonparametric level crossing random walk test is proposed, in which the crossing level is determined locally. This modification results in a test that is robust to unknown multiple structural breaks in the level and slope of the trend function under both the null and alternative hypotheses. No knowledge regarding the number or timing of the breaks is required. An algorithm is proposed to select the degree of localization in order to maximize bootstrapped power in a proximate model. A computational procedure is then developed to adjust the critical values for the effect of this selection procedure by replicating it under the null hypothesis. The test is applied to Canadian nominal inflation and nominal interest rate series with implications for the Fisher hypothesis. © 2010 Elsevier B.V. All rights reserved.
Published: 2012

47. Localized level crossing random walk test robust to the presence of structural breaks

Author: Alexeev, V, Maynard, A, Alexeev, V, and Maynard, A
Abstract: A modified version of the nonparametric level crossing random walk test is proposed, in which the crossing level is determined locally. This modification results in a test that is robust to unknown multiple structural breaks in the level and slope of the trend function under both the null and alternative hypotheses. No knowledge regarding the number or timing of the breaks is required. An algorithm is proposed to select the degree of localization in order to maximize bootstrapped power in a proximate model. A computational procedure is then developed to adjust the critical values for the effect of this selection procedure by replicating it under the null hypothesis. The test is applied to Canadian nominal inflation and nominal interest rate series with implications for the Fisher hypothesis. © 2010 Elsevier B.V. All rights reserved.
Published: 2012

48. NOWCAST: Fate of early 2000s Arctic warm water pulse.

Author: Polyakov, I. V., Alexeev, V. A., Ashik, I. M., Bacon, S., Beszczynska-Möller, Agnieszka, Carmack, E. C., Dmitrenko, I. A., Fortier, L., Gascard, J. C., Hansen, E., Hölemann, Jens, Ivanov, V. V., Kikuchi, T., Kirillov, S., Lenn, Y. D., McLaughlin, F. A., Piechura, J., Repina, I., Timokhov, L. A., Walczowski, W., Woodgate, R., Polyakov, I. V., Alexeev, V. A., Ashik, I. M., Bacon, S., Beszczynska-Möller, Agnieszka, Carmack, E. C., Dmitrenko, I. A., Fortier, L., Gascard, J. C., Hansen, E., Hölemann, Jens, Ivanov, V. V., Kikuchi, T., Kirillov, S., Lenn, Y. D., McLaughlin, F. A., Piechura, J., Repina, I., Timokhov, L. A., Walczowski, W., and Woodgate, R.
Published: 2011

49. NOWCAST: Fate of early 2000s Arctic warm water pulse.

Author: Polyakov, I. V., Alexeev, V. A., Ashik, I. M., Bacon, S., Beszczynska-Möller, Agnieszka, Carmack, E. C., Dmitrenko, I. A., Fortier, L., Gascard, J. C., Hansen, E., Hölemann, Jens, Ivanov, V. V., Kikuchi, T., Kirillov, S., Lenn, Y. D., McLaughlin, F. A., Piechura, J., Repina, I., Timokhov, L. A., Walczowski, W., Woodgate, R., Polyakov, I. V., Alexeev, V. A., Ashik, I. M., Bacon, S., Beszczynska-Möller, Agnieszka, Carmack, E. C., Dmitrenko, I. A., Fortier, L., Gascard, J. C., Hansen, E., Hölemann, Jens, Ivanov, V. V., Kikuchi, T., Kirillov, S., Lenn, Y. D., McLaughlin, F. A., Piechura, J., Repina, I., Timokhov, L. A., Walczowski, W., and Woodgate, R.
Published: 2011

50. Testing weak form efficiency on the Toronto Stock Exchange

Author: Alexeev, V, Tapon, F, Alexeev, V, and Tapon, F
Abstract: In order to test for weak form efficiency in the market a vast pool of individual stocks must be analyzed rather than a stock market index. In this paper, a model-based bootstrap is used to generate a series of simulated trials and a modified chart pattern recognition algorithm is applied to all stocks listed on the Toronto Stock Exchange (TSX). The number of patterns detected in the original price series is compared with the number of patterns found in the simulated series. By simulating the price path specific time dependencies present in real data are eliminated, making price changes purely random. Patterns, if consistently identified, carry information which adds value to the investment process, however, this informativeness does not guarantee profitability. Conclusions are drawn on the relative efficiency of some sectors of the economy. Although the null hypothesis of weak form efficiency on the TSX cannot be rejected, some sectors of the Canadian economy appear to be less efficient than others. In addition, pattern frequencies appear to be negatively dependent on the two moments of return distributions, variance and kurtosis. © 2011 Elsevier B.V.
Published: 2011

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