Search

The currency market is one of the main markets of the world's modern financial and economic systems. With respect to the availability of information, this market is the most data-rich and public. These two features allow it to be viewed as volatile. This study aims to assess the dependence of the currency market's volatility on the conditions and factors that formed during the COVID-19 pandemic. The research used statistical and correlation analysis and general scientific methods (notably, the abstraction method). The study examined the effects of pandemic events on currency market volatility, revealing a quantitative measure of approximately 2% of reactions in response to global economic and political events. Correlation analysis revealed an insignificant effect of the dollar index on the change in currency market volatility during the COVID-19 pandemic (r = 0.09). The outcomes indicated the impact of the disease factor on currency trading at the 0.8% level (r2 = 0.0081). The research conclusions suggest that the effects of information on the currency market differed during 2015-2019 and deviated in response to various political and economic events. The study highlights that the COVID-19 pandemic and its aftermath did not significantly influence the currency pairs' volatility. In the future, if crisis trends re-emerge, the findings of this study may influence the behavior of investors and participants in currency markets and global trade. This could affect their investment decisions, currency reserve diversification, insurance strategies, and capital migration. [ABSTRACT FROM AUTHOR]

Several factors influence the movements and dynamics of bond yields in financial markets. The determination of monetary policy, specifically the decisions regarding interest rates made by central banks, is a critical factor. Moreover, bond yields can be influenced by various factors such as geopolitical events, financial volatility, market sentiment, and investor risk appetite. These factors can impact the demand and supply dynamics in bond markets. This research aims to analyze the influence of Interest Rates, IDR to USD Exchange Rates, Volatility Index (VIX), Gold and Oil Prices on Bond Yields in Indonesia. The data used in this research is secondary data, which consists of time series data from 2019-2023. This research investigates the impact of financial and commodity prices on bond yields in Indonesia by using the autoregressive distributed lag (ARDL) model to examine both the long-run correlation and short-run effect. Empirical results found that Interest Rate, Volatility Index (VIX), and Oil Prices have a significant positive influence. Meanwhile, the Gold Price variable has a significant negative influence. This research has several crucial policy implications for investors concerning the national monetary policy, exchange rate fluctuation, and global volatility index to create profitable and sustainable portfolio strategies. Moreover, investment managers and investors should be concerned about the global commodities prices that will affect bond yield performances. This research contributes to the recent literature presenting causal relations of global volatility index (VIX) on Indonesian bond yield.

This article studies the economic impact of China and the U.S. from 2011 to 2023 through stock market linkages. A bivariate VAR model is used for nonlinear Granger causality analysis. The results show that the U.S. economy's dominance remains unshaken, impacting the world economy. Despite China's progress and significant role in the global economy, it cannot yet impact the U.S. economy. Understanding this is crucial for global economic patterns, the political state, and enriching global economic stability and development. [ABSTRACT FROM AUTHOR]

The currency market is one of the main markets of the world's modern financial and economic systems. With respect to the availability of information, this market is the most data-rich and public. These two features allow it to be viewed as volatile. This study aims to assess the dependence of the currency market's volatility on the conditions and factors that formed during the COVID-19 pandemic. The research used statistical and correlation analysis and general scientific methods. Correlation analysis revealed an insignificant effect of the dollar index on the change in currency market volatility during the COVID-19 pandemic (r = 0.09). The outcomes indicated the impact of the disease factor on currency trading at the 0.8% level (r2 = 0.0081). The research conclusions suggest that the effects of information on the currency market differed during 2015-2019 and deviated in response to various political and economic events. The study highlights that the COVID-19 pandemic and its aftermath did not significantly influence the currency pairs' volatility. In the future, if crisis trends re-emerge, the findings of this study may influence the behavior of investors and participants in global trade. This could affect their investment decisions, currency reserve diversification, insurance strategies, and capital migration.

Purpose: Since 2008, bitcoin has continued to attract investors due to its growing capitalization and opportunity for speculation. The purpose of this paper is to analyze the impact of bitcoin (BTC) on gold, the volatility index (VIX) and the dollar index (USDX). Design/methodology/approach: The series used are weekly and cover the period from January 2016 to November 2022. To generate the results, the unrestricted vector autoregression (VAR), structural vector autoregression (SVAR) and wavelet coherence were performed. Findings: The findings are mixed as not all tests show the exact effects of BTC in the three asset classes. However, common to all the tests is the significant influence that BTC maintains on gold and vice versa. The positive shock in BTC significantly increases the gold prices, confirmed in three different tests. The effects on the VIX and USDX are still being determined, where in some tests, it appears to be influential while in others not. Originality/value: BTC's diversification potential with equity stocks and USDX makes it a valuable security for portfolio managers. Furthermore, regulatory authorities should consider that BTC is not an isolated phenomenon and can significantly influence other asset classes such as gold. [ABSTRACT FROM AUTHOR]

In this study, the causal relationships between asymmetric information in Borsa Istanbul and volatility index are investigated using daily data from 2003-2022. We first construct the asymmetric information series using the method of Llorente et al. (2002) and then apply the asymmetric causality test developed by Hatemi-J (2012) to investigate the causality between these variables. The approach captures nonlinear effects in the series by dividing them into positive and negative shocks. The results of the symmetric causality test indicate that there is no bidirectional causality between asymmetric information in Borsa İstanbul and volatility index. However, the results of the asymmetric causality test show that positive financial volatility shocks do cause positive asymmetric information shocks. An increase in financial volatility causes an increase in asymmetric information. Overall, this study highlights the asymmetric relationship between volatility index and asymmetric information in Borsa Istanbul, providing valuable insights for investors and policymakers in the Türkiye. [ABSTRACT FROM AUTHOR]

This paper distinguishes whether the shocks of various uncertainties on oil-stock correlation are alike from a time-frequency perspective. Specifically, using data from December 1997 to April 2021, the effects of EPU, VIX, and GPR on the interaction between Brent oil prices and stock indices in the G7 and BRIC countries are examined. The results reveal that the influence of EPU, VIX, and GPR on the oil-stock co-movement is dependent upon investment horizons. In developed markets, the most influential uncertainty index is the VIX, followed by EPU and GPR. In emerging markets, the interactions between oil prices and stock indices in Brazil and India are more susceptible to the VIX, followed by GPR and EPU. For China, the co-movement relation is more sensitive to EPU. Moreover, better prediction power on the stock market is obtained by combining oil prices and EPU or VIX. Our findings provide a crucial reference for financial risk management. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine the effect of global economic uncertainty on the stock markets in four developing countries in Southeast Asia, namely Indonesia (JKSE), Malaysia (KLCI), Thailand (SETI), and Vietnam (VNI). The study uses the U.S., China, and Europe Economic Policy Uncertainty (EPU) indices and the CBOE Volatility Index (VIX) from the Chicago Board Options Exchange as proxies for global uncertainty. By analyzing monthly composite stock index return rates in each stock market and monthly percentage changes in both the EPU and VIX, the Vector Auto-Regressive (VAR) model demonstrates that increases in the US EPU negatively impact JKSE, KLCI, and SETI return rates, while VNI tends to respond positively. Increases in EPU in China and Europe tend to have a negative effect on all stock markets. However, the impact of the Chinese EPU was stronger than that of the European EPU, particularly in JKSE and SETI, and the KLCI was more sensitive to the European EPU shock. On the other hand, the effect of an increase in the VIX was comparable to the impact of the US EPU, with JKSE, KLCI, and SETI experiencing negative pressure, while VNI responded positively. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine the effect of global economic uncertainty on the stock markets in four developing countries in Southeast Asia, namely Indonesia (JKSE), Malaysia (KLCI), Thailand (SETI), and Vietnam (VNI). The study uses the U.S., China, and Europe Economic Policy Uncertainty (EPU) indices and the CBOE Volatility Index (VIX) from the Chicago Board Options Exchange as proxies for global uncertainty. By analyzing monthly composite stock index return rates in each stock market and monthly percentage changes in both the EPU and VIX, the Vector Auto-Regressive (VAR) model demonstrates that increases in the US EPU negatively impact JKSE, KLCI, and SETI return rates, while VNI tends to respond positively. Increases in EPU in China and Europe tend to have a negative effect on all stock markets. However, the impact of the Chinese EPU was stronger than that of the European EPU, particularly in JKSE and SETI, and the KLCI was more sensitive to the European EPU shock. On the other hand, the effect of an increase in the VIX was comparable to the impact of the US EPU, with JKSE, KLCI, and SETI experiencing negative pressure, while VNI responded positively.

Under stress events, most of the asset prices tend to be positively correlated, breaking the diversification benefits. In this study, we explore the performance of different assets particularly during stress events (the 2008 crisis and the COVID-19 crisis) which can come to the rescue of portfolio managers as hedging strategies. Further, the analysis evaluates the performance of different combinations of portfolios with and without including volatility assets. Empirical results indicate that with only an allocation of 5% of the portfolio to volatility asset class, investors with different risk appetites were able to achieve 10% expected returns with reduced uncertainty.

We present a computationally tractable method for simulating arbitrage-free implied volatility surfaces. We illustrate how our method may be combined with a data-driven model based on historical SPX implied volatility data to generate dynamic scenarios for arbitrage-free implied volatility surfaces. Our approach conciliates static arbitrage constraints with a realistic representation of statistical properties of implied volatility co-movements. [ABSTRACT FROM AUTHOR]

In this study, we proposed a new model to improve the accuracy of forecasting the stock market volatility pattern. The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange (Tadawul). The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations. The proposed forecasting model combines the best maximum overlapping discrete wavelet transform (MODWT) function (Bl14) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. The results show the model's ability to analyze stock market data, highlight important events that contain the most volatile data, and improve forecast accuracy. The results were compared from a number of mathematical models, which are the non-linear spectral model, autoregressive integrated moving average (ARIMA) model and EGARCH model. The performance of the forecasting model will be evaluated based on some of error functions such as Mean absolute percentage error (MAPE), Mean absolute scaled error (MASE) and Root means squared error (RMSE). [ABSTRACT FROM AUTHOR]

We use the nonlinear autoregressive distributed lag model to assess the asymmetric relationship between the Chicago Board Options Exchange's volatility index (VIX) and volatility-of-volatility index (VVIX) over the period January 2007 to March 2020. To control for potentially confounding factors, we include measures for economic policy uncertainty and the volatility risk premium. There are three key findings. First, we find that there is an asymmetric long run cointegrating positive relationship running from VVIX to VIX. In this long run relationship, VIX is more responsive to deceases in VVIX than increases in VVIX. Second, we also find an asymmetric short run relationship between VIX and VVIX. However, in contrast to the long run results, for the short run VIX is more responsive to increases in VVIX than decreases in VVIX. Third, consistent with other studies, we find that in the long run VIX rises with greater economic policy uncertainty but falls with increases in the volatility risk premium. We discuss the implications of our findings for practitioners in risk and portfolio management, derivative pricing, and trading. [ABSTRACT FROM AUTHOR]

For reducing investment risks, predicting the volatility of financial markets is crucial. We propose a method for effectively embedding social media posts to facilitate accurate predictions of financial market trends. While discussions on social media inherently consist of paired information – a topic and its sentiment mood – most conventional studies have produced embeddings focusing only on either topic or sentiment information. This approach tends to neglect the intertwined nature of topic and sentiment, thereby overlooking potentially valuable information for market predictions. In this study, we overcome this challenge by introducing a novel document embedding technique that explicitly leverages both topics and sentiments collaboratively for market forecasting. The obtained embeddings are co-trained with financial time series data in a machine learning model, and their efficacy is evaluated through a market prediction task. Benchmarking against various existing document embedding techniques, our model demonstrated superior performance in terms of F-1 Score and Matthews correlation coefficient. The proposed model was further assessed from a practical viewpoint, utilizing investment simulations based on its predictions. These simulations confirmed the model's potential to generate profits even during heightened market volatility, demonstrating its effectiveness as a real-world investment risk mitigation model. Model interpretation using SHapley Additive exPlanations revealed that while some topic-sentiment pairs on social media consistently contribute to market forecasting, others have only a transient impact. The SHapley Additive exPlanations experiment was also compared to the ablation model and showed that the proposed embedding allows for effective prediction by treating topics and sentiment jointly. [Display omitted] • Propose a social media text embedding method for financial market forecasting. • Combines social media text and price to predict volatility index(VI). • Performs well compared with traditional embedding methods. • The proposed embedding achieves an excellent interpretation. • Provides the link between social media topic-sentiment pairs and market trends. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Management & Systems is the property of Business Administration Association of R.O.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper explores how fear sentiment affects the price of Bitcoin by employing the rolling-window Granger causality tests. The analysis reveals negative influences from the volatility index (VIX) to Bitcoin price (BTC), which ascertains that Bitcoin can not be considered a haven in fear sentiment. Due to the liquidity in economic downside risks, BTC may decrease with high VIX to hedge losses, increasing during low VIX periods. The empirical results conflict with the intertemporal capital asset pricing model, which underlines that the increasing VIX can promote the price of Bitcoin. In turn, BTC positively impacts VIX, which shows that Bitcoin price can be treated as the main indicator for a more comprehensive analysis of the fear index. Under severe global uncertainty and changeable fluctuation of market sentiment, investors can optimize investment decisions based on market fear sentiment. The government can also consider VIX to grasp the trend of BTC to participate in cryptocurrency speculation effectively. [ABSTRACT FROM AUTHOR]

This paper focuses on the 2008–2020 period during which two major crises, affecting the economy and the financial markets, occurred. Between 2008 and 2020, there were less extreme tail events, including the lingering Eurozone and Greece crises. In particular, after extremely high stock market volatility and volatility of volatility (VoV) during 2008, the long-run average volatility declined to about 20% and the VoV to around 100%. This paper analyzes this period through the lens of risk and ambiguity (uncertainty). It aims to address the question: what are the financial markets that trade risk — the volatility derivatives markets — telling us? To this end, this paper uses several measures of uncertainty. It reviews the history of volatility and uncertainty measures and discusses their informativeness. It then discusses the information derived from volatility derivatives. [ABSTRACT FROM AUTHOR]

This article aims to uncover the effects of the COVID-19 pandemic on the energy markets in terms of energy stock indexes, energy futures, ETFs, and implied volatility indexes. We model the volatility of energy markets and demonstrate the effects of various phases of the pandemic outbreak (COVID-19) on the energy market. COVID-19-induced uncertainty indicators like the growth of the infection, economic policy uncertainty (EPU), and infectious diseases market volatility (IDsMV) have shown pronounced effects on energy markets' historical volatility. The volatility of energy ETFs–stocks appears to be more resilient in line with S&P 500 energy stocks. WTI crude oil market has shown an unprecedented overreaction amid pandemic outbreaks and traded with an extreme volatility level. The investors' sentiment in the energy market was factually higher on the tail events, indicating that fearful investors rushed toward put options and paid an excess premium to protect from unparalleled risk in the energy market. [ABSTRACT FROM AUTHOR]

Copyright of Istanbul Commerce University Journal of Social Sciences / İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi is the property of Istanbul Commerce University Journal of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The COVID-19 pandemic, which started in China in December 2019, caused the great economic recession for about ten years, threatening health, devastating economic activity, and hurting well-being and jobs. The COVID-19 crisis is one the largest globally public health and economic crisis. The COVID-19 pandemic has affected economic activities across various sectors, including tourism, supply of medical equipment, global supply chains, electronic and financial markets, energy, and food services, while also significantly impacting social activities. This paper investigates the impact of S&P 500 Volatility Index (VIX) on returns of the six sector indexes, informatics index, food index, real estate investment trust index, textile index, tourism index and Bank index from 11 March 2020 to 28 May 2021 in Turkey. Using the VAR analysis method, the results of the empirical study showed that the responses of the other indexes except for Bank index to VIX index are negative at the first. Contrary to these reactions, Bank index return reacts positively to the VIX index. [ABSTRACT FROM AUTHOR]

This study investigated the relationship between the Ibovespa returns and the implied volatility index in Brazil (IVol-BR). We analyzed whether the level of the IVol-BR is related to the present and future Ibovespa returns. OLS and quantile regressions were used to investigate possible differences in the relationships along the distribution of the IVol-BR. To test the robustness of the model, we applied an alternative proxy for volatility, estimated from a GARCH (1,1) model. An asymmetrical relationship was found in Brazilian investors' response to different moments of market volatility, suggesting that negative Ibovespa returns have a stronger relationship with the IVol-BR than positive returns. The coefficients were higher at the extremes of the IVol-BR distribution. These results suggest that the Brazilian market reacts more strongly to bad news than to good news, in line with the ideas developed in the behavioral finance field. [ABSTRACT FROM AUTHOR]

Copyright of Acta Infologica is the property of Acta Infologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The SKEW index of the Chicago Board Options Exchange (CBOE), launched in February 2011, measures the tail risk not fully captured by the VIX index. In this paper we introduce, for the first time, a skewness index for the Italian stock market (ITSKEW) and investigate the pairwise and trilateral relations of this index with volatility and market returns. The results are compared with those of the US market. Data for the period 3 January 2011 to 29 December 2017 are used and three main results are found. First, in both the US and the Italian markets, the skewness index acts as a measure of market greed, as opposed to market fear, in the sense that it captures investor excitement to a larger extent than investor fear. Second, increases in the skewness index are related to returns with high significance in the Granger causality test, while the reverse is not the case. Last, volatility and skewness may give conflicting signals. When skewness and volatility indices move in the same direction, investors should rely on volatility because it has a stronger influence on market returns. The implications for investors and policy-makers are outlined. [ABSTRACT FROM AUTHOR]

Residual coherence is a graphical tool for selecting potential second-order interaction terms as functions of a single time series and its lags. This paper extends the notion of residual coherence to account for interaction terms of multiple time series. Moreover, an alternative criterion, integrated spectrum, is proposed to facilitate this graphical selection. A financial market application shows that new insights can be gained regarding implied market volatility. [ABSTRACT FROM AUTHOR]

This study investigates the impact of trying economic times, i.e., periods of economic decline and uncertainty, on managerial ability. We find that trying economic times positively moderate the association between managerial ability and firm profitability. Consistent with these primary findings, we also find that the positive moderating role of economic decline and uncertainty on the ability-performance relationship is most salient in procyclical industries. Our results are robust across multiple measures of economically trying times and managerial ability. Further analyses indicate that in trying times more capable managers generate stronger performance through both improved asset turnover and profit margins. [ABSTRACT FROM AUTHOR]

• We explore the VIX term structure of US active stocks over the next 1 to 12 months. • The shape of the VIX's term structure is informative regarding future returns. • VIX term structure of the individual stocks slopes upwards. • Variance risk premium of the individual stocks slopes upwards. • Investors seek greater compensation for the uncertainty surrounding the variance in returns in the distant future. We create the VIX's term structure of five US active stocks over the next 1 to 12 months and examine how the shape of the VIX's term structure affects the evolution of the stocks' prices. We find that the level, slope, and curvature of the VIX's term structure are significant predictors of future returns. Decomposing the VIX of individual stocks into physical volatility and variance risk premiums reveals that the variance risk premium of individual stocks is a significant predictor of future returns. Finally, the VIX and variance risk premiums of individual stocks are priced differently for different time horizons. [ABSTRACT FROM AUTHOR]

• Ambiguity is the uncertainty of the probabilities of possible outcomes. • This study documents that ambiguity tends to increase along the calendar year. • December records an exceptionally high level of ambiguity. • A possible explanation is the obsoleteness of disclosures made early in the year. This study documents a seasonality in capital market ambiguity. It shows that ambiguity tends to increase along the calendar year. Moreover, December records an exceptionally high level of ambiguity. In half of the sample years, December exhibits the highest or the second-highest ambiguity among all months. This pattern is not found for the volatility index (VIX). The "December Ambiguity Effect" is robust to numerous tests and persists after controlling for macroeconomic parameters. A possible explanation for this effect is the obsoleteness of information disclosed at the beginning of the year by firms with December fiscal year-end. [ABSTRACT FROM AUTHOR]

İlk defa Chicago Opsiyon Borsası tarafından 1993 yılında oluşturulan volatilite endeksi (VIX) piyasalardaki korkunun derecesini ölçen bir endeks olup, finansal piyasaların gelecekteki belirsizlikleri hakkında bilgi sağlaması nedeniyle dünya genelinde takip edilen önemli göstergelerden biri olmuştur. Temelde örtülü volatilite kavramına dayanan volatilite endeksleri 1970’li yılların ortalarından itibaren finansal piyasalarda kullanılmak amacıyla oluşturulmaya başlanmıştır. İlk endeks sonrasında başta gelişmekte olan piyasalarda sonrasında da gelişmekte olan piyasalarda volatilite endeksleri oluşturulmaya başlanmış olup, bu endeksler hesaplanma metodolojileri göre, oluşturulma biçimlerine yani akademik veya resmi endeks olup olmamalarına göre ve hesaplamada kullanılan opsiyonların dayanak varlıklarına göre sınıflandırılabilmektedirler. Ülkemizde de VIX endeksi uluslararası piyasaları izlemek için kullanılıyor olsa da henüz bir volatilite endeksi oluşturulmuş değildir. Bu çalışmada, gelişmiş piyasalarda uygulama alanı bulan ve risk yönetimi için önemli bir yere konumlandırılmış olan volatilite endeksleri tanıtılmış, dünya genelindeki volatilite endekslerine ilişkin olarak referans olma niteliğinde bilgilere değinilerek Türkiye için modelden bağımsız volatilite endeksi hesaplanırken kullanılabilecek en uygun modelin Chicago Opsiyon Borsası tarafından VIX endeksi hesaplaması sırasında kullanılan metodoloji olduğu düşünülmektedir.

This study investigates the drivers of the Standard & Poor's (S&P) 500 equity returns during the COVID-19 crisis era. The paper considers various determinants of the equity returns from December 31, 2019, to February 19, 2021. It is observed that the United States Dollar (USD) and the volatility indices (VIX) negatively affect the S&P 500 equity returns. However, the newspaper-based infectious disease “equity market volatility tracker” is positively associated with the stock market returns. These results are robust to consider both the ordinary least squares (OLS) and the least angle regression (LARS) estimators.

Volatility indexes provide a tool for investors to speculate and trade on market sentiment regarding future volatility. The risk of trading on volatility indexes can be measured by their second moments, namely, variance and correlation. This study considers the four representative volatility indexes published by the CBOE: stock market volatility index (VIX), crude oil volatility index (OVX), foreign exchange rate volatility index (EVZ), and gold price volatility index (GVZ). To examine their risk, we develop an extended multivariate Markov switching ARCH (MSARCH) model in which regime-switching variances, correlations, and variance-correlation relations are designed. Our empirical sample consists of the four volatility indexes from June 2008 to April 2020 for 612 weekly observations (Wednesday to Wednesday). For the conditional variances, we find evidence of regime-switching processes (switching between low and high volatility regimes) for the individual volatility index returns, with the exception of the GVZ. The estimated probability of the high volatility regime may be used to track economic distress and uncertainty shocks. These results provide evidence for volatility-of-volatility risk. For the conditional correlations, we find a regime-switching relation between variances and correlations. That is, the highest correlation appears when the paired volatility markets are simultaneously experiencing a state of high volatility. By contrast, when the paired volatility markets are encountering different volatility states, the correlation is weaker. These results indicate that the volatility-of-volatility risk is a factor affecting the dynamics of correlations between volatility indexes. [ABSTRACT FROM AUTHOR]

The focus of this paper is the foreign exchange (FX) variance risk premium and the modelling of FX volatility indices of both a developed (United States Dollar) and an emerging market (South African Rand). Regarding the methodology, the variance risk premium is estimated as the difference between realised variance and the variance obtained from the volatility index, and different univariate Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models are considered for the modelling of the volatility index. Empirical results indicate that the variance risk premium is negative on average, this is consistent with previous findings in the literature. Furthermore, asymmetric GARCH models outperform the symmetric GARCH model when modelling FX implied volatility indices. [ABSTRACT FROM AUTHOR]

Volatility, which is one of the leading indicators of the investment atmosphere in the capital markets, affects the developing country markets significantly. Turkey, with its performance in recent years, is nominated as a member of BRICS group formed with emerging economies shaping the global economy. In this study, it is aimed to reveal the effect of volatility index, accepted as international volatility indicator, on BRICS countries and Turkey stock markets. Within this context; the effect of volatility index, exchange rate, consumer confidence index and index of economic freedom on composite stock market index were analyzed by panel data analysis methods. According to the results of the analysis, it has been determined that the volatility index and exchange rate have a negative effect and the economic freedom index has a positive effect on the stock market index. No significant relationship has been found between the consumer confidence index and the stock market index. [ABSTRACT FROM AUTHOR]

This research investigates whether the market sentiment influences firm stock returns with bank loan announcements. We find that CBOE Volatility Index (VIX) is positively related to cumulative abnormal returns, confirming VIX reflects the effect of loan announcements on the financial market. Results show that Financial and Economic Attitudes Revealed by Search (FEARS25) index without predictive power. Instead of that, the daily change of searching VIX (SVIX) of individual investors significantly capture the negative abnormal return before the loan announcements and cease as loan announcements occur. Our findings support that not only professional investors but also individual investors search on Google consider bank loan announcement as good news for borrowers. [ABSTRACT FROM AUTHOR]

Copyright of Istanbul Commerce University Journal of Social Sciences / İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi is the property of Istanbul Commerce University Journal of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bu çalışmada, oynaklık endeksi VIX ile küresel ticaretin öncüsü olarak kabul edilen Baltık Kuru Yük Endeksi Baltic Dry Index - BDI arasındaki ilişki 06.12.2010 - 30.11.2017 dönemi günlük verileri esas alınarak incelenmiştir. Engle - Granger Eşbütünleşme Testi, Hata Düzeltme Modeli ve Kalman Filtresi yaklaşımının kullanıldığı analiz sonucunda iki değişken arasında uzun dönemde bir eşbütünleşme ilişkisinin olduğu, VIX’in BDI’daki değişimi kısa ve uzun dönemde pozitif yönde ve istatistiksel olarak anlamlı biçimde etkilediği belirlenmiştir. Yapılan Kalman Filtresi Analizi de VIX’in BDI üzerindeki etkisini her zaman noktası için tahmin ettiğinden, değişkenler arasındaki ilişkiyi dinamik bir şekilde ortaya koymuştur

We propose a framework for modeling in a consistent manner the VIX index and the VXX, an exchange-traded note written on the VIX. Our study enables to link the properties of VXX to those of the VIX in a tractable way. In particular, we quantify the systematic loss observed empirically for VXX when the VIX futures term-structure is in contango and we derive option prices, implied volatilities and skews of VXX from those of VIX in infinitesimal developments. We also perform a calibration on real data which highlights the flexibility of our model in fitting the futures and the vanilla options market of VIX and VXX. Our framework can be used to model other exchange-traded notes on the VIX as well as any market where exchange-traded notes have been introduced on a reference index, hence providing tools to better anticipate and quantify systematic behavior of an exchange-traded note with respect to the underlying index. [ABSTRACT FROM AUTHOR]

In this paper, the pricing performance of the generalised autoregressive conditional heteroskedasticity (GARCH) option pricing model is tested when applied to Bitcoin (BTCUSD). In addition, implied volatility indices (30, 60- and 90-days) of BTCUSD and the Cyptocurrency Index (CRIX) are generated by making use of the symmetric GARCH option pricing model. The results indicate that the GARCH option pricing model produces accurate European option prices when compared to market prices and that the BTCUSD and CRIX implied volatility indices are similar when compared, this is consistent with expectations because BTCUSD is highly weighted when calculating the CRIX. Furthermore, the term structure of volatility indices indicate that short-term volatility (30 days) is generally lower when compared to longer maturities. Furthermore, short-term volatility tends to increase to higher levels when compared to 60 and 90 day volatility when large jumps occur in the underlying asset. [ABSTRACT FROM AUTHOR]

This article deals with the subject of volatility of financial markets in relation to the US stock market and its volatility index, i.e. the VIX index. The authors analyzed previous studies on the VIX index and based on them, defined a research gap that relates to the problem of market response to emerging macroeconomic information about the US economy. The vast majority of research on the VIX index relates to its forecasting based on mathematical models not taking into account current market data. The authors attempted to assess the impact of emerging macro data on the variability of the VIX index, thus illustrating the magnitude of the impact of individual variables on the so-called US Stock Exchange fear index. The study analysed 80 macroeconomic variables in the period from January 2009 to June 2019 in order to check which of them cause the greatest market volatility. The study was based on correlation study and econometric modeling. The obtained results allowed to formulate conclusions indicating the most important macroeconomic parameters that affect the perception of the market by investors through the pricing of options valuation on the S&P 500 index. The authors managed to filter the most important variables for predicting the change of VIX level. In the eyes of the authors, the added value of the article is to indicate the relationship between macro variables and market volatility illustrated by the VIX index, which has not been explored in previous studies. The analyzes carried out are part of the research trend on market information efficiency and broaden knowledge in the area of capital investments. [ABSTRACT FROM AUTHOR]

Transformed diffusions (TDs) have become increasingly popular in financial modeling for their model flexibility and tractability. While existing TD models are predominately one-factor models, empirical evidence often prefers models with multiple factors. We propose a novel distribution-driven nonlinear multifactor TD model with latent components. Our model is a transformation of a underlying multivariate Ornstein–Uhlenbeck (MVOU) process, where the transformation function is endogenously specified by a flexible parametric stationary distribution of the observed variable. Computationally efficient exact likelihood inference can be implemented for our model using a modified Kalman filter algorithm and the transformed affine structure also allows us to price derivatives in semi-closed form. We compare the proposed multifactor model with existing TD models for modeling VIX and pricing VIX futures. Our results show that the proposed model outperforms all existing TD models both in the sample and out of the sample consistently across all categories and scenarios of our comparison. [ABSTRACT FROM AUTHOR]

In the recent time, there has been increasing importance of tourism development to the global economic dynamics inspite of the global uncertainties. In this regard, the current study is aimed to find out if the volatility index (VIX) affects the returns of the firms operating in the tourism sector in 11 countries. The relationship between the variables in the study was tested through causality and cointegration tests. As a result, the change in the VIX was found to have causality towards the change in the tourism indices of the countries except for the USA and Sri Lanka. In addition, it was found that there was a long-term relationship between the variables and that the increase in VIX caused a decrease in the return of tourism indices. Hence, the current study offers significant policy direction for the tourism industry operations and the government of the examined destination countries. [ABSTRACT FROM AUTHOR]

This paper proposes a method that uses volatility index of US and six other markets of Pacific Basin, namely Hong Kong, Australia, India, Japan, Korea, and China, to provide value-at-risk (VaR) and expected shortfall (ES) forecasts. Empirical constants that are used to multiply the levels of volatility indexes for estimating VaR and ES of various significance levels for 1–22 days ahead, one by one, for seven market indexes have been statistically determined using daily data spanning from 4.75 to 16 years. It is because it would be inappropriate to simply scale up the one-day volatility by multiplying the square root of time (or the number of days) ahead to determine the risk over a longer horizon of i days. Results show that the shift to ES approach generally increases the regulatory capital requirements from 2.09% of India market to 8.56% of Korea market except for the China market where ES approach yields an unexpected decrease of 3.21% of capital requirement. [ABSTRACT FROM AUTHOR]

This article tests whether a correlation exists between a stochastic synthetic volatility index (SVIX) and the Chicago Board Options Exchange (CBOE) volatility index (VIX) and assesses the success of the indicators’ application by pairing an undeveloped trading strategy to gauge its forecasting accuracy. The SVIX aims to address the scaling limitations of the CBOE VIX. The SVIX allows traders to graph volatility as a 100% scale on securities that do not have an official CBOE VIX ticker symbol. The SVIX shows high correlation with the CBOE VIX. Backtesting indicators with an investment strategy using US stocks proved successful. The winning percentage of trades and net profit are positive only for long strategies and fail in short strategies.

Hofstede's six measures of national culture (Hofstede in Culture's consequence. Sage Publications, New York, 1980; Bond and Hofstede in Organ Dyn 16(4):4–21, 1988; Hofstede et al. in Cultures and organizations: software of the mind, 3rd edn. McGraw-Hill, New York, 2010) are applied to study the implied volatility levels in 15 international major equity markets. The cross-sectional analysis, based on the Generalized Additive Model, confirms that all the six measures play an intuitive role in explaining the volatility levels in the respective equity markets. This study confirms the significant long-term role of national culture. The outcomes contribute to provide the specific intuitive patterns of the six measures in explaining the implied volatility levels in the international equity markets. [ABSTRACT FROM AUTHOR]

This study examines the impacts of domestic and counter-terrorist attacks on India's securities markets after collecting time series data of SENSEX and NVIX during the period 1980-2015 for the empirical work. We employ the conditional volatility framework to estimate stock market volatility around the terrorist events, presenting empirical results that corroborate earlier studies and report a significant impact of terrorist activities on investors' sentiment. Stock market volatility remains volatile for pre- and post-terrorist events. This paper is the first empirical research investigating the impact of terrorist attacks on stock market performance in an emerging economy like India, such as stock returns and investors' fear-gauge-index (NVIX). The implications of the study underpin a forward-looking direction on stock market volatility and investors' behavior towards risky capital investment decisions. [ABSTRACT FROM AUTHOR]