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International audience; Exloring the role of different types of investors on stock market is crticial since different types of investors react and behave differently when making investment decision. The role of investor behavior is a very important issue in an immature stock market like Vietnam stock market because the market is characterized by a large number of individual investors and low reporting standard. Institutional and foreign investors however play an influential role due to their large exposure and strong investment expertise. Clearly, examining the role of investor behavior and its impact on the stock market in Vietnam is an important topic in finance. A significant body of empirical research has shown that investor behavior is an essential factor to explain stock price that the classical financial theory cannot explain. This research examines the role of investor behavior in stock market by examining the relationship between investor behavior and stock return using the Vietnamese stock exchange data. We create a sentiment index using the principal components analysis (PCA). Consistent with the sentiment and stock return literature, the research shows a negative contemporaneous relationship between investor sentiment and market return.

In this paper, we implement a bivariate Markov-Regime-Switching (MRS) model to filter time-varying size risk premia relying on five international equity markets. Our statistical frame-work identifies two endogenous size states exhibiting distinct characteristics. The upward (downward) size state is characterized by strongly positive (low or negative) size spreads, and is positively (negatively) correlated with the lagged changes of the Composite Leading Indicator (CLI).These results hold for all the considered markets, time intervals, and are robust to alternative factor sets used to adjust for risk. They directly challenge the statistical fluke hypothesis stating that the size effect may be a chance result., Dans cet article, nous implémentons un modèle Markov-Regime-Switching (MRS) bivarié pour filtrer les primes de risque qui varient dans le temps en nous appuyant sur cinq marchés internationaux. Notre cadre statistique identifie deux états de taille endogènes présentant des caractéristiques distinctes. L'état de taille haute (basse) est caractérisé par des écarts de taille fortement positifs (faibles ou négatifs) et est corrélé positivement (négativement) aux changements retardés de l'indicateur composite avancé (CLI).Ces résultats sont valables pour tous les marchés et intervalles de temps considérés, et sont robustes aux autres ensembles de facteurs utilisés pour ajuster le risque. Ils remettent directement en cause l'hypothèse « hasard » statistique selon laquelle l'effet de taille peut être un résultat aléatoire.

We study an extension of the Heston stochastic volatility model that incorporates rough volatility and jump clustering phenomena. In our model, named the rough Hawkes Heston stochastic volatility model, the spot variance is a rough Hawkes-type process proportional to the intensity process of the jump component appearing in the dynamics of the spot variance itself and the log returns. The model belongs to the class of affine Volterra models. In particular, the Fourier-Laplace transform of the log returns and the square of the volatility index can be computed explicitly in terms of solutions of deterministic Riccati-Volterra equations, which can be efficiently approximated using a multi-factor approximation technique. We calibrate a parsimonious specification of our model characterized by a power kernel and an exponential law for the jumps. We show that our parsimonious setup is able to simultaneously capture, with a high precision, the behavior of the implied volatility smile for both S&P 500 and VIX options. In particular, we observe that in our setting the usual shift in the implied volatility of VIX options is explained by a very low value of the power in the kernel. Our findings demonstrate the relevance, under an affine framework, of rough volatility and self-exciting jumps in order to capture the joint evolution of the S&P 500 and VIX.

International audience; In this paper, we examine the influence of the daily institutional investor attention to particular stocks on stock volatility and liquidity. The institutional investor attention is measured from the number of times that users of Bloomberg terminal, who are mostly institutional investors, search for or read articles on a specific stock. Relying on a large international dataset of approximately a million daily observations over the period 2011–2020 from nine countries (Canada, France, Germany, Japan, Russia, South Korea, Switzerland, the UK, and the US), we find that this recent measure of institutional investor attention has a strong positive effect on stock volatility and liquidity. Confirmed by a battery of robustness tests, our findings suggest that this continuous barometer of attention by institutional investors can be used by financial practitioners to predict future stock volatility and liquidity.

International audience; We construct a new indicator to capture media sentiment about the European Central Bank monetary policy and its relevant environment by analyzing 25,000 articles from five major international newspapers. Using named entity recognition and part-of-speech tagging, we propose a methodology to dissociate the dissemination of official communications of the central bank from the media comments. The resulting (daily) index correlates with some (monthly) standard measures of economic sentiment but reveals idiosyncratic information on monetary policy. Analyzing the determinants of our index, we find that both press conference and inter-meeting communications of the President significantly affect media sentiment. We then show that, controlling for a large range of factors, daily changes in media sentiment have predictive power for financial market inflation expectations.

What is the optimal form of firm organization during “bad times”? The greater turbulence following macro shocks may benefit decentralized firms because the value of local information increases (the “localist” view). On the other hand, the need to make tough decisions may favor centralized firms (the “centralist” view). Using two large micro datasets on decentralization in firms in ten OECD countries (WMS) and US establishments (MOPS administrative data), we find that firms that delegated more power from the central headquarters to local plant managers prior to the Great Recession outperformed their centralized counterparts in sectors that were hardest hit by the subsequent crisis (as measured by export growth and product durability). Results based on measures of turbulence based on product churn and stock market volatility provide further support to the localist view. This conclusion is robust to alternative explanations such as managerial fears of bankruptcy and changing coordination costs. Although decentralization will be suboptimal in many environments, it does appear to be beneficial for the average firm during bad times. (JEL D22, G12, G32, G34, L23)

International audience; This paper applied the capital-asset pricing model (CAPM) to determine stock returns of listed firms in the Nigeria Stock Exchange (NSE). For the purpose of investigation, annual data on stock price of twenty six (26) listed firms, Treasury bill a measure of risk-free rate and all share indexes a proxy for market returns were extracted while beta value was computed for the period 2010 to 2016 upon which the model was analyzed. Finding indicates that the CAPM generated a very high return among the firms given the influence of the beta coefficient. The study concludes that higher market risk measured by beta, is associated with higher expected returns. It is therefore recommended that managers of firms in other sectors in Nigeria need to constantly use this model to price security return with a view to guiding investors at investing in securities based on risk preference behavior and also to enable them maximize wealth from a basket of portfolio. .

International audience; The goal of this paper is to provide and examine an important extension of the usual portfolio insurance, namely to study the notion of portfolio performance participation. In this framework, the portfolio is based on two risky assets: the first one corresponds to a reserve asset, while the second one is considered as an active asset which has usually both a higher mean and a higher variance. We aim at insuring a given percentage of the reserve asset return, whatever the market fluctuations. The two main performance participation methods are the Option-Based Performance Participation (OBPP) and the Constant Proportion Performance Participation (CPPP). We compare these two portfolio strategies by means of various criteria such as their payoffs at maturity, their four first moments and their cumulative distributions functions. We also compare their dynamic hedging properties by computing in particular their deltas and vegas.; L’objectif de cet article est de proposer et d’examiner une extension importante des modèles standards d’assurance de portefeuille en étudiant la notion de participation à la performance d’un indice de référence. Dans ce contexte, la stratégie du portefeuille est basée sur deux actifs risqués le premier correspond à un actif de réserve, tandis que le second est considéré comme un actif plus risqué qui a généralement une moyenne et une variance plus élevées. Notre objectif est d’assurer un pourcentage donné du rendement de l’actif de réserve, quelles que soient les fluctuations du marché. Les deux méthodes principales de participation à la performance sont la participation à la performance basée sur les options (OBPP) et la participation à la performance à proportion constante (CPPP). Nous comparons ces deux stratégies de portefeuille à l’aide de divers critères tels que leurs fonctions de paiement à l’échéance, leurs quatre premiers moments et leurs fonctions de répartition. Nous comparons également leurs propriétés de couverture dynamique en calculant notamment leurs Deltas et leurs Vegas.

Using a large database of US institutional investors’ trades in the equity market, this paper explores the effect of simultaneous executions on trading cost. We design a Bayesian network modelling the inter-dependencies between investors’ transaction costs, stock characteristics (bid-ask spread, turnover and volatility), meta-order attributes (side and size of the trade) and market pressure during execution, measured by the net order flow imbalance of investors meta-orders. Unlike standard machine learning algorithms, Bayesian networks are able to account for explicit inter-dependencies between variables. They also prove to be robust to missing values, as they are able to restore their most probable value given the state of the world. Order flow imbalance being only partially observable (on a subset of trades or with a delay), we show how to design a Bayesian network to infer its distribution and how to use this information to estimate transaction costs. Our model provides better predictions than standard (OLS) models. The forecasting error is smaller and decreases with the investors' order size, as large orders are more informative on the aggregate order flow imbalance (R2 increases out-of-sample from -0.17% to 2.39% for the smallest to the largest decile of order size). Finally, we show that the accuracy of transaction costs forecasts depends heavily on stock volatility, with a coefficient of 0.78.

International audience; We model a continuous-time economy with a continuum of investors who differ both in belief and time preference rate and analyze the impact of these heterogeneities on the behavior of financial markets. In particular, we allow the two types of heterogeneity to be correlated: a negative correlation means that the most optimistic agents are also the most patient ones. We fully characterize the risk-free rate which is procyclical and the market price of risk which is countercyclical. When the two types of heterogeneity are negatively correlated, the former is higher and the latter lower compared to the standard case. A negative correlation also leads to a higher market volatility. Moreover, we find that the trading volume increases with the variance of the belief heterogeneity distribution. Finally, the surviving agent of this economy is not necessarily the one who maximizes her utility over her lifetime: a shorter life might be more rewarding than a longer one.

International audience; While most of the debates about cryptocurrencies are centered on the global Bitcoin market, in thisarticle, we focus on local Bitcoin trading volume in 21 emerging countries. In particular, we attempt todetermine the drivers of Bitcoin trading volume in these countries over the period August 1st, 2015 – June2nd, 2018. Based on VECM and ARDL models, we find evidence of significant relationship between thelocal Bitcoin trading volume in each country and the associated banking system access, especially in theshort-run. Moreover, altcoins (Ethereum, Ripple) prices are shown to affect positively and significantlythe local Bitcoin trading volume for most countries in the long-run (VECM results) and the short-run (ARDL results).

International audience; In a stock market experiment, we examine how regret avoidance influences the decision to sell an asset while its price changes over time. Participants know beforehand whether they will observe the future prices after they sell the asset or not. Without future prices, participants are affected only by regret about previously observed high prices (past regret), but when future prices are available, they also avoid regret about expected after-sale high prices (future regret). Moreover, as the relative sizes of past and future regret change, participants dynamically switch between them. This demonstrates how multiple reference points dynamically influence sales. (JEL C91, G12, G41)

International audience; We implement a new framework to mitigate the errors-in-variables (EIV) problem in the estimation of asset pricing models. Considering an international data of portfolio stock returns from 1990 to 2021 widely used in empirical studies, we highlight the importance of the estimation method in time-series regressions. We compare the traditional Ordinary-Least Squares (OLS) method to an alternative estimator based on a Compact Genetic Algorithm (CGA) in the case of the CAPM, three-, and five-factor models. Based on intercepts, betas, adjusted R2 , and the Gibbons, Ross and Shanken (1989) test, we find that the CGA-based method outperforms overall the OLS for the three asset pricing models. In particular, we obtain less statistically significant intercepts, smoother R2 across different portfolios and lower GRS test statistics. Specifically, in line with Roll's critique (1977) on the unobservability of the market portfolio, we reduce the attenuation bias in market risk premium estimates. Moreover, our results are robust to alternative methods such as Instrumental Variables estimated with Generalized-Method of Moments (GMM). Our findings have several empirical and managerial implications related to the estimation of asset pricing models as well as their interpretation as a popular tool in terms of corporate financial decision-making.

International audience; We show that both real indeterminacy and asset price bubble may appear in an infinite-horizon exchange economy with infinitely lived agents and an imperfect financial market. We explain how the asset structure and heterogeneity (in terms of preferences and endowments) affect the existence and the dynamics of asset price bubbles as well as the equilibrium indeterminacy. Moreover, this paper bridges the literature on bubbles in models with infinitely lived agents and that in overlapping generations models.

This paper reports two experiments in which attitudes towards temporal risk resolution is elicited from choices between two-outcome lotteries that pay out at some future fixed date and can be resolved either now or later. We show that matching probabilities provides a simple method to measure attitudes towards temporal resolution-via the utility scaleunder Kreps and Porteus' (1978) recursive expected utility. We also analyze our data using a general recursive model that can reveal attitudes towards temporal risk resolution through the utility scale and/or the probability weighting scale. In terms of goodness of fit, as well as of prediction accuracy, our results point to a better performance of the probability weighting approach. More specifically, we show that individuals become less sensitive and more pessimistic with respect to winning probabilities when lotteries are resolved later rather than now.

International audience; In this study, we investigate monthly seasonality in the foreign exchange market. Given the well-known recurrent higher returns in some month than in others in stock markets around the world, we consider it likely that a seasonal outperformance of a country's stock market over another is associated with similar seasonal patterns in capital flows and exchange rates. A seasonal profit (carry trade) opportunity can be created by the simultaneous appreciation of a country's currency and the outperformance of its stock market. By focusing on the world's key currency pairs, the US dollar-Deutsche mark and the US dollar-euro, and by using a Markov-switching framework, we document persistent January and December effects in the foreign exchange market from 1971 to 2017. Analysis of the German-US stock returns differential and their bilateral capital flows reveal similar month effects in 65% of the whole sample.

International audience; The purpose of the paper is twofold. First, it aims at identifying when UK and European (France, Germany, Italy and Spain) Credit Default Swaps (CDSs) exhibit explosivity with respect to their past behaviors. Second, it seeks to quantify the dynamics of CDS volatility spillover effects surrounding the UK’s EU membership referendum commonly known as "Brexit". Using a recursive identification algorithm and new spillover measures suggested by Diebold and Yilmaz (2012), two interesting findings were drawn. We detect significant build-ups in CDS prices for all countries under study soon after the day relative to the announcement of Brexit. In addition, we show that the great uncertainty over Brexit generates significant risk spillovers across the underlined CDS. In particular, we find that UK, Italy and Spain are the "net volatility transmitters", while France and Germany seem the "net volatility receivers". Our findings may help in formulating appropriate regulatory policies and designing effective hedging strategies.

The Fama-French (FF) five-factor model is cast into a dynamic setting to capture the impact of illiquidity over the phases of the business cycle on the returns of the passive FF twelve sector portfolios. We use two dynamic approaches, Kalman filtering and a recursive/rolling robust instrumental variables (IV) algorithm cast into a GMM framework, to determine time-varying alpha and beta estimates. Our principal result is that the Kalman filter approach supports the hypothesis that illiquidity is an important risk factor in a dynamic context. However, the only factor found to matter in the dynamic GMM approach is the market risk premium. Nevertheless, illiquidity may be prescient with respect to financial crises. Keywords: Illiquidity, Fama-French five-factor model, Kalman filter, Robust IV algorithm, JEL Classification: C58, G12

In this thesis, we studied the problem of the first passage time in options pricing which are financial products allowing the transfer of risks related to the stochastic dynamics of financial markets. In this framework, we developed a new derivative called Geometric Istanbul Option. This option is an extension of Asian options since its price depends onthe average price of the underlying asset over a random period of time. Moreover, as in the case of barrier options, the payoff of a geometric Istanbul option depends on the first passage time of a geometric Brownian motion since we assume that the economic model chosen for the study is the Black-Scholes (1973) model. An approximation formula is given in closed-form for a geometric Istanbul option in both cases of call and put options. In our numerical approaches, we adopt the Monte-Carlo technique with reduction of the variance of the estimator obtained by the control variates method. This numerical approach has allowed us to validate the effciency of our analytical approximation formulas since the option price obtained numerically is very close to the one we propose for various input parameters such as interest rate, volatility, initial price, strike price and maturity date. Furthermore, we have shown, numerically, that our option price is relatively cheaper than that of an arithmetic Istanbul option that was rst proposed by Michel Jacques in 1979.; Dans cette thèse, nous avons étudié le problème de l’instant de premier passage dans la tarification des options qui sont des produits financiers permettant le transfert des risques liés à la dynamique stochastique des marchés financiers. Dans ce contexte, nous avons développé un nouveau produit dérivé appelé option géométrique d'Istanbul. Cette option est une extension des options asiatiques car son prix dépend du prix moyen de l'actif sous-jacent sur une période de temps aléatoire. De plus, comme dans le cas des options à barrière, le gain d'une option géométrique d'Istanbul dépend de l’instant de premier passage d'un mouvement brownien générique puisque nous supposons que le modèle économique choisi pour l'étude est celui de Black-Scholes(1973). Une formule d'approximation est donnée sous forme fermée pour une option géométrique d'Istanbul dans le cas d'une option d'achat (Call) et également pour une option de vente (Put). Dans nos approches numériques, nous adoptons la technique de Monte-Carlo avec réduction de la variance de l'estimateur obtenu par la méthode des variables de contrôle. Cette approche numérique nous a permis de valider l'efficacité de nos formules d'approximation analytiques puisque le prix de l'option obtenu numériquement est très proche de celui que nous proposons pour divers paramètres d'entrée tels que le taux d'intérêt, la volatilité, le prix initial, le prix d'exercice et la date d'échéance. Par ailleurs, nous avons montré, numériquement, que le prix de notre option est relativement moins cher que celui d'une option arithmétique d'Istanbul proposée pour la première fois par Michel Jacques en 1979.

Real and private-value assets—defined here as the sum of real estate, infrastructure, collectibles, and noncorporate business equity—compose an investment class worth an estimated $84 trillion in the U.S. alone. Furthermore, private values can affect pricing in many other financial markets, such as that for sustainable investments. This paper introduces the research on real assets and private values that can be found in this special issue. It also reviews recent advances and highlights new research directions on a number of topics in the real assets space that we believe to be particularly important and exciting.

The recently developed rough Bergomi (rBergomi) model is a rough fractional stochastic volatility (RFSV) model which can generate more realistic term structure of at-the-money volatility skews compared with other RFSV models. However, its non-Markovianity brings mathematical and computational challenges for model calibration and simulation. To overcome these difficulties, we show that the rBergomi model can be approximated by the Bergomi model, which has the Markovian property. Our main theoretical result is to establish and describe the affine structure of the rBergomi model. We demonstrate the efficiency and accuracy of our method by implementing a Markovian approximation algorithm based on a hybrid scheme., Comment: 20 pages, 3 figures

International audience; We use a dataset of approximately one million messages sent on StockTwits to explore the relationship between investor sentiment on social media and intraday Bitcoin returns. We find a statistically significant relationship between investor sentiment and Bitcoin returns for frequencies of up to 15 minutes. For lower frequencies, the relation disappears. We also find that the impact of sentiment on returns is concentrated on the period around the Bitcoin bubble. However, the magnitude of the effect is rather small making it impossible for a trader to make economic profits by trading on the information published on social media.

We study price formulas suited for empirical research in financial markets in which put–call parity is satisfied. We find a connection between risk and the bid–ask spread. We further study the compatibility of the model with market frictions, and determine market subsets where the Fundamental Theorem of Asset Pricing applies. Finally, we characterize the price formula.

Entrepreneurship, growth and total factor productivity are larger when there is a financial bubble. We explain these facts using a growth model with financial bubbles in which individuals face heterogeneous wages and returns on productive investment. The heterogeneity in the return of in- vestment separates individuals between savers and entrepreneurs. Savers buy financial assets, which are deposits or a financial bubble. Entrepreneurs incur in a start-up cost and borrow to invest in productive capital. The bubble provides liquidities to credit-constrained entrepreneurs. These liquidities increase investment and entrepreneurship when the start- up cost is large enough, which explains that growth and entrepreneurship can be larger with bubbles. Finally, productivity can be larger when the bubble further increases the investment of more productive entrepreneurs. This can occur when the return of investment is correlated with wages.

International audience; Beta coefficients are the cornerstone of asset pricing theory in the CAPM and multiple factor models. This chapter proposes a review of different time series models used to estimate static and time-varying betas, and a comparison on real data. The analysis is performed on the USA and developed Europe REIT markets over the period 2009–2019 via a two-factor model. We evaluate the performance of the different techniques in terms of in-sample estimates as well as through an out-of-sample tracking exercise. Results show that dynamic models clearly outperform static models and that both the state space and autoregressive conditional beta models outperform the other methods.

International audience; The green bond market has dramatically expanded especially in Europe but severe liquidity issues may undermine its rapid development. If few studies have assessed the implied liquidity risks for investors in terms of liquidity premium, none of them have specifically analysed its behavior across bond maturities. To fill this gap, this paper studies the term structure of the liquidity premium of the green bond market. We find that the sizes of short-term and long-term premia are close to those estimated on the German government bond market. We show that those premia are affected by economic factors and by spillover effects between them, which contribute to the U-Shape of the liquidity premium. Finally, we detect a liquidity clientele effect on the ask side impacting the liquidity premium, which implies a maturity segmentation i.e., high-risk (resp. low-risk) investors buy short-term (resp. long-term) green bonds and hold them until maturity. Taken together, our results deliver valuable insights on investors' strategies in the green bond market. Quite importantly, green bond investors prefer to opt for buy and hold strategies because they are compensated for higher liquidity risks along the entire maturity spectrum.

International audience; This paper studies the term structure of the liquidity premium of the European green bond market along three liquidity (short-term, medium-term, and long-term) premia.If the liquidity premium is an increasing function of bond’s maturity, the estimated sizes of liquidity premia are comparable to those found on the German government bond markets. We then show that liquidity premia are moderately sensitive to economic determinants. Moreover, we detect a liquidity clientele effect on the ask side, which contributes to increase all liquidity premia. This clientele effect is, however, mitigated by spillover effects operating from the short-end to the long-end of the term structure and vice versa. In sum, our results deliver valuable insights on investors’ strategies in the European green bond market, notably those of long-term investors who are not only compensated for their higher illiquidity risks but also provide market stability with their buy and hold strategies.

This article challenges factor models widely used to explain stock returns. For European firms involved in corporate social responsibility (CSR) actions, we find a risk premium associated with extra-financial ratings priced by the market (that is, environmental, social, and governance [ESG] ratings). This premium is calculated as the excess return of low-rated firms compared to high-rated firms. To describe rated firms' returns, we propose a parsimonious two-factor model that includes both the market factor and this premium. Unlike the CAPM, three-, or five-factor models, our model is validated by the Gibbons, Ross and Shanken (1989) test. Our results lead to many managerial implications related to portfolio management, asset pricing, and corporate financial and investing decisions.

International audience; We use a large dataset of one million messages sent on the microblogging platform StockTwits to evaluate the performance of a wide range of preprocessing methods and machine learning algorithms for sentiment analysis in finance. We find that adding bigrams and emojis significantly improve sentiment classification performance. However, more complex and time-consuming machine learning methods, such as random forests or neural networks, do not improve the accuracy of the classification. We also provide empirical evidence that the preprocessing method and the size of the dataset have a strong impact on the correlation between investor sentiment and stock returns. While investor sentiment and stock returns are highly correlated, we do not find that investor sentiment derived from messages sent on social media helps in predicting large capitalization stocks return at a daily frequency.

International audience; Using an affine model to compute the price of equities based on a dataset of macroeconomic factors, we propose a measure of equity bubbles. We use a dynamic affine term structure framework to price equity and bonds jointly, and investigate how prices are related to a set of macrofactors extracted from a large dataset of economic time series. We analyze the discrepancies between market and model implied equity prices and use them as a measure for bubbles. A bubble is diagnosed over a given period whenever the discrepancies are not stationary and impact the underlying economy consistently with the literature’s findings, increasing over the shorter term economic activity before leading to a net loss in it. We perform the analysis over 3 major US and 3 major European equity indices over the 1990–2017 period and find bubbles only for two of the US equity indices, the S&P500 and the Dow Jones. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

International audience; When firms want to buy back their own shares, they have a choice between several alternatives. If they often carry out open market repurchase, they also increasingly rely on banks through complex buyback contracts involving option components, e.g. accelerated share repurchase contracts, VWAP-minus profit-sharing contracts, etc. The entanglement between the execution problem and the option hedging problem makes the management of these contracts a difficult task that should not boil down to simple Greek-based risk hedging, contrary to what happens with classical books of options. In this paper, we propose a machine learning method to optimally manage several types of buyback contract. In particular, we recover strategies similar to those obtained in the literature with partial differential equation and recombinant tree methods and show that our new method, which does not suffer from the curse of dimensionality, enables to address types of contract that could not be addressed with grid or tree methods.

We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique - neural networks - to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers' pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts' systematic biases in expectations formation - and identify ex ante situations in which such biases are likely to arise.

Is the effect of US monetary policy on stock price bubbles asymmetric? We use a range of measures of excessive stock price variations that are unrelated to business cycle fluctuations. We find that the effects of monetary policy are asymmetric so responses to restrictive and expansionary shocks must be differentiated. The effects of restrictive monetary policy are more powerful than the effects of expansionary policies. We also find evidence that the asymmetric effect of monetary policy is state-contingent and depends on monetary, credit and business cycles as well as stock price boom-bust dynamics.

International audience; This paper assesses the impact of low-carbon policy on stock returns by means of an environmental extension of Fama and French's (2015) five factor model. This paper makes four major contributions. Firstly, for the first time a factor, GMC (green minus carbon), meant to provide the premium which results from not paying a carbon price is constructed. The GMC factor is obtained by means of a sample of 182 firms from 19 European countries operating in 35 sectors: from January 2008 to December 2018 the value-weight returns of 91 firms regulated by the 2003/87/CE directive are subtracted from the value-weight returns of 91 firms exempted by the 2003/87/CE directive upon which the EU-ETS is based. Secondly, we provide evidence that the addition of the GMC factor improves the performance of the 5 factor model in Europe in the 2008-2018 time span. Thirdly, results show that there is a high green premium rather than a carbon premium as it was asserted by parts of the literature, and that this green premium is highly statistically significant. Fourthly, after performing a carbon stress test, we show the effects of EU-ETS average price shocks on both carbon and green firms for each market cap tranche.

Using an international sample of corporate Green bond issuances over the recent period, this paper highlights the potential consequences of the issuance of a Green bond on the issuer&rsquo, s financial performance. Starting with a first sample of 2079 Green bond issuances of 190 unique issuers from 2009 to 2018, we investigate only corporate green bond issuances. Our final sample contains 475 green bonds issued by 145 unique firms. We find that the market reacts negatively to the announcement of green bond issuances. In particular, results show that the stock market reacts on the day of the green bond announcement date and the day after, and that the cumulative abnormal return is between &minus, 0.5% and &minus, 0.2%, depending on the asset pricing model (CAPM, the 3-factor Fama and French models, and the 4-factor Carhart models). This effect is mainly noticeable at the first Green Bond issuance and in developed markets. Our results provide evidence that the investors react in the same manner for Green bonds as for conventional or convertible bonds. This evidence suggests that green debt offerings convey unfavorable information about the issuing firms.

National audience; We propose a new class of performance measures for Hedge Fund (HF) returns based on a family of empirically identifiable stochastic discount factors (SDFs). The SDF-based measures incorporate no-arbitrage pricing restrictions and naturally embed information about higher-order mixed moments between HF and benchmark factors returns. We provide a full asymptotic theory for our SDF estimators to test for the statistical significance of each fund's performance and for the relevance of individual benchmark factors within each proposed measure. We apply our methodology to a panel of 4815 individual hedge funds. Our empirical analysis reveals that fewer funds have a statistically significant positive alpha compared to the Jensen's alpha obtained by the traditional linear regression approach. Moreover, the funds' rankings vary considerably between the two approaches. Performance also varies between the members of our family because of a different fund exposure to higher-order moments of the benchmark factors, highlighting the potential heterogeneity across investors in evaluating performance. (C) 2019 Elsevier B.V. All rights reserved.

Climate change is one of the greatest economic challenges of our time. Given the scale of the problem, the question of whether a carbon tax should be introduced is hotly-debated in policy circles. This paper studies the design of a carbon tax when environmental factors, such as air carbon-dioxide emissions (CO2), directly affect agents’ marginal utility of consumption. Our first result is that the optimal tax is determined by the shadow price of CO2 emissions. We then use asset-pricing theory to estimate this implicit price in the data and find that the optimal tax is pro-cyclical. It is therefore optimal to use the carbon tax to “cool down” the economy during booms and stimulate it in recessions. The optimal policy not only generates large welfare gains, it also reduces risk premiums and raises the average risk-free real rate. The effect of the tax on asset prices and welfare critically depends on the emission-abatement technology.

International audience; In recent years, business valuation models have evolved from a set of complex methods to an essential tool for valuing financial assets and real assets. In the case of real assets, the precision of the analysis is based on the existence of perfect, efficient, and complete markets, since through them, it is possible to replicate the risk involved and estimate the behavior of the variables to be studied. However, these conditions are weaknesses in certain real assets, such as startups. These companies, in their early stages of life, have a low level of income, negative cash flow and limited information about future economic and financial performance. This study aims to contribute to the literature, mainly in the financial area related to the valuation of startup companies. It is evident that there are few authors who propose a particular methodology for the valuation of this type of company, since in a significant number of cases, the traditional methods are not applicable due to the insufficient or little financial and market information of companies of this type.; En los últimos años, los modelos de valoración de empresas han pasado de ser un conjunto de métodos complejos a ser una herramienta esencial para valorar activos financieros y activos reales. En el caso de los activos reales, la precisión del análisis se basa en la existencia de mercados perfectos, eficientes y completos, ya que, a través de ellos, es posible replicar el riesgo involucrado y estimar el comportamiento de las variables a estudiar. No obstante, estas condiciones son debilidades en ciertos activos reales, como por ejemplo las startups. Estas empresas, en sus primeras fases de vida, tienen un bajo nivel de ingresos, flujo de efectivo negativos e información limitada sobre el futuro desempeño económico y financiero. El presente estudio pretende realizar un aporte a la literatura, principalmente en el área financiera relacionada con la valoración de empresas startups. Es evidencia que son escasos los autores que proponen una metodología en particular para la valoración de este tipo de empresas, pues en un importante número de casos, los métodos tradicionales no son aplicables debido a la insuficiente o poca información financiera y de mercado de las empresas de este tipo.

In this paper we study the Stochastic Volatility Inspired model (SVI). Until recently, it was not possible to find sufficient conditions that would guarantee the absence of static arbitrage in this SVI model. Recently, we proposed a new numerical method based on Sequential Quadratic Programming (SQP) algorithm to resolve this problem. The main contribution in this paper is that we provide sufficient conditions that guarantee an SVI static arbitrage-free. These conditions ensure that the probability density function will remain positive.Finally, we present several computational synthetic examples with static arbitrage and we show how to fix them.Next, we use real market data coming from 23 indexes to calibrate the SVI model. The calibration method is robust and easy to implement,it guarantees calibration arbitrage free (calendar spread and butterfly arbitrage).

International audience; In recent years, business valuation models have evolved from a set of complex methods to an essential tool for valuing financial assets and real assets. In the case of real assets, the precision of the analysis is based on the existence of perfect, efficient, and complete markets, since through them, it is possible to replicate the risk involved and estimate the behavior of the variables to be studied. However, these conditions are weaknesses in certain real assets, such as startups. These companies, in their early stages of life, have a low level of income, negative cash flow and limited information about future economic and financial performance. This study aims to contribute to the literature, mainly in the financial area related to the valuation of startup companies. It is evident that there are few authors who propose a particular methodology for the valuation of this type of company, since in a significant number of cases, the traditional methods are not applicable due to the insufficient or little financial and market information of companies of this type.; En los últimos años, los modelos de valoración de empresas han pasado de ser un conjunto de métodos complejos a ser una herramienta esencial para valorar activos financieros y activos reales. En el caso de los activos reales, la precisión del análisis se basa en la existencia de mercados perfectos, eficientes y completos, ya que, a través de ellos, es posible replicar el riesgo involucrado y estimar el comportamiento de las variables a estudiar. No obstante, estas condiciones son debilidades en ciertos activos reales, como por ejemplo las startups. Estas empresas, en sus primeras fases de vida, tienen un bajo nivel de ingresos, flujo de efectivo negativos e información limitada sobre el futuro desempeño económico y financiero. El presente estudio pretende realizar un aporte a la literatura, principalmente en el área financiera relacionada con la valoración de empresas startups. Es evidencia que son escasos los autores que proponen una metodología en particular para la valoración de este tipo de empresas, pues en un importante número de casos, los métodos tradicionales no son aplicables debido a la insuficiente o poca información financiera y de mercado de las empresas de este tipo.

EnglishIn this article, we propose to analyze the impact of regret and rejoicing on the allocation of risky assets. Regret and rejoicing are two emotions that are defined by comparing the result of an action retained by an agent with respect to the result that he could have obtained from an alternative action. We show that the choice of the alternative action impacts the allocation of risky assets. When it is defined in relation to the ex post maximum expected result, the agent only receives regret. This may lead, for low values of the marginal cost of risk, to retain more risky assets than an agent who maximized a standard expected utility function. On the contrary, the agent could express a preference for certainty and defines his emotions in relation to what a total investment in risk-free assets would bring to him. In this case, we show that investment in risky assets is weaker than the one realized by an agent who maximized a standard expected utility function. francaisDans cet article, nous proposons d’analyser l’impact du regret et de la rejouissance sur l’allocation d’actifs risques. Le regret et la rejouissance sont deux emotions qui se definissent en comparant le resultat issu d’une action retenue par un agent, par rapport au resultat que ce dernier aurait pu obtenir en ayant opte pour une action alternative. Nous montrons que le choix de l’action alternative impacte l’allocation d’actifs risques. Lorsque cette derniere est definie par rapport au resultat maximal attendu ex post, l’agent ne percoit que du regret. Ceci peut le conduire, pour des valeurs faibles du cout marginal du risque, a retenir plus d’actifs risques que le ferait un agent maximisant une esperance d’utilite standard. Au contraire, lorsque l’agent exprime une preference pour la certitude, et qu’il definit ses emotions par rapport a ce que lui rapporterait un investissement total dans l’actif sans risque, nous montrons que l’investissement en actifs risques est plus faible que l’investissement realise par un agent maximisant une esperance d’utilite standard.

International audience; We investigate the conditional cross effects and volatility spillover between equity markets and commodity markets (oil and gold), Fama and French HML and SMB factors, volatility index (VIX) and bonds using different multivariate GARCH specifications considering the potential asymmetry and persistence behaviours. We analyse the dynamic conditional correlation between the US equity market and a set of commodity prices and risk factors to forecast the transmission of shock to the equity market firstly, and to determine and compare the optimal hedge ratios from the different models based on the hedging effectiveness of each model. Our findings suggest that all models confirm the significant returns and volatility spillovers. More importantly, we find that GO-GARCH is the best-fit model for modelling the joint dynamics of different financial variables. The results of the current study have implications for investors: (i) the equity market displays inverted dynamics with the volatility index suggesting strong evidence of diversification benefit; (ii) of the hedging assets gold appears the best hedge for the US equity market as it has a higher hedge effectiveness than oil and bonds over time; and (iii) despite these important results, a better hedge may be obtained by using well-selected firm sized and profitability-based portfolios.

International audience; This article studies contagion effects between traditional financial markets, represented by five equity indices and the EUR, USD, GBP, and JPY centralized Bitcoin markets. We apply a regime switching skew-normal model of asset returns that distinguishes between linear and non-linear contagion and also structural breaks in the periods. We find significant contagion effects from financial to Bitcoin markets in terms of both correlation and co-skewness of market returns. Our results also indicate that during crisis periods, risk-averse investors tend to move away from risky Bitcoin markets towards safer financial markets.

International audience; We argue that the risk of an asset is measured by the covariance of an asset's return with the return on the aggregate market and human capital. The intertemporal and consumption-based CAPM, along with an extended version of CAPM framework examines the excess return on Fama and French portfolios sorted on size-BE/ME and momentum across the economies. The frequently used priced factors in anomaly literature include, Fama and French factors, momentum, dividend yield, bond market factors, saving, along with aggregate market and human capital component. Using unique panel data sets of emerging and developed economies, the panel regression, IV-GMM with random effects and PCA, finds the aggregate market and human capital are the strongest predictors of asset returns across the economies. Furthermore, the aggregate market and saving are strong predictors of asset return in emerging economies, whereas aggregate market and human capital emerge the best predictors of asset return in developed economies. Interestingly, human capital subsumes the predictive ability of Fama and French factors and becomes redundant along with momentum, dividend yield, and bond market factors.

International audience; Copeland (1976) and Shalen (1993) state that the causal relationship between trading activity variables, such as volume, open interest and volatility, the three most important factors for traders and portfolio managers, extends beyond one day. However, the literature on causality thus far concerns a one-day horizon. In this study, we provide a more powerful causality test by measuring the strength of the causal relationship over a multiple horizon. The robustness of the results is analysed by splitting the sample into two period pre and post 2008 crisis. Our findings may impact the designing of trading strategies.

International audience; The mixture of distribution hypothesis (MDH) model offers an appealing explanation for the positive relation between trading volume and volatility of returns. In this specification, the information flows constitute the only mixing variable responsible for all changes. However, this single static latent mixing variable cannot account for the observed short-run dynamics of volume and volatility. In this paper, we propose a dynamic extension of the MDH that specifies the impact of information arrival on market characteristics in the context of liquidity frictions. We distinguish between short-term and long-term liquidity frictions. Our results highlight the economic value and statistical accuracy of our specification. First, based on some goodness of fit tests, we show that our dynamic two-latent factor model outperforms all competing specifications. Second, the information flows latent variable can be used to propose a new momentum strategy. We show that this signal improves once we allow for a second signal – the liquidity frictions latent variable – as the momentum strategies based on our model present better performance than the strategies based on competing models

International audience; We study the risk and return characteristics of IPOs for to 60 months. After controlling for Asness et al.'s (2014) quality minus junk factor, IPOs outperform the benchmark portfolios. The previously-documented negative abnormal IPO returns may derive from inaccurate benchmarks. JEL Classification: G12; G32

The aim of this paper is to investigate if, and to what extent, events from the three financially troubled EU markets (Greece, Ireland, and Portugal) affected energy prices during the recent EU financial crisis. More specifically, (i) we test for contagion effects from bond markets on energy/commodity prices, (ii) we examine whether the nature of energy price volatility is affected, and (iii) we investigate whether bond volatility from the financially distressed EU markets spills over, subsequently affecting energy/commodity return volatility. Our results indicate the existence of significant contagion effects from the bond markets of the three EU countries that received bailout packages on energy/commodity prices and significant changes in the nature of energy/commodity volatility during the EU financial crisis. We also report the existence of significant volatility spill-over effects.

This paper investigates the determinants of value and growth investing in a large administrative panel of Swedish residents over the 1999-2007 period. We document strong relationships between a household's portfolio tilt and the household's financial and demographic characteristics. Value investors have higher financial and real estate wealth, lower leverage, lower income risk, lower human capital, and are more likely to be female than the average growth investor. Households actively migrate to value stocks over the life-cycle and, at higher frequencies, dynamically offset the passive variations in the value tilt induced by market movements. We verify that these results are not driven by cohort effects, financial sophistication, biases toward popular or professionally close stocks, or unobserved heterogeneity in preferences. We relate these household-level results to some of the leading explanations of the value premium.

Purpose The purpose of this paper is to investigate whether liquidity risk (i.e. the returns’ vulnerability to the unexpected changes in overall market liquidity) is a priced risk factor in China. Moreover, it investigates the potential role of a stock’s information quality in reducing its liquidity risk during the period of post-non-tradable shares reforms in China. Design/methodology/approach The authors collect data of all the A-share issuing firms listed either on the Shanghai Stock Exchange or Shenzhen Stock Exchange during the period 2006–2016. The authors perform two-stage cross-sectional regression testing. First, the authors perform firm-specific time-series regressions of excess returns over Fama–French’s three-factor model and a liquidity factor. Second, to test whether firm-specific liquidity risk is a priced risk factor, the authors apply Fama and MacBeth’s regressions. Findings Firm-level asset pricing tests provide substantial evidence for market pricing of liquidity risk in China. The authors find a significant negative association between information quality and liquidity risk. The authors also find that the reduction in liquidity risk induced by better information quality is substantial enough to reduce required returns. These findings are robust to alternative measures of liquidity risk and information quality. Practical implications The study underscores that a policy initiative to enhance the information environment can significantly reduce the market volatility in China. Originality/value To the best of authors’ knowledge, this is the first study that considers the Shanghai Stock Exchange as well as Shenzhen Stock Exchange to investigate market pricing of liquidity risk in China.