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International audience; This paper introduces the class of quasi score-driven (QSD) models. This new class inherits and extends the basic ideas behind the development of score-driven (SD) models and addresses a number of unsolved issues in the score literature. In particular, the new class of models (i) generalizes many existing models, including SD models, (ii) disconnects the updating equation from the log-likelihood implied by the conditional density of the observations, (iii) allows testing of the assumptions behind SD models that link the updating equation of the conditional moment to the conditional density, (iv) allows QML estimation of SD models, (v) and allows explanatory variables to enter the updating equation. We establish the asymptotic properties of the QLE, QMLE and MLE of the proposed QSD model as well as the likelihood ratio and Lagrange multiplier test statistics. The finite sample properties are studied by means of an extensive Monte Carlo study. Finally, we show the empirical relevance of QSD models to estimate the conditional variance of 400 US stocks.

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.

International audience; The contribution of this paper is threefold: first, it introduces a new geopolitical risk index that incorporates recent geopolitical events ignored in Caldara and Iacoviello (2018)’s index. In addition to war threats and acts, terrorist threats and acts and nuclear threats, the new indicator accounts for global trade tensions, the changing fundamentals of U.S.-China relations, the escalated U.S.-Iran conflict, Saudi Arabia’s uncertainty, Venezuela’s crisis, and OPEC news that rise in response to important OPEC meetings and events connected with OPEC production levels. Second, it addresses how the volatility of six oil prices (the Nigerian Bonny Light, Brent, Dubai, OPEC, Tapis, and WTI) behave when the developed geopolitical risk index unexpectedly changes. Third, it examines whether OPEC maintains a buffer of spare capacity that it uses to respond to potential crises that reduce oil supplies. We show that an increase in the geopolitical risk index is significantly associated with unanticipated oil price changes, though with varying sensitivities. Our findings also reveal that OPEC’s use of spare capacity reduces the reaction of oil price to geopolitical risks but moderately, thus suggesting a limited stabilizing influence on the oil market. The limited amount of spare production capacity leaves the oil market on a knife’s edge as it deals with a host of potential supply disruptions stemming from geopolitical issues.

International audience; To cope with the weakened financial system, the innovative ecosystem of cryptocurrencies and the blockchain seem to emerge as alternative solutions. While they offer a decentralized governance, cryptocurrencies also provide exposure to social dimensions. We thus pose the following research question: is there a virtuous circle between extra-financial and financial performance for cryptocurrencies? To answer this question, we analyze the social performance, an extra-financial dimension, through miners’ compensation. Using a sample of cryptocurrencies between 2015 and 2021, we follow two econometric modeling approaches, namely portfolios constituted based on miner compensation and a panel regression model. This dual approach allows an analysis of the relationship at the macro and microeconomic level as well as enhancing the robustness of our inferences. Our results show that the social and financial performance relationship is positive. Thus, enhanced value sharing in the cryptocurrency landscape appears synonymous of greater financial performance.; Pour faire face au système financier fragilisé, l’écosystème innovant des cryptomonnaies et de la blockchain semble être une alternative. En plusd’une gouvernance décentralisée, les cryptomonnaies offrent une exposition aux dimensions sociales. Nous posons ainsi la question suivante :existe-t-il un cercle vertueux entre performance extra-financière et financière pour les cryptomonnaies ? Nous analysons ainsi la performancesociale, une dimension extra-financière, à travers la rémunération des mineurs. En utilisant un échantillon de cryptomonnaies entre 2015et 2021, nous réalisons une analyse de portefeuille constitué en fonction de la rémunération des mineurs et utilisons un modèle en données de panel.Cette double approche permet d’étudier la relation à l’échelle macro et microéconomique, renforçant la robustesse des inférences. Nos résultats montrent que la relation performance sociale et boursière est positive. Ainsi, un partage de la valeur ajoutée accrue pour une cryptomonnaiesemble être synonyme de performance financière plus élevée.

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; Gold is considered as a hedge against inflation, it offers an opportunity for portfolio diversification. This paper examines the recent evolution of seasonal anomalies in the Chinese Gold market. It studies the day of the week effect and the monthly effect through gold prices at the Shanghai Gold Exchange (SGE) over the 2002 to 2016 period. We investigate seasonal patterns in economically favorable times and unfavorable times by using a UCM model and an ARCH model. The reforms in regulations have rendered the Chinese financial market more efficient, in such cases; we expect an alteration in seasonal anomalies in the Chinese Gold market. However, it would seem that seasonality does exist in the Chinese Gold Market. The Monday returns have been positive and the Tuesday returns have been negative for the whole period. We also highlight that January and February generate the best returns. The return in the middle of the year is negative. This paper contributes to the existing finance literature by investigating the anomalies during the recent period. Although in the Chinese stock market, the seasonal anomalies persist, the index may be efficient despite the regularity in price formation, in this case, a study over a more recent period is necessary.

This paper analyses the purchase and redemption behaviour of mutual fund investors and its implications on fund liquidity risk. We collect a novel set of proprietary data which contains a large number of French investors holding funds with various degrees of asset liquidity. We build a Self-Exciting Poisson model capturing fund flows' clustering effects and over-dispersion. The model improves the forecast accuracy of future flows and provides a reliable risk indicator (Flow Value at Risk.) Accordingly, we introduce the notion of liability risk where investor's behaviour increases mutual fund liquidity risk. We further decompose fund flows into investor categories. We find that investors exhibit high heterogeneous behaviour, and a lead-lag relation exists between them. Finally, we control flow dynamics for various economic conditions. We show that although flows evolve with economic conditions, investor's behaviour stays the main significant determinant of flows' randomness. Our findings encourage fund manager to adopt an ALM approach.

The fractional Brownian motion (fBm) extends the standard Brownian motion by introducing some dependence between non-overlapping increments. Consequently, if one considers for example that log-prices follow an fBm, one can exploit the non-Markovian nature of the fBm to forecast future states of the process and make statistical arbitrages. We provide new insights into forecasting an fBm, by proposing theoretical formulas for accuracy metrics relevant to a systematic trader, from the hit ratio to the expected gain and risk of a simple strategy. In addition, we answer some key questions about optimizing trading strategies in the fBm framework: Which lagged increments of the fBm, observed in discrete time, are to be considered? If the predicted increment is close to zero, up to which threshold is it more profitable not to invest? We also propose empirical applications on high-frequency FX rates, as well as on realized volatility series, exploring the rough volatility concept in a forecasting perspective.

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

We highlight the considerable recent research that investigates how cryptocurrencies have been impacted by COVID-19. We highlight common threads of investigation and consider common findings and conclusions. We also provide suggestions for future research. Additionally, we provide an overview of a recent paper in which we report on a study that applies wavelet methods to daily data of COVID-19 world deaths and daily Bitcoin prices

International audience; We examine if women on corporate boards (WOCB) influence a firm's corporate social performance (CSP). To do this, we utilize stakeholder theory. From an empirical standpoint, we use the control function (CF) approach suggested by Wooldridge (2015), which takes into account the issue of endogeneity raised in the literature (namely, omitted variables, reverse causality, and dynamic endogeneity). Using a sample of firms from the S&P 500 between 2004 and 2015, we find that WOCB have a positive and significant effect (at the 5% level) on CSP. We compare our results to more traditional approaches (pooled OLS, the fixed-effects model, and system GMM). We shed light on an issue that is still considered controversial (Byron and Post, 2016).

Preliminary results were presented at IRCUWU 2020 (the International Research Conference of Uva Wellassa University 2020): Davydenko, A., & Charith, K. (2020a, July 29 30). A Visual Framework for Longitudinal and Panel Studies (with Examples in R) [ePoster]. IRCUWU2020. https://doi.org/10.6084/m9.figshare.12749432; International audience; The shareholder wealth consists of dividends and capital gains. The former is considered to be risk averse, whereas the latter is perceived to be risky. The risk return trade-off in these two returns drives the investor preference. The objective of a for-profit organization is to maximize shareholders’ wealth, however, disbursing dividends may not always be in the best interest of shareholders. Theoretically, retained earnings increase share prices as firms have more funds to be invested. The objective of the study is to measure the stimulus of cash dividends on share prices. We conduct empirical analysis based on data relating to companies listed on the Colombo Stock Exchange (CSE) under the manufacturing sector. As we show in our literature review, in order to reduce the risk of obtaining spurious results, this analysis requires the use of advanced modelling techniques allowing to model non-stationarity of time series, as well the presence of control variables and lagged variables. The novelty of our study is in the use of advanced modelling and data visualisation techniques (including the xdPlot dataviz framework recently proposed by the authors), especially in application to CSE data. We conduct a thorough exploratory data analysis (EDA) aiming to spot data anomalies and initiate appropriate data transformations. Given the results of EDA and the nature of the data available, we select the Arellano-Bond estimator as the most adequate method for regression analysis. Market Price per share (MPS) termed as the dependent variable, whereas Dividend per Share (DPS) is viewed as the independent variable. The results validated theoretical literature such as signaling effect and bird in hand theory, but questioned some previous empirical studies. The study validated cash dividends as stimulus to investors given the positive relationship between DPS and MPS.

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 aim of this paper is to analyze the dynamic evolution of six liquidity proxies ontime, and to find their causality with the French CAC 40 stock market index returns, overthe period from January 2007 to December 2018. For that, we use a vector autoregressiveapproach and the impulse response function, to do causality test between the CAC 40 index returns and six differents liquidity proxies. Empirical results suggest a significantshort-term relationship between the returns and the liquidity. As for Granger’s causalitytest, the results reveal that there is unidirectional causality running from equity returnsto liquidity.

This paper extends the work of Pindyck, R. S. 2012. “Uncertain Outcomes and Climate Change Policy.” Journal of Environmental Economics and Management 63 (3): 289–303. by taking into consideration a large class of different utility functions of economic agents. As in Pindyck, R. S. 2012. “Uncertain Outcomes and Climate Change Policy.” Journal of Environmental Economics and Management 63 (3): 289–303, instead of considering a social utility function that is characterized by constant relative risk aversion (C.R.R.A), we use the expo-power utility function of Saha, A. 1993. “Expo-power Utility: A ‘Flexible’ Form for Absolute and Relative Risk Aversion.” American Journal of Agricultural Economics 75 (4): 905–13. In fact, depending on the choice of the expo-power utility function parameters, we cover a diverse range of utility functions. Apart from covering the other utility functions that a C.R.R.A omits, the Expo-power utility function permits us to discern if under the other utility-regimes of economic agents, the willingness to pay remains more affected by uncertain outcomes than certain outcomes when we vary the expectation and standard deviation of the temperature’s probability distribution. Our paper has maintained the small-tailed gamma distributions of temperature and economic impact of Pindyck, R. S. 2012. “Uncertain Outcomes and Climate Change Policy.” Journal of Environmental Economics and Management 63 (3): 289–303. not only because they hinder infinite future welfare losses (for an exponential utility function), but because it is easy to change some moments of the distribution (jointly or holding the others fixed) while studying how uncertainty influences the willingness to pay as explained in Pindyck, R. S. 2012. “Uncertain Outcomes and Climate Change Policy.” Journal of Environmental Economics and Management 63 (3): 289–303.

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.

This paper revisits the international evidence on hedge, safe haven and diversificationproperties of precious metals, namely; gold, silver and platinum for the G-7 stock marketswhereas most of the studies have focused only on gold properties. Conversely to the studies in the literature that use only bivariate copula, we use the multivariate vine copula based GARCH model. We then find that precious metals have valuable hedge and safe haven roles with different degrees. Our findings show that gold is the strongest hedge and safe haven asset, in almost all the G-7 stock markets. For silver and platinum, results show that they may act as weak hedge assets. Also, silver bear the potential of a strong safe haven role only for Germany and Italy stock markets. However, platinum provides weak safe haven role for most developed stock markets. Using the Bivariate VaR risk measure, we suggest that precious metals may offer diversification benefits in the G-7 stock markets.

This thesis examines, empirically and theoretically, the pro-cyclicality of risk measurements made on historical data. Namely, the effect that risk measurements overestimate the future risk in times of crisis, while underestimating it in quiet times. As starting point, we lay down a methodology to empirically evaluate the amount of pro-cyclicality when using a sample quantile (Value-at-Risk) process to measure risk. Applying this procedure to 11 stock indices, we identify two factors explaining the pro-cyclical behavior: The clustering and return-to-the-mean of volatility (as modeled by a GARCH(1,1)) and the very way of estimating risk on historical data (even when no volatility dynamics are present). To confirm these claims theoretically, we proceed in two steps. First, we derive bivariate (functional) central limit theorems for quantile estimators with different measure of dispersion estimators. We establish them for sequences of iid random variables as well as for the class of augmented GARCH(p,q) processes.Then, we use these asymptotics to theoretically prove the pro-cyclicality observed empirically. Extending the setting of the empirical study, we show that no matter the choice of risk measure (estimator), measure of dispersion estimator or underlying model considered, pro-cyclicality will always exist., Cette thèse analyse, d’un point de vue empirique et théorique, la procyclicité des mesures de risque sur les données historiques, i.e. l'effet de surestimation du risque futur en temps de crise, et sa sous-estimation en temps normal.Nous développons une méthodologie pour évaluer empiriquement le degré de procyclicité, en introduisant un processus de quantiles (`Value-at-Risk’) historiques pour mesurer le risque. En appliquant cette procédure à 11 indices boursiers, nous identifions deux facteurs expliquant la procyclicité: le ‘clustering’ et le retour à la moyenne de la volatilité (tel que modélisée par un GARCH(1,1)), mais aussi la façon intrinsèque d'estimer le risque sur des données historiques (même en l'absence de dynamique de la volatilité).Pour confirmer théoriquement ces arguments, nous procédons en deux étapes. Premièrement, nous démontrons des théorèmes bivariés (fonctionnels) de limite centrale pour les estimateurs de quantiles avec différents estimateurs de dispersion. Comme modèles de base, nous considérons les suites de variables aléatoires iid, ainsi que la classe des processus GARCH(p,q) augmentés. Enfin, ces résultats asymptotiques permettent de valider théoriquement la procyclicité observée empiriquement. Généralisant cette étude à d’autres mesures de risque et de dispersion, nous concluons que la procyclicité persistera quel que soit le choix de ces mesures.

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.

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.

We propose a new methodology to implement unconditionally optimal dynamic mean-variance portfolios. We model portfolio allocations using an auto-regressive process in which the shock to the portfolio allocation is the gradient of the investor's realized certainty equivalent with respect to the allocation. Our methodology can accommodate transaction costs, short-selling and leverage constraints, and a large number of assets. In out-of-sample tests using equity portfolios, long-short factors, government bonds, and commodities, we find that its risk-adjusted performance, net of transaction costs, is on average more than double that of other benchmark allocations.

International audience; In a first step, we model the multivariate tail dependence structure and spillover effects across energy commodities such as crude oil, natural gas, ethanol, heating oil, coal and gasoline using canonical vine (C-vine) copula and c-vine conditional Value-at-Risk (CoVaR). In the second step, we formulate portfolio strategies based on different performance measures to analyze the risk reduction and diversification potential of carbon assets for energy commodities. We identify greater exposure to losses arising from investments in heating oil and ethanol markets. We also find evidence of carbon asset providing diversification benefits to energy commodity investments. These findings motivate for regulatory adjustments in the trading and emission permits for the energy markets most strongly diversified by carbon assets.

International audience; This paper explores empirically the relationship among speculative financial futures (carry trade), nominal exchange rates, and interest rates differentials in developing and developed countries, during the periods of quantitative easing, tapering and quantitative tightening based on the changes of policy interest rates in the United States (US). The public data supplied by the US Commodity Futures Trading Commission Large Trader Reporting Data is taken as a proxy for foreign exchange carry trade. With a time-series model for each country, we estimate the Granger causality using Vector Autoregressive (VAR) models as proposed by Toda and Yamamoto (1995). We investigate three developing countries (Brazil, Mexico, and Russia) and seven developed countries (Australia, Canada, Euro area countries, Japan, New Zealand, Switzerland, and the United Kingdom). Our findings give support to a better understanding of the relationship between foreign exchange activity and monetary policy.

International audience; This study characterizes the oil market as a nonlinear-switching phenomenon and examines its dynamics in response to changes in geopolitical risks over low- and high-risk scenarios. We separate the shocks due to geopolitical acts from those due to geopolitical threats to address whether the serious effects of geopolitical risks are mostly due to increased threats of adverse events or to their realization as acts. While we find the acts to generate a positive and strong impact on oil price dynamics, the effect of threats appears to be moderate or non-significant. Imperfect information in the oil price determination, the history of oil supply disruptions emanating from geopolitical events, the continued rise in populism in the world, oil market volatility, multifractility and the time-varying degree of weak-form efficiency have been advanced to explain the unforeseen responses of oil prices to geopolitical threats. To accommodate recent oil-related events, we construct a composite geopolitical risk indicator by accounting for contemporaneous sources of geopolitical risks, namely global trade tensions, US-China relation risks, US-Iran tensions, Saudi Arabia’s uncertainty and Venezuela’s crisis. The combined effects have an outsized impact on oil prices.

This study contributes to the existing literature on the empirical characteristics of virtual currency allowing for a dynamic transition between different economic regimes and considering various crashes and rallies over the business cycle, that is captured by jumps. We combine Markov-switching models with Levy jump-diffusion offer a new model that captures the different sub-period of crises over the business cycle, that is captured by jumps. This method also enables to test the relevance of dynamic measures of regime switching concerning the independent pure-jump process, which are not frequently used in the literature. Bitcoin offers something different than a traditional currency; there is potential value of having a network that helps as a secure repository for the common knowledge of all transactions. Besides, the value of Bitcoin fluctuates so wildly that it may be too risky to serve as a credible store of value.

Corresponding publicationsWorking Paper | Asymptotics of Cholesky GARCH Models and Time-Varying Conditional Betas.〈halshs-01944656〉; International audience; This paper proposes a new model with time-varying slope coefficients. Our model, called CHAR, is a Cholesky-GARCH model, based on the Cholesky decomposition of the conditional variance matrix introduced by Pourahmadi (1999) in the context of longitudinal data. We derive stationarity and invertibility conditions and prove consistency and asymptotic normality of the Full and equation-by-equation QML estimators of this model. We then show that this class of models is useful to estimate conditional betas and compare it to the approach proposed by Engle (2016). Finally, we use real data in a portfolio and risk management exercise. We find that the CHAR model outperforms a model with constant betas as well as the dynamic conditional beta model of Engle (2016).

This paper investigates the role of precious metals (Gold, Silver and Platinum) as hedge and/or safe haven assets against the G-7 stock markets that represents the most developed economies, by characterizing the multivariate dependence structure using vine copulas, mainly the C-vine and the D-vine, to capture more flexibly the dependencies between the three precious metals and each stock market index. Therefore, by the information about the dependence on average and the dependence in extreme market conditions provided by the vine copula, we find that all precious metals under study serve as hedge and safe haven assets in almost all countries with different degrees. On one hand, our hedge analysis results indicate that only gold may serve as a strong hedge on average while silver and platinum may be good diversifier assets. On the other hand, our safe haven analysis results show that both of gold and silver may be strong safe haven assets in almost all the G-7 countries.

International audience; We employ a multi-stage methodology combining complex network analytics and financial risk modelling to unveil the correlation structures amongst the price jump risks of companies forming the KSE-100 index in Pakistan. We identify the most influential companies in terms of jump risk, and identify communities — clusters of companies with similar price movement characteristics or with highly correlated price jumps. We find that equities in Pakistan stock market experience jumps in different time periods that are correlated to varying degrees within and across industries resulting in 19 different communities, four of which are strongly connected. While Oil & Gas, Cement and Banking sectors exhibit a significant representation of firms in communities, the automobile industry, however, seems to play an important role in risk propagation. These results provide an interesting insight to investors and other stakeholders from an emerging market viewpoint identifying the major sectors driving the volatility of KSE-100 index.

Highlights • Spillovers, portfolio allocation and diversification potential of bank returns are examined. • Spillover index, tail-event driven network and nonlinear portfolio optimization are applied to bank returns data. • Spillovers among banks from emerging America are noticeably smaller than those from developed America. • The largest emerging market spillover transmitters and receivers are the banks from Brazil. • Portfolio of banks from emerging America offers greater diversification potential and lower portfolio allocation risk., We examine the network spillovers, portfolio allocation characteristics and diversification potential of bank returns from developed and emerging America. We draw our results by applying a directional spillover index, the tail-event driven network (TENET) and nonlinear portfolio optimization methods on bank returns. We find that the spillovers and connectedness among banks from emerging America are noticeably smaller than those among banks from developed America. The largest emerging market spillover transmitters and receivers are the banks from Brazil, followed by the banks from Chile. The largest developed market spillover transmitter is JP Morgan Chase. The connectedness among banks from developed America is dominated by the banks from the USA, relative to those from Canada. The total connectedness of the emerging market banks is more intensified than that of the banks from developed America due to the effect of the COVID-19 pandemic. The portfolio optimization shows that in developed America, the largest banks from the USA are the largest risk contributors to total portfolio risk, whereas the banks from Canada contribute the least risk. In emerging America, the banks from Brazil contribute the most risk to total portfolio risk while the banks from Peru and one bank from Colombia contribute the least risk. The portfolio of banks from emerging America offers greater diversification potential and lower total portfolio allocation risk.

CNRS 4, FNEGE 3, HCERES B, ABS 3; International audience; This paper focuses on the linkage between equity prices and fundamentals for 27 individual shares belonging to the French stock price index (CAC40). To assess fundamental value, the traditional Dividend Discount Model (DDM) is coupled with the Arbitrage Pricing Theory (APT), which assumes that investors hold efficient portfolios. This yields a simple equity valuation relationship for which the APT determines the long-term risk premium included in the DDM. Accordingly, equity risk premia are determined by common factors reflecting the non diversifiable risk. These factors are not a priori identified by the theory, and therefore must be exhibited through an empirical analysis. Four domestic and three international common factors are found, all being among those identified by empirical analyses of the APT in the literature. While studies related to stock price indices showed that DDM fundamental values are very smooth compared to stock indices, our DDM-APT model reproduces both trends and major fluctuations of share prices. Further, as for studies based on stock indices, a mean-reverting process of equity prices towards fundamentals is highlighted, but the linear Error Correction Model (ECM) that was considered contains shortcomings suggesting a more complex adjustment process.

International audience; This article empirically investigates the impact of renewable production on congestion using a unique database on the Italian Power Market, where zonal pricing is implemented. We estimate two econometric models: a multinomial logit model, to assess whether renewables increase the occurrence of congestion, and a two stage least squares (2SLS) model to evaluate the impact of wind and photovoltaics on congestion costs. Our analysis suggests that larger renewable supply in importing regions decreases the probability of congestion compared to the no congestion case, while the reverse occurs when renewable production is located in an exporting region. The 2SLS estimations reveal that the same mechanisms explain the level of congestion costs. Our results also highlight that the magnitude of the congestion effects, both in terms of probability and costs, is very sensitive to the location of the historical efficient production, mainly hydro power, and to the geographical configuration of the transmission network.

International audience; This study assesses the roles of Bitcoin as a hedge, a safe haven and/or a diversifier against extreme oil price movements, in comparison to the corresponding roles of gold. We use a quantile-on-quantile regression approach to capture the dependence structure between the considered market returns under different Bitcoin market conditions, while considering nuances of oil price movements, compared to gold. Our findings show that both Bitcoin and gold would serve the roles of a hedge, a safe haven and a diversifier for oil price movements. However, this property seems to be sensitive to the Bitcoin's and gold's different (bear, normal or bull) market conditions and to whether the oil price is in a downside, normal or upside regime. By controlling for new and relevant U.S. and global uncertainty indicators, we confirm that both Bitcoin and gold, but not oil, are assets where investors may park their cash during times of political and economic turmoil. The conditional Value-at-Risk (CoVaR) approach to risk management is then conducted, providing robust evidence of the usefulness of each of the Bitcoin and gold in expanded oil portfolios, in terms of diversification opportunities and downside risk reductions.

This paper provides an innovative perspective on the role of gold as a hedge and safe haven. We use a quantile-on-quantile regression approach to capture the dependence structure between gold returns and changes in uncertainty under different gold market conditions, while considering the nuances of uncertainty levels. To capture the core uncertainty effects on gold returns, a dynamic factor model is used. This technique allows summarizing the impact of six different indexes (namely economic, macroeconomic, microeconomic, monetary policy, financial and political uncertainties) within one aggregate measure of uncertainty. In doing so, we show that the gold's role as a hedge and safe haven cannot be assumed to hold at all times. This ability seems to be sensitive to the gold's various market states (bearish, normal or bullish) and to whether the uncertainty is low, middle or high. Interestingly, we find a positive and strong relationship between gold returns and the uncertainty composite indicator when the uncertainty attains its highest level and under normal gold market scenario. This suggests that holding a diversified portfolio composed of gold could help protecting against exposure to uncertain risks., Economic Modelling, Elsevier, In press

Gold plays an important role as a precious metal with portfolio diversification; also it is an underlying asset in which volatility is an important factor for pricing option. The aim of this paper is to examine which autoregressive conditional heteroscedasticity model has the best forecast accuracy applied to Chinese gold prices. It seems that the Student's t distribution characterizes better the heavy-tailed returns than the Gaussian distribution. Assets with higher kurtosis are better predicted by a GARCH model with Student's distribution while assets with lower kurtosis are better forecasted by using an EGARCH model. Moreover, stochastic models such as Stable processes appear as good candidates to take heavy-tailed data into account. The authors attempt to model and forecast the volatility of the gold prices at the Shanghai Gold Exchange (SGE) during 2002–2016, using various models from the ARCH family. The analysis covers from as in-sample and out-of-sample sets respectively. The results have been estimated with MAE, MAPE and RMSE as the measures of performance.

International audience; Dans cet article, nous nous proposons de tester une extension du modèle d’évaluation des actifs financiers (MEDAF), dans lequel nous ajoutons un facteur de risque systémique, mesuré par une agrégation des principales mesures de risques systémiques, comme proposée récemment par différents auteurs, dans le cadre d’une analyse en composantes principales parcimonieuse (ACPP). Il ressort de nos analyses empiriques menées sur le marché américain que le risque systémique est effectivement une composante importante de la rémunération sur certains titres. Nous proposons finalement une application originale servant à l’identification et au classement des institutions financières d’importance systémique (IFIS).

International audience; This paper proposes an indirect inference (Gourieroux, Monfort and Renault, 1993; Smith, 1993) estimation method for a large class of dynamic equilibrium models. Our approach is based on the observation that the econometric structure of these systems naturally generates auxiliary equilibria that can serve as building blocks for estimation. We use this insight to develop an accurate estimator for the long-run risk model of Bansal and Yaron (2004). We demonstrate the accuracy of our method by Monte Carlo simulation and estimate the long-run risk model on U.S. data. We also illustrate the good performance of the methodology on an asset pricing model with investor learning.

International audience; The paper applies an event study methodologyaims to investigate the macroeconomic announcements effects on Standard&Poor’s500 and oil prices. Our results provide evidence for a significant impact of the US macroeconomic news on oil prices. This impact is split into two components, namely the direct effect (common response) and indirect effect (volatility transmission). Altogether our results show that the volatility transmission is bidirectional. Not only a significant volatility transmission from the oil market to the US stock market is revealed, but also a high volatility transmission is recorded from the oil market to the stock market especially after the release of consumption indicators.

This paper presents empirical evidence of the impact of the recent global financial crises on Islamic and conventional banks in three GCC countries. Our assumptions are discussed within the framework of Khan (1976), Khan and Mirakhor (2005) and Chapra (2008). A diagonal BEKK model is used to examine the impact of the global crisis on conditional beta of the selected banks. Results show that Islamic and conventional banks have been largely affected by the global crisis, except for few banks. They reveal also that small banks have been less affected than larger banks. These results are in line with the other studies which have found that Islamic banks are not more resilient than conventional ones.; Cet article présente des preuves empiriques sur l'impact de la récente crise financière sur les banques islamiques et conventionnelles dans trois pays membres du CCG. Nos hypothèses se basent sur les travaux de Khan (1976), Khan et Mirakhor (2005) et Chapra (2008). Un modèle BEKK diagonal est utilisé pour examiner l'impact de la crise mondiale sur le bêta conditionnel des banques sélectionnées. Les résultats montrent que les banques islamiques et conventionnelles ont été largement touchées par la crise mondiale, à l'exception de quelques banques. Ils révèlent également que les petites banques ont été moins touchées que les grandes banques. Ces résultats sont en ligne avec les autres études qui ont constaté que les banques islamiques ne sont pas plus résilientes que celles conventionnelles.

International audience; We develop and implement a method for maximum likelihood estimation of a regime-switching stochastic volatility model. Our model uses a continuous time stochastic process for the stock dynamics with the instantaneous variance driven by a Cox-Ingersoll-Ross (CIR) process and each parameter modulated by a hidden Markov chain. We propose an extension of the EM algorithm through the Baum-Welch implementation to estimate our model and filter the hidden state of the Markov chain while using the VIX index to invert the latent volatility state. Using Monte Carlo simulations, we test the convergence of our algorithm and compare it with an approximate likelihood procedure where the volatility state is replaced by the VIX index. We found that our method is more accurate than the approximate procedure. Then, we apply Fourier methods to derive a semi-analytical expression of S&P 500 and VIX option prices, which we calibrate to market data. We show that the model is sufficiently rich to encapsulate important features of the joint dynamics of the stock and the volatility and to consistently fit option market prices.

International audience; This article aims to study the role of gold quoted on the Shanghai Gold Exchange in the diversification of Chinese portfolios using a mean-risk and stochastic dominance analysis. With the 2004–2014 period, our results show that in general, risk-averse investors prefer not to include gold while risk-seeking investors prefer to include it in their stock–bond portfolios, especially in crisis periods. This result is found to be time-varying but not time-frequency dependent and the inclusion of the risk-free asset does not induce relevant impacts. Furthermore, risk-seekers prefer including gold in an equal-weighted portfolio while risk-averters prefer including gold in efficient portfolios.

This paper shows that large dimensional vector autoregressive (VAR) models of fi nite order can generate long memory in the marginalized univariate series. We derive high-level assumptions under which the fi nal equation representation of a VAR(1) leads to univariate fractional white noises and verify the validity of these assumptions for two speci fic models. We consider the implications of our findings for the variances of asset returns where the so-called golden-rule of realized variances states that they tend always to exhibit fractional integration of a degree close to 0:4.

The paper explains how stock price of oil companies depends on oil futures market price. The model is applied to major oil company stocks: Shell, Exxon Mobil, BP, Total and Chevron. The topic is original because it focuses on short‐ and long‐term relationships with vector error correction models (VECM) with changing regimes. To get structural model between stock price of oil companies and oil futures market price, investigation is oriented to cointegration link with autoregressive vector (VAR). Research is conducted in using monthly data from November 1989 to June 2011. The stationarity of times series is tested with Dickey‐Fuller unit root test, Philips‐Perron and KPSS. The approaches of Engle‐Granger and Johansen do not enable to find long‐term relationship over the overall period. However, cointegration with 5 changing regimes is found. The Bai and Perron approach enables to find 5 breakpoints. In order to identify cointegration relationships with changing regimes, the Gregory and Hansen method is used and results show cointegration with changing regimes. Vector error correction (VEC) models associated to cointegration with changing regimes are estimated. VEC looks at the dynamics on the short‐term.Then economic and financial analysis is done and choc analysis is implemented with impulse response function. Furthermore, ARCH‐LM test shows the existence of an ARCH vectorial model. The paper shows how recent cointegration techniques are useful in including endogenous structural breaks leading to changing regimes. Further investigations could be done to estimate whether one could be able to hedge commodity price fluctuations in using stocks whose markets are a lot more liquid. This modeling will be extended by short‐term construction of models incorporating changing regimes with Markovian approach MS‐VAR and MS‐VECM., La recherche explique comment le cours des actions des compagnies pétrolières dépend du prix à terme du pétrole. La modélisation est appliquée aux principales compagnies pétrolières Shell: Exxon Mobil, BP, Total et Chevron. La recherche est originale car elle porte sur les relations à court et long terme à partir de modèles à correction d'erreur vectorielle (VECM) avec changements de régime. La modélisation structurelle, entre le cours des actions des compagnies pétrolières et le prix à terme du pétrole, est menée à partir de modèles de d’autorégression vectorielle (VAR) en lien avec la cointégration. La recherche est conduite en utilisant des données mensuelles de novembre 1989 à juin 2011. La stationnarité des séries temporelles est testée avec les tests de Dickey‐Fuller, Philips‐Perron et KPSS. Les approches d’Engle‐Granger et Johansen ne permettent pas de trouver une relation de long terme sur toute la période. Cependant, l’approche de Bai et Perron permet d’identifier 5 changements de régime et de modéliser des relations de cointégration différentes sur ces sous‐périodes. Afin d'identifier les relations de cointégration à changements de régime, la méthode Gregory et Hansen est utilisée et les résultats montrent une cointégration avec des changements de régime. Les VECM associés à la cointégration avec changements de régime sont estimés. Le VECM permet de comprendre la dynamique sur le court terme. Puis l'analyse économique et financière est faite. L’analyse de choc est mise en oeuvre avec la fonction de réponse impulsionnelle. De plus, le test ARCH‐LM montre l'existence d'un modèle ARCH vectoriel. La recherche indique comment les dernières techniques de cointégration sont utiles notamment en incluant des ruptures structurelles endogènes menant à des évolutions de régimes. D'autres recherches seront effectuées pour estimer si l'on peut couvrir les risques de fluctuations des prix des matières premières en utilisant les cours boursiers des entreprises cotées dans des marchés beaucoup plus liquides. Cette modélisation sera complétée par la construction de modèles de court terme incorporant des changements de régime avec l’approche markovienne MS‐VAR et MS‐VECM.

International audience; To assess how financial markets and commodities are inter-related, this paper introduces a ‘volatility surprise’ component into the asymmetric DCC with one exogenous variable (ADCCX) framework. We develop an econometric model in which returns and volatility allow to influence pairs of assets, and derive several case studies linking commodities to stocks, bonds and currencies from 1983 to 2013. The innovative feature of our model is that these volatility spillovers are modeled consistently within the correlation dynamics of the ADCCX model. We find evidence that return and volatility spillovers do exist between commodity and financial markets and that in turn, their relative impact on each other is very substantial.

International audience; This article addresses the issue of measuring the level of aggregate financial stress on stock markets, which is a central issue for investors and policy-makers. To this end, Realized EquiCorrelation (REC) is obtained by plugging realized volatility as an input into the Dynamic EquiCorrelation (DECO) model where both the continuous and jump components of realized volatility are considered. An application is provided for the 20 major stock markets over January 2000–May 2014 using intra-day data. The results remarkably pick up financial stress periods.

In this paper, we propose an alternative approach to estimate long-term risk. Instead of using the static square root method, we use a dynamic approach based on volatility forecasting by non-linear models. We explore the possibility of improving the estimations by different models and distributions. By comparing the estimations of two risk measures, value at risk and expected shortfall, with different models and innovations at short, median and long-term horizon, we find out that the best model varies with the forecasting horizon and the generalized Pareto distribution gives the most conservative estimations with all the models at all the horizons. The empirical results show that the square root method underestimates risk at long horizon and our approach is more competitive for risk estimation at long term., Dans cet article, nous proposons une approche alternative pour estimer le risque à long terme. Au lieu d'utiliser la méthode de la racine carrée, nous proposons une approche dynamique. Nous comparons deux mesures de risques pour différents modèles de séries chronologiques. Nous trouvons que le meilleur modèle varie en fonction de l'horizon de prévision et de la distribution de Pareto généralisée utilisée. Les résultats empiriques montrent que la méthode de la racine carrée sous estime les risques.

International audience; This article proposes a new empirical methodology for computing a cross-market volatility index - coined CMIX - based on the Factor-Dynamic Conditional Correlation (DCC) model, implemented on volatility surprises. This approach solves problems in treating high-dimensional data and estimating time-varying conditional correlations. We provide an application to multi-asset market data composed of equities, bonds, foreign exchange rates and commodities during 1983-2013. This new methodology may be attractive to asset managers, because it provides a simple way to hedge multi-asset portfolios with derivatives contracts written on the CMIX.

International audience; Financial sector development is increasingly recognised as critically important to the micro-foundations of wealth creation and economic development of nations. An increasingly relevant component in this relationship relates to the issue of financial access. This paper contributes to the growing debate on the relationship between financial development indicators and output growth by investigating the long run relationship between financial depth, financial access and economic growth in Nigeria. The research question is: how growth propelling is an inclusive financial system in Nigeria? This question is of significant policy relevance, as Nigeria recently launched a financial inclusion programme as a strategy for wealth creation and poverty alleviation for her citizens. By setting up an error correction model, this paper showed that increased financial depth (measured either as ratio of broad money supply to output or as ratio of credit to private sector to output) propelled output growth in Nigeria during 1975-2012. However, population per bank branch conferred significant negative effect on economic growth, implying that financial access matters for growth in Nigeria. Therefore, the study strongly endorses the current financial inclusion programme of the Central Bank of Nigeria as a way of promoting growth in the country. Also, the authors call for the inclusion of financial access questions in the General Household Survey (GHS) questionnaire of the National Bureau of Statistics (NBS) as a way forward. This is based on the author's belief that the first step to improving financial access is measuring it. It is hoped that this effort would trigger further analysis that will help policy makers identify the real constraints to financial access in Nigeria.

We provide a mathematical definition of fragility and antifragility as negative or positive sensitivity to a semi-measure of dispersion and volatility (a variant of negative or positive "vega") and examine the link to nonlinear effects. We integrate model error (and biases) into the fragile or antifragile context. Unlike risk, which is linked to psychological notions such as subjective preferences (hence cannot apply to a coffee cup) we offer a measure that is universal and concerns any object that has a probability distribution (whether such distribution is known or, critically, unknown). We propose a detection of fragility, robustness, and antifragility using a single "fast-and-frugal", model-free, probability free heuristic that also picks up exposure to model error. The heuristic lends itself to immediate implementation, and uncovers hidden risks related to company size, forecasting problems, and bank tail exposures (it explains the forecasting biases). While simple to implement, it improves on stress testing and bypasses the cillib flaws in Value-at-Risk.

International audience; This paper proposes a new dynamic approach to modelling intra-day trading volume based on factor models. It assumes that intra-day volume can be decomposed into two parts each predicted using separate time-series models. By enabling more accurate prediction of intra-day volume, this methodology allows for a significant reduction in the cost of executing Volume weighted Average Price orders.