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Cross-sector volatility spillovers can both threaten the financial stability of credit markets and the diversification of a credit bond portfolio. In this article, we measure cross-sector volatility spillovers, casting light on their intensity in the US-denominated investment grade bond universe. We find that volatility spillovers are high in the US credit market and that the insurance, goods and energy sectors have been net contributors to these shocks over the 1996–2017 period. A structural analysis of the spillover history based on a three-regime multivariate VAR Markov Switching model is then proposed. It highlights that with different volatility regimes come different volatility spillover structures: the insurance and goods sectors are volatility spillover sources during crisis periods. However, according to our estimates a large portion of spillovers are non-recurring and therefore difficult to anticipate.

Recent contributions highlight the importance of intraday jumps in forecasting realized volatility at horizons up to one month. We extend the methodology developed in Maheu and McCurdy (2011) to exploit the information content of intraday data in forecasting the density of returns. Considering both intra-week periodicity and signed jumps, we estimate two variants of a bivariate model of returns and volatilities where the jump component is independently modeled. Our empirical results for four futures series (S&P 500, U.S. 10-year Treasury Note, USD/CAD exchange rate and WTI crude oil) highlight the importance of considering the continuous/jump decomposition of volatility for the purpose of density forecasting. Specifically, we show that models considering jumps apart from the continuous component consistently deliver better density forecasts for horizons up to one month and a half and, in two cases out of four, for horizons up to three months.

The authors thank the participants to the following conferences: CFE 2013 in London, FEBS 2014 hosted by the Surrey University, the IAAE 2014 in London, the EEA-ESEM 2016 in Geneva Switzerland and the Paris Financial Management Conference 2016 in Paris.; International audience; This article questions the empirical usefulness of leverage effects to forecast the dynamics of equity returns. In sample, we consistently find a significant but limited contribution of leverage effects over the past 25 years of S&P 500 returns. From an out-of-sample forecasting perspective and using a variety of different models, we find no statistical or economical value in using leverage effects, provided that an asymmetric and fat-tailed conditional distribution is used. This conclusion holds both at the index level and for 70% of the individual stocks constituents of the equity index.

This article investigates momentum strategies in commodity markets. Using a Markov-switching model and formal tests for the number of regimes in the data, we identify momentum trends for a variety of commodities, exchange rates, interest rates and equities. The data cover the period 1995–2012 at a daily frequency. The results shed light on the key differences between commodities and standard assets with regard to the presence of trends, mean-reverting behaviour and number of regimes that would need to be accurately taken into account to build profitable trend-following strategies. The results are also of economic significance for researchers interested in the modelling of commodity time series.

This article investigates volatility spillovers in commodity markets by following the methodology pioneered in Diebold and Yilmaz (2012). By using a broad data set during 1995–2012, we address three key research questions: are there volatility spillovers within commodities? between standard assets and commodities? between commodities and commodity currencies? The main results indicate first that commodities exhibit weaker than other asset classes volatility spillovers. These spillovers have, however, been increasing over the period. Second, agricultural commodities are the commodities exhibiting the lowest spillovers, whereas precious metals and energy are the biggest net contributors. In a diversified portfolio, including commodities – and especially agricultural products – helps decreasing the total spillover index. This stylized fact has, however, been less and less valid over the years. Third, some currencies are more responsive than others to commodity volatility spillovers.

In this article, we provide statistical evidence around jumps affecting commodity returns. Using nearly 20 years of daily data, we use Laurent, Lecourt, and Palm's (2011) methodology to jump extraction, and discuss various aspects of the estimated jump activity. On average across various commodity markets, we find a high number of days for which returns exhibit the presence of jumps, consistently with the intuition that commodities are affected by large price fluctuations. We emphasize that the post-jump average return depends on the commodity sector considered (e.g. agriculture, energy, or metals). We also show evidence of a jump-to-volatility channel for commodities (similar to the effect usually found for equities). Finally, we diagnose around 40 dates during which commodity indices, stocks, bonds and currencies `co-jump’, revealing a tail dependence between standard and alternative assets.

This article assesses the cross-market linkages between commodities, stocks and bonds in a cointegration framework during 1993–2011.

Both domestic economies and financial markets are affected by cycles that are often represented through multi-state models such as Markov Switching (MS) models. This article discusses the performances associated to the government bond, the equity and the credit cases along the business cycle, using both an European and a US dataset over the 1987 to 2010 period. Periods of noninflationary growth have been strongly supportive to the credit universe, whereas inflationary growth has led to a strong performance of the equity asset class. On the contrary, recession periods are characterized by strong performances from government and investment grade bonds. These statements hold both in the US and in the European cases.

This article explores the commodity–industrial production nexus. More precisely, we assess the cointegration relationships between commodity markets and industrial production during 1993-2011, with an overview for several countries: the USA, the EU, Australia, China, Brazil, Canada, Germany. First, we explore the descriptive statistics and unit root tests of the dataset. Second, we develop two kinds of cointegration analyses (e.g. with/without structural break) between commodities on the one hand, and industrial production indices on the other hand. Third, we conclude on the main results achieved by this econometric procedure. The key contribution of our paper is to revisit the link between industrial production and commodity prices, by using an econometric methodology incorporating structural breaks, and by using a very recently updated dataset. By carrying out a systematic comparison between our results and papers previously published in this literature, we gain a wealth of insights.

In this paper, we present and discuss the estimation of the Wishart Affine Stochastic Correlation (WASC) model introduced in Da Fonseca et al. (2006) under the historical measure. We review the main estimation possibilities for this continuous time process and provide elements to show that the utilization of empirical characteristic function-based estimates is advisable as this function is exponential affine in the WASC case. We thus propose to use the estimation strategy presented in Carrasco et al. (2003), using a continuum of moment conditions based on the characteristic function. We investigate the behavior of the estimates through Monte Carlo simulations. Then, we present the estimation results obtained using a dataset of equity indexes: SP500, FTSE, DAX and CAC. On the basis of these results, we show that the WASC captures many of the known stylized facts associated with financial markets, including the negative correlation between stock returns and volatility. It also helps reveal interesting patterns in the studied indexes'covariances and their correlation dynamics.

In this paper we propose new option pricing models based on class of models with jump contain in the Lévy-type based models (NIG-Lévy, Merton-jump (Merton 1976) and Duan based model (Duan 2007)). By combining these different class of models with several volatility dynamics of the GARCH type, we aim at taking into account the dynamics of financial returns in a realistic way. The associated risk neutral dynamics of the time series models is obtained through two different specifications for the pricing kernel : we provide a characterization of the change in the probability measure using the Esscher transform and the Minimal Entropy Martingale Measure. We finally assess empirically the performance of this modelling approach, using a dataset of European options based on the S&P 500 and on the CAC 40 indices. Our results show that models involving jumps and a time varying volatility provide realistic pricing results for options with different kinds of time to maturities and moneyness. Furthermore, our results provide evidence of consistency between historical and risk neutral distributions, making the approach developed here interesting to price option when option markets are illiquid or when such markets simply do not exist., Dans cet article, nous proposons de nouveaux modèles de valorisation des options à partir de modèles avec sauts incluant en particulier des processus de Lévy (NIG-Lévy, Schoutens (2003), modèle de Merton (Merton, 1976) et modèle de Duan (Duan 2007)). En combinant ces différentes classes de modèles avec des dynamiques de volatilité de type GARCH, nous expliquons les rendements financiers d'une manière réaliste. La dynamique de risque neutre est alors obtenue par deux spécifications différentes pour le pricing kernel : la transformée Esscher et la mesure de martingale entropie minimale. Finalement, nous évaluons empiriquement la performance de notre approche à partir d'un ensemble de données d'options européennes de deux types : le S&P 500 et le CAC 40. Nos résultats montrent que les modèles impliquant des sauts donnent des résultats de prix réalistes pour les options quelle que soient les échéances et les moneyness. En outre, nos résultats fournissent une approche intéressante lorsque les marchés d'options sont illiquides ou lorsque ces marchés n'existent tout simplement pas.

What is the optimal number of uncorrelated strategies to include in a portfolio consisting of cross-asset strategies? Various criteria have been proposed for finding the optimal number of factors in a factor model, many of them in the framework of Principal Component Analysis. Using a refined information criterion, the authors estimate the number of factors associated with various asset classes. In the case of U.S. Treasury Bonds, the authors find the usual three factors, related to level, slope, and term spread, while credit spreads are affected by just one factor. Commodity prices are affected by two common factors, and currencies by only one. Bringing all these assets together, the authors find that a total of five factors are at work. The authors identify and interpret these factors, and investigate their stability over time by testing the significance of the correlations among the factors over a rolling window. During recessions, the number of uncorrelated strategies drops for U.S. Treasury Bond rates. For commodities, however, the concentration of correlation is weaker. This provides evidence to support their diversification potential, even during economic downturns.

This article reviews cointegration analyses in energy markets (e.g. WTI, Brent, Gasoil, Heating Oil, Natural Gas) during 1993-2011 using daily data. The main focus lies in the determination of long-term relationships between these specific energy commodities, with the inclusion of structural breaks. The results globally point out the existence of shared trends among energy markets. The crude oil price (with the WTI as its benchmark) seems to be the leader in the price discovery process, since most of the time it triggers the adjustement towards the long term stationary equilibrium between the variables in the cointegration system.