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International audience; This paper proposes a new empirical methodology for computing a cross-market volatility index – coined CMIX – based on the Factor DCC-model, implemented on volatility surprises. This approach solves both problems of treating high-dimensional data and estimating time-varying conditional correlations. We provide an application to a 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, since it provides a simple way to hedge multi-asset portfolios with derivative contracts written on the CMIX.

International audience; The spike trains are the main components of the information processing in the brain. To model spike trains several point processes have been investigated in the literature. And more macroscopic approaches have also been studied, using partial differential equation models. The main aim of the present article is to build a bridge between several point processes models (Poisson, Wold, Hawkes) that have been proved to statistically fit real spike trains data and age-structured partial differential equations as introduced by Pakdaman, Perthame and Salort.

International audience; This paper proposes a new empirical methodology for computing a cross-volatility index, coined CVIX, that characterizes the country risk understood here as the financial market risk measurement. The approach, based on the Factor DCC-model, requires to encapsulate all the sources of risk stemming from the financial markets for any given country. We provide an application to the U.S. economy by constructing an aggregate volatility index composed of implied volatility indexes characterizing the equity market, the FX market, fixed income market and the commodity market. The analysis reveals that 75% of the aggregate risk comes from the commodity market, and that the volatility index average value evolves around 22%. The CVIX provides a better hedging performance than the VVIX used as a benchmark.

The Unitary Events (UE) method is a popular and efficient method used this last decade to detect dependence patterns of joint spike activity among simultaneously recorded neurons. The first introduced method is based on binned coincidence count (Grun, 1996) and can be applied on two or more simultaneously recorded neurons. Among the improvements of the methods, a transposition to the continuous framework has recently been proposed by Muino and Borgelt (2014) and fully investigated by Tuleau-Malot et al. (2014) for two neurons. The goal of the present paper is to extend this study to more than two neurons. The main result is the determination of the limit distribution of the coincidence count. This leads to the construction of an independence test between L≥2 neurons. Finally, we propose a multiple test procedure via a Benjamini and Hochberg approach (Benjamini and Hochberg, 1995). All the theoretical results are illustrated by a simulation study, and compared to the UE method proposed by Grun et al. (2002). Furthermore our method is applied on real data.

International audience; We consider spatially extended systems of interacting nonlinear Hawkes processes modeling large systems of neurons placed in Rd and study the associated mean field limits. As the total number of neurons tends to infinity, we prove that the evolution of a typical neuron, attached to a given spatial position, can be described by a nonlinear limit differential equation driven by a Poisson random measure. The limit process is described by a neural field equation. As a consequence, we provide a rigorous derivation of the neural field equation based on a thorough mean field analysis.

This article explores seven international stock markets (DJIA, Euro STOXX 600, Russell 2000, Nikkei, NASDAQ, FTSE and Global Dow) in the quest for jumps and regime switches. The methodological framework borrows from the Markov-switching approach and the stochastic modelling literature based on Levy processes. The econometric procedure is detailed in a two-step fashion. The data set covers the period from June 2004 to July 2014. The main results uncover changing market dynamics according to economic and/or financial phenomena (e.g., economic crises/growth, news events) with the occurrence of several episodes characterized by a high jump intensity. We advocate the use of such a jump-robust model modulated by a Markov chain to further study the dependence structure of financial time series.

The concentrations of particulate matter with aerodynamic diameters less than 2.5 µm (PM

International audience; We generalize multivariate Hawkes processes mainly by including a dependence with respect to the age of the process, i.e. the delay since the last point. Within this class, we investigate the limit behaviour, when n goes to infinity, of a system of n mean-field interacting age-dependent Hawkes processes. We prove that such a system can be approximated by independent and identically distributed age dependent point processes interacting with their own mean intensity. This result generalizes the study performed by Delattre, Fournier and Hoffmann (2015). In continuity with the work of Chevallier et al. (2015), the second goal of this paper is to give a proper link between these generalized Hawkes processes as microscopic models of individual neurons and the age-structured system of partial differential equations introduced by Pakdaman, Perthame and Salort (2010) as macroscopic model of neurons.; Cet article propose d'étudier l'approximation en champ moyen de Processus de Hawkes généralisés dont l'intensité peut également dépendre de l'age du processus, c'est à dire le délai depuis le dernier point. Ce travail poursuit celui initié par Delattre, Fournier et Hoffmann en 2015.De plus, cette généralisation des processus de Hawkes est adaptée aux applications en neuroscience et est directement reliée, comme il est prouvé dans l'article, au système d'equation aux dérivées partielles introduit par Pakdaman, Perthame et Salort (2010) pour la modélisation de réseau de neurones.

International audience; The propagation of chaos and associated law of large numbers for mean-field interacting age-dependent Hawkes processes (when the number of processes n goes to +∞) being granted by the study performed in (Chevallier, 2015), the aim of the present paper is to prove the resulting functional central limit theorem. It involves the study of a measure-valued process describing the fluctuations (at scale n −1/2) of the empirical measure of the ages around its limit value. This fluctuation process is proved to converge towards a limit process characterized by a limit system of stochastic differential equations driven by a Gaussian noise instead of Poisson (which occurs for the law of large numbers limit).

As speculative flows into commodity futures are expected to link commodity prices more strongly to equity indices, we investigate whether this process also creates increased correlations amongst the commodities themselves. Considering U.S. oil and gas futures, we investigate whether common factors, derived from a large international data set of real and nominal macroeconomic variables by means of the large approximate factor models methodology, are able to explain both returns and whether, beyond these fundamental common factors, the residuals remain correlated. We further investigate a possible explanation for this residual correlation by using some proxies for trading intensity derived from CFTC publicly available data, showing most notably that the proxy for speculation in the oil market increases the oil-gas correlation. We thus identify the central role\ud of financial activities in shaping the link between oil and gas returns.

Globally, natural gas prices vary from one region to another, which is a key factor worth considering in formulating energy-related policies or in entering the energy market for a well-considering portfolio to minimize the market risk. This study aims to detect the beginning and the culmination of possible speculative bubbles and analyzed the causes in natural gas prices of three key regional markets, namely, the European Union (EU), Asia, and the United States (US). The monthly data from January 1996 to June 2017 are analyzed by a new bubble detection method called the generalized sup ADF (GSADF) test. There are four significant findings. Firstly, two episodes of bubbles have occurred in the EU, six in Asia, and five in the US during the study period. Secondly, in 2008, the bubbles have struck all the three markets simultaneously, as the prices fluctuated rapidly and drastically. Thirdly, an apparent heterogeneity emerges in terms of the occurrence and duration of bubbles in the three markets, as mentioned above. The bubbles in the EU tend to last longer. Fourthly, the qualitative analysis of the occurrence of natural gas pricing bubbles summarized the cause of the bubbles in different markets. The global economic events can create a simultaneous influence. Geopolitical factors are attributed to the long-duration bubbles in the EU market. Economic euphoria and oil price fluctuation are the main contributing factors to the bubbles in the Asian market. Price volatility and speculation activities are the main factors leading to the price bubble in the US natural gas market. This paper contributes empirically to detect and depict the bubble periods for three natural gas market prices and investigates the cross-market different features and causes for bubbles. This research implies the financialization should be considered as the promotion of a hub-based pricing mechanism, the segmentation among different natural gas market in geography and pricing mechanism should be eliminated. This paper suggests policymakers and investors defend the possible bubbles by establishing an efficient market, diversification of natural gas supply, and unifying the global pricing mechanism.

This paper develops a methodology for estimating a time-varying conditional version of the CAPM with regime changes in conditional variance dynamics. Our research goal is related to documenting the power of the beta when it is estimated dynamically. The empirical performance is tested across a sample of 81 financial, energy, and other commodity markets for the period August 1999–January 2018. The conditional regime-switching GARCH CAPM, with time-varying betas explaining both bull and bear markets, outperforms the unconditional (static) CAPM. Among stocks, there are significant time variations in betas across our models and regimes. This empirical feature is even more pronounced in the USA, the UK, Germany, France, China, and Malaysia. Among energy and other commodities, we find similar variations in the market price of risk. The direction of the relation with market returns for Crude Oil, Gold, Copper, Tin, Rubber, Aluminum, and Platinum is the same across our nested models. This result also holds for aggregate markets indices. Secondly, we provide a ranking by mean filtered volatility series where Natural Gas stands out at a high level. Average pricing errors are inferior in the case of the conditional model, and for Crude Oil. Lastly, we demonstrate that the regime-switching model delivers better estimates of one-day-ahead Value-at-Risk than its non-switching counterpart. Our results shed light on the supremacy of the market factor alone associated with time variation in risk premia across the energy and financial markets.

Purpose– The purpose of this paper is to contain an empirical application of the concept of “time series momentum” – as developed by Moskowitzet al.(2012) – to commodity markets with daily data during 1995-2012.Design/methodology/approach– The paper applies the new concept of “time series momentum” to the sphere of commodity markets.Findings– The paper extends the results previously obtained by Moskowitzet al.(2012) to a second category labeled “breakout strategy.”Research limitations/implications– Further management strategies can be elaborated for investment management purposes, based on the suggested inclusion of the “time series momentum” in commodities.Practical implications– The empirical evidence gathered in this paper bears practical significance for portfolio managers and commodity tradings advisors relying on trend following strategies.Originality/value– Commodity markets are quickly developing to an alternative asset class for investors. Discovering their properties and characteristics has a broad appeal in finance.

This chapter proposes a Zipf-type analysis in order to understand the trading behavior on the carbon market. Several categories are devised to characterize greedy versus non-greedy speculators. We map the trading horizons of buy-and-hold (bearish) long-term investors versus short-term speculators taking advantage of short-term (bullish) extreme price movements.

This article provides the first empirical application of the dynamic equicorrelation (DECO) model to a cross-market data set composed of equities, bonds, foreign exchange and commodity returns during 1983–2013. The results reveal that the average cross-market equicorrelation is around 47%, although it is found to be time-varying and mean-reverting. Besides, we display the equicorrelation across markets as a natural way of looking at the DECO dynamics, which overcomes the cumbersome estimation difficulties encountered with multivariate GARCH models. Implications are derived in terms of asset management.

From 2008 to 2011, commodity markets experienced growing attention from the banking industry for various reasons: the summer 2008 oil price swing, the price surge in an ounce of gold, or sharp variations in agricultural prices. As a consequence, can we hypothesize the existence of a global connection between commodities and economic cycles? If these recent events suggest that commodity markets are strongly related to the business cycle, this evidence goes nevertheless against the widespread intuition that commodity markets are a strong source of diversification in a standard cash–bond–equity portfolio. Based on a data-set from 1990 to present, this paper investigates this issue by (i) looking at the reaction of commodity markets to economic news, and (ii) using a Markov regime-switching model to analyse economic regimes and commodity markets as an asset class.

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 timely Handbook reviews many key issues in the economics of energy and climate change, raising new questions and offering solutions that might help to minimize the threat of energy-induced climate change.

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.

International audience; This article brings new insights on the role played by (implied) volatility on the WTI crudeoil spot price. An increase in the volatility subsequent to an increase in the oil price (i.e. inverseleverage effect) remains the dominant effect as it might reflect the fear of oil consumers to facerising oil prices. However, this effect is amplified by an increase in the oil price subsequent toan increase in the volatility (i.e. inverse feedback effect) with a two-day delayed effect. Thislead-lag relation between the oil price and its volatility is determinant for any type of tradingstrategy based on futures and options on the OVX implied volatility index, and thus is of interest to traders, risk- and fund-managers.

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.

This chapter develops ensemble empirical mode decomposition and fine-to-coarse reconstruction in order to extract carbon price signals from a multiscale viewpoint. The decomposition shows the carbon price is affected by both long-term (e.g., trend) and short-term (e.g., supply–demand fundamentals) imbalances that require appropriate forecasting strategies.

This final chapter is devoted to an adaptive model of carbon price forecasting that makes use of artificial neural networks. Considering either ensemble empirical mode decomposition, the least squares support vector machine, or the particle swarm optimization variant, the competing models are given an extra dimension by incorporating a learning paradigm.

This chapter deals with structural breaks detection techniques in the time series of carbon futures. The ICSS algorithm is detailed, followed by an event study analysis. Several explanations are advanced as to how and why the breakpoints occurred, related to high carbon era or, on the contrary, to the burst of the 2008 subprimes crisis and the 2011 European debt crisis.

This chapter augments the least squares support vector machine (LSSVM) approach of forecasting carbon prices by adding the uniform design (UD) feature. Compared with the particle swarm optimization (PSO) feature, uniform design displays optimization efficiency advantages in complex (low/high) carbon price environments.

This chapter provides an accessible introduction to this book. First, we detail the importance of pricing and forecasting carbon market. Second, we review the pricing and forecasting carbon market from the perspectives of carbon price drivers, single scale forecasting and multiscale forecasting. Third, we provide the architecture of this book, and summarize the chapters.

This chapter discusses the main driving factors behind carbon prices in detail. It presents key data, then proceeds with the results of cointegration test, Granger causality test, and ridge regression estimation. The chapter provides as well a comparative analysis of the equilibrium carbon price and observed carbon price, before drawing conclusions from the research.

This paper proposes a new methodology to measure the volatility of CO2 assets computed as the difference between model-free implied volatility (from option prices) and model-free realized volatility (from high-frequency intraday data), coined as ‘variance risk-premia’ ( Carr and Wu, 2009 ; Bollerslev et al., 2009 ; Trolle and Schwartz, 2010 ), during 2008–2011. We find that variance risk-premia are equal to a daily sample average of 0.79 for European Union Allowances and 0.18 for Certified Emissions Reductions. In the spirit of the CAPM, we show that the beta can only explain a small portion, and that macro risk factors specific to CO2 markets and energy volatilities can improve this result. Hence, there exists a systematic variance risk factor in CO2 markets that asks for a highly risk premium. Further analysis shows that variance risk-premia are time-varying, and can be used as strong predictors for forecasting CO2 returns.

This article aims at establishing an understanding of the common risk factors in commodity markets, as well as their interactions with equities, currencies and interest rates. Since commodity markets often exhibit cross-sectional dependency, common risk factors exist and can be identified. By using daily data from 1995 to 2012, the econometric methodology resorts to factor modeling combined with a criterion to determine the number of factors presented in Alessi et al.(2010). The operational significance of the results is to evaluate risk-adjusted performance of portfolios allocated to commodities, and to help building cross-asset strategies. Investors can then pinpoint the correlation between any two-position taken within commodity markets, and attempt to profitably exploit the common sources of risk. In turn, it should provide the researcher with an increased understanding of the risks at work in the commodity world.

This article investigates whether anticipated technological progress can be expected to be strong enough to offset carbon dioxide (CO2) emissions resulting from the rapid growth of air transport. Aviation CO2 emissions projections are provided at the worldwide level and for eight geographical zones until 2025. Total air traffic flows are first forecast using a dynamic panel-data econometric model, and then converted into corresponding quantities of air traffic CO2 emissions using specific hypotheses and energy factors. None of our nine scenarios appears compatible with the objective of 450 ppm CO2-eq. (a.k.a. "scenario of type I") recommended by the Intergovernmental Panel on Climate Change (IPCC). None is either compatible with the IPCC scenario of type III, which aims at limiting global warming to 3.2°C.

This article proposes a simple rule to implement dynamic stop-loss strategies in the case of a long-only S&P 500 portfolio, that is a problem market participants such as pension funds have to ponder when investing in US equity. It is based on a Monte Carlo analysis, as advised in Phoa [1999].

This article analyses energy efficiency coefficients and their evolution in the air transport sector. The proposed ’macro-level’ methodology allows obtaining energy efficiency coefficients and their growth rates (corresponding to the evolution of energy gains) from 1983 to 2006 for eight distinct geographical regions and at the world level. During the whole period, energy efficiency improvements have been equal to 2.88% per year at the world level, with strong differences between regions. Moreover, our results indicate that domestic air travels are less energy efficient (i.e. more carbon intensive) than international air travels. This result applies in all regions.

This article proposes a theory of banking of emission permits under conditions of regulatory uncertainty. Based on a two-period partial equilibrium framework, we examine the effects of increasing risk–in the sense of a mean preserving spread–regarding a future permit allocation at the firm level. We also examine the role of an agency to pool risks by re-allocating permits for a group of firms. Our results are twofold. First, an increase in risk may lead to changes in a firm’s banking strategy, depending on the third partial derivative of its production function with respect to pollution. Second, we define an optimal risk-sharing rule between agents to respond to political decision changes. Our results overall suggest that the bankability of permits may be used as a risk-management tool.

This paper studies the nonlinear adjustment between industrial production and carbon prices – coined as ‘the carbon-macroeconomy relationship’ – in the EU 27. We model carbon price returns and industrial production as nonlinear and state-dependent, with dynamics depending on the sign and magnitude of past realization of returns and the growth of industrial production. Our findings show that (i) macroeconomic activity is likely to affect carbon prices with a lag, due to the specific institutional constraints of this environmental market; (ii) the joint dynamics of industrial production and carbon prices seem adequately captured by two-regime threshold vector error-correction and two-regime Markov-switching VAR models compared to linear models as main competitors. The regime-switching models proposed are profoundly checked for their economic content and statistical congruency, and are found to provide a sound statistical framework for a comprehensive analysis of the carbon-macroeconomy relationship.

Cet article etudie les coefficients energetiques et leur evolution dans le secteur du transport aerien. Il propose une methodologie alternative permettant d’estimer ces coefficients et leurs taux de croissance (une mesure de l’evolution des gains energetiques) a un niveau macro. Cette methodologie est ensuite appliquee sur huit zones geographiques distinctes ainsi qu’au niveau mondial entre 1983 et 2006. Si sur l’ensemble de la periode l’efficacite energetique de la flotte aeronautique mondiale s’est amelioree de 2,88 % par an en moyenne, on note cependant de fortes disparites au niveau des flottes regionales. De plus, quelle que soit la region consideree, les vols interieurs sont systematiquement moins energie-efficaces – ou dit autrement, plus carbone-intensifs – que les vols internationaux.Classification JEL : L93 ; Q54 ; Q55.

This article deals with the issue of banking and borrowing in the context of the European Union Emissions Trading Scheme (EU ETS) by attempting to provide a policy-oriented unifying theoretical framework. After describing the main features of the EU ETS, the core part of the article consists of variations of an inter-temporal continuous time optimization model. Additional elements include flexibility measures currently discussed and proposed in other emissions trading systems (USA, Australia), which include the use of safety valves, minimum price auctioning or offsets from domestic or international projects.