Keynote Speakers of the JEF'2016 Conference
CREST-ENSAE and Université de Lille 3, France
Christian Francq is member of the laboratory CREST and professor of applied mathematics at Lille 3 University and ENSAE, where he teaches time series analysis and financial econometrics. He is associate editor of Journal of Time Series Analysis and Statistical Inference for Stochastic Processes. His main research interests include financial and time series econometrics, and theoretical econometrics. In these areas, his current research focuses on risk estimation, the estimation of volatility models and conditional ellipticity testing. He is co-author of a book entitled “GARCH models: Structure, Statistical Inference and Financial Applications” and of recent research articles published in Econometrica, Annals of Statistics and JRSS-B.
Title of the Talk: “ESTIMATION RISK FOR THE VaR OF PORTFOLIOS DRIVEN BY SEMI-PARAMETRIC MULTIVARIATE MODELS”
Summary: Joint estimation of market and estimation risks in portfolios is investigated, when the individual returns follow a semi-parametric multivariate dynamic model and the asset composition is time-varying. Under ellipticity of the conditional distribution, asymptotic theory for the estimation of the conditional Value-at-Risk (VaR) is developed. An alternative method – the Filtered Historical Simulation - which does not rely on ellipticity, is also studied. Asymptotic confidence intervals for the conditional VaR, which allow to simultaneously quantify the market and estimation risks, are derived. The particular case of minimum variance portfolios is analyzed in more detail. Potential usefulness, feasibility and drawbacks of the two approaches are illustrated via Monte-Carlo experiments and an empirical study based on stock returns. (Joint work with J-M Zakoïan).
CREST-ENSAE and Université de Lille 3, France
Jean-Michel Zakoian is co-director of the Finance-Insurance laboratory at CREST and professor of applied mathematics at Lille 3 University and ENSAE. He is associate editor at Econometric Theory and Journal of Time Series Analysis. His main research interests include financial and time series econometrics, and theoretical econometrics. In these areas, his current research focuses on risk estimation, the estimation of multivariate GARCH models and the modeling of bubbles by non causal processes. He is co-author of a book entitled “GARCH models: Structure, Statistical Inference and Financial Applications” and of recent research articles published in Econometrica, Annals of Statistics, JASA and JRSS-B.
Title of the Talk: “BUBBLE MODELLING BY NON-CAUSAL PROCESSES” (joint work with C. Gouriéroux)
Summary: The noncausal autoregressive process with heavy-tailed errors possesses a nonlinear causal dynamics, which allows for bubbles (local explosion) or asymmetric cycles often observed in economic and financial time series. It provides a new model for multiple local explosions in a strictly stationary framework. The causal predictive distribution displays surprising features, such as the existence of higher moments than for the marginal distribution, or the presence of a unit root in the Cauchy case. Aggregating such models can yield complex dynamics with local and global explosion as well as variation in the rate of explosion. The asymptotic behavior of a vector of sample autocorrelations is studied in a semi-parametric noncausal AR(1) framework with Pareto-like tails, and diagnostic tests are proposed. Empirical results based on the Nasdaq composite price index are provided.