Financial Mathematics, Volatility and Covariance Modelling

Julien Chevallier is Full Professor of Economics at the University Paris 8 (LED), France. He undertakes research and lectures on empirical finance, applied time-series econometrics, and commodity markets. He has published articles in leading refereed journals.Stephane Goutte is a Maître de Conférences-HDR of Financial Mathematics at University Paris 8, France and Senior Lecturer in Mathematics at University of Luxembourg. He is also a researcher at the Chair European Electricity Markets of Paris Dauphine PSL University. David Guerreiro is an Assistant Professor of Economics at the University Paris 8 (LED), France. His fields of research are International Macroeconomics, Monetary Economics and Meta-Analysis and he has published in numerous peer-reviewed journals.Sophie Saglio is an Assistant Professor of Economics at the University Paris 8 (LED), France. Her research focuses on international economics and finance and she has published in various peer-reviewed journals.Bilel Sanhaji is an Assistant Professor of Economics at the University Paris 8 (LED), France. His main research focuses on nonlinear time series econometrics and modelling volatility. He has published theoretical and applied research papers in various peer-reviewed journals.

• Introduction. Part 1: Commodities Finance. 1. Long Memory and Asymmetry in Commodity Returns and Risk: The Role of Term Spread. 2. The Quantile-Heterogeneous Autoregressive Model of Realized Volatility: New Evidence from Commodity Markets. 3. The Importance of Rollover in Commodity Returns using PARCH models. Part 2: Mathematical Stochastical Finance. 4. Variance and Volatility Swaps and Futures Pricing for Stochastic Volatility Models. 5. A nonparametric ACD model. 6. Sovereign debt crisis and economic growth: new evidence for the euro area. 7. On the spot-futures no-arbitrage relations in commodity markets. 8. Compound Hawkes Processes in Limit Order Books. Part 3: Financial Volatility and Covariance Modelling. 9. Models with Multiplicative Decomposition of Conditional Variances and Correlations. 10. Do High-frequency-based Measures Improve Conditional Covariance Forecasts?. 11. Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of the US Banking Sector. 12. Covariance estimation and quasi-likelihood analysis. 13. The Log-GARCH Model via ARMA Representations