This book provides an up-to-date series of advanced chapters on applied financial econometric techniques pertaining the various fields of commodities finance, mathematics & stochastics, international macroeconomics and financial econometrics.
Financial Mathematics, Volatility and Covariance Modelling: Volume 2 provides a key repository on the current state of knowledge, the latest debates and recent literature on financial mathematics, volatility and covariance modelling. The first section is devoted to mathematical finance, stochastic modelling and control optimization. Chapters explore the recent financial crisis, the increase of uncertainty and volatility, and propose an alternative approach to deal with these issues. The second section covers financial volatility and covariance modelling and explores proposals for dealing with recent developments in financial econometrics
This book will be useful to students and researchers in applied econometrics; academics and students seeking convenient access to an unfamiliar area. It will also be of great interest established researchers seeking a single repository on the current state of knowledge, current debates and relevant literature.
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