Information Spillover Effect and Autoregressive Conditional Duration Models

Cover
Half Title
Title Page
Copyright Page
Table of Contents
List of figures
List of tables
Preface
1 Introduction
	1.1 Review of recent developments
	1.2 Organization and major conclusions
2 Methodology to detect extreme risk spillover
	2.1 Granger causality in risk
	2.2 Method and test statistics
	2.3 Asymptotic theory
	2.4 Two-way Granger causality in risk
	2.5 Finite-sample performance
	2.6 Conclusion
	Notes
3 VaR estimation
	3.1 Upside VaR and Downside VaR
	3.2 Parametric conditional VaR estimation
	3.3 Semi-parametric VaR estimation based on volatility, skewness and kurtosis
	3.4 Nonparametric VaR estimation based on kernel function
	3.5 Backtest
	3.6 Data
	3.7 Empirical analysis in Chinese futures market
	3.8 Conclusion
4 Extreme risk spillover between Chinese stock markets and international stock markets
	4.1 The Chinese stock market
	4.2 Data
	4.3 Evidence on Granger causality in risk
	4.4 Conclusion
	Notes
5 Information spillover effects between Chinese futures market and spot market
	5.1 Granger causality test
	5.2 Data
	5.3 VaR estimation
	5.4 Empirical results for information spillover between futures market and spot market
	5.5 Conclusion
6 How well can autoregressive duration models capture the price durations dynamics of foreign exchanges?
	6.1 Nonparametric density forecast evaluation
	6.2 ACD models
	6.3 Data and estimation
	6.4 Empirical evidence
	6.5 Conclusion
	Notes
7 Intraday effect
	7.1 Calendar Effect
	7.2 Data
	7.3 Intraday trends of yield and volume
	7.4 Analysis of correlation among yield, volume and open interest
	7.5 Conclusion
8 Conclusions and perspective studies
Appendix: mathematical proof
Bibliography
Index