Empirical Studies on Volatility in International Stock Markets

by Eugenie M.J.H. Hol

Overview

Empirical Studies on Volatility in International Sk Markets describes the existing techniques for the measurement and estimation of volatility in international sk markets with emphasis on the SV model and its empirical application. Eugenie Hol develops various extensions of the SV model, which allow for additional variables in both the mean and the variance equation. In addition, the forecasting performance of SV models is compared not only to that of the well-established GARCH model but also to implied volatility and so-called realised volatility models which are based on intraday volatility measures.

The intended readers are financial professionals who seek to obtain more accurate volatility forecasts and wish to gain insight about state-of-the-art volatility modelling techniques and their empirical value, and academic researchers and students who are interested in financial market volatility and want to obtain an updated overview of the various methods available in this area.

Product Details

ISBN-13:	9781441953759
Publisher:	Springer US
Publication date:	11/19/2010
Series:	Dynamic Modeling and Econometrics in Economics and Finance , #6
Edition description:	Softcover reprint of hardcover 1st ed. 2003
Pages:	161
Product dimensions:	6.10(w) x 9.25(h) x 0.01(d)

Table of Contents

1. Introduction.- 2. Asset Return Volatility Models.- 3. The Shastic Volatility in Mean Model: Empirical evidence from international sk markets.- 4. Forecasting with Volatility Models.- 5. Implied Volatility.- 6. Forecasting the Variability of Sk Index Returns with Shastic Volatility Models and Implied Volatility.- 7. Sk Index Volatility Forecasting with High Frequency Data.- 8. Conclusions.- Appendices.- A. Estimation of the SVM Model.- A.1 Model.- A.2 Likelihood Evaluation Using Importance Sampling.- A.3 Approximating Gaussian Model Used For Importance Sampling.- A.4 Monte Carlo Evidence of Estimation Procedure.- B. Estimation of the SVX Models.- B.1 The SVX Model in State Space Form.- B.2 Parameter Estimation by Simulated Maximum Likelihood.- B.3 Computational Implementation.- C. Data and Programs.