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Financial market volatility plays a crucial role in financial decision making, as volatility forecasts are important input parameters in areas such as option pricing, hedging strategies, portfolio allocation and Value-at-Risk calculations. The fact that financial innovations arrive at an ever-increasing rate has motivated both academic researchers and practitioners and advances in this field have been considerable. The use of Stochastic Volatility (SV) models is one of the latest developments in this area. Empirical Studies on Volatility in International Stock Markets describes the existing techniques for the measurement and estimation of volatility in international stock 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.
List of Figures. List of Tables.
2: Asset Return Volatility Models. 2.1. Empirical Stylised Facts of Sk Index Return Series. 2.2. Time-Varying Volatility Models. 2.3. Empirical Applications of Time Varying Volatility Models.
3: The Shastic Volatility in Mean Model: Empirical Evidence from International Sk Markets. 3.1. Introduction. 3.2. The Shastic Volatility in Mean Model. 3.3. Some Theory on the Relationship between Returns and Volatility. 3.4. Data. 3.5. Estimation Results for the SVM Model and Some Diagnostics. 3.6. Some Comparisons with GARCH-M Estimation Results. 3.7. Summary and Conclusions.
4: Forecasting with Volatility Models. 4.1. Volatility Models and Their Forecasts. 4.2. An Empirical Study of Six International Sk Indices.
5: Implied Volatility. 5.1. The Black-Scholes Option Pricing Model. 5.2. Forecasting with Implied Volatility: Empirical Evidence.
6: Forecasting the Variability of Sk Index Returns with Shastic Volatility Models and Implied Volatility. 6.1. Introduction. 6.2. Model Specifications. 6.3. Data Description and Empirical In-Sample Results. 6.4. Volatility Forecasting Methodology. 6.5. Out-of-Sample Results. 6.6. Summary and Conclusions.
7: Sk Index Volatility Forecasting with High-Frequency Data. 7.1. Introduction. 7.2. Sk Return Data and Volatility. 7.3. Realised Volatility Models. 7.4. Daily Time-Varying Volatility Models. 7.5. Forecasting Methodology and Evaluation Criteria. 7.6. Empirical Results. 7.7. Summary and Conclusions.
Appendices: 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.