Eugenie M. J. H. Hol

Empirical Studies on Volatility in International Stock Markets (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 161
• Erscheinungstermin: 9. März 2013
• Englisch
• ISBN-13: 9781475751291
• Artikelnr.: 43993781

Inhaltsangabe

1. Introduction.- 2. Asset Return Volatility Models.- 3. The Stochastic Volatility in Mean Model: Empirical evidence from international stock markets.- 4. Forecasting with Volatility Models.- 5. Implied Volatility.- 6. Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility.- 7. Stock 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.