Kurzbeschreibung Financial market volatility forecasting is one of today's most important areas of expertise for professionals and academics in investment, option pricing, and financial market regulation. While many books address financial market modeling, no single book is devoted primarily to the exploration of volatility forecasting and the practical use of forecasting models. Forecasting Volatility in Financial Markets provides practical guidance on this vital topic through an in-depth examination of a range of popular forecasting models. Details are provided on proven techniques for building volatility models, with guide-lines for actually using them in forecasting applications.
Aus dem Inhalt Foreword by Clive Granger.
Preface.
1 Volatility Definition and Estimation.
1.1 What is volatility?
1.2 Financial market stylized facts.
1.3 Volatility estimation.
1.4 The treatment of large numbers.
2 Volatility Forecast Evaluation.
2.1 The form of Xt.
2.2 Error statistics and the form of μt.
2.3 Comparing forecast errors of different models.
2.4 Regression-based forecast efficiency and orthogonality test.
2.5 Other issues in forecast evaluation.
3 Historical Volatility Models.
3.1 Modelling issues.
3.2 Types of historical volatility models.
3.3 Forecasting performance.
4 Arch.
4.1 Engle (1982).
4.2 Generalized ARCH.
4.3 Integrated GARCH.
4.4 Exponential GARCH.
4.5 Other forms of nonlinearity.
4.6 Forecasting performance.
5 Linear and Nonlinear Long Memory Models.
5.1 What is long memory in volatility?
5.2 Evidence and impact of volatility long memory.
5.3 Fractionally integrated model.
5.4 Competing models for volatility long memory.
6 Stochastic Volatility.
6.1 The volatility innovation.
6.2 The MCMC approach.
6.3 Forecasting performance.
7 Multivariate Volatility Models.
7.1 Asymmetric dynamic covariance model.
7.2 A bivariate example.
7.3 Applications.
8 Black-Scholes.
8.1 The Black-Scholes formula.
8.2 Black-Scholes and no-arbitrage pricing.
8.3 Binomial method.
8.4 Testing option pricing model in practice.
8.5 Dividend and early exercise premium.
8.6 Measurement errors and bias.
8.7 Appendix: Implementing Barone-Adesi and Whaley's efficient algorithm.
9 Option Pricing with Stochastic Volatility.
9.1 The Heston stochastic volatility option pricing model.
9.2 Heston price and Black-Scholes implied.
9.3 Model assessment.
9.4 Volatility forecast using the Heston model.
9.5 Appendix: The market price of volatility risk.
10 Option Forecasting Power.
10.1 Using option implied standard deviation to forecast volatility.
10.2 At-the-money or weighted implied?
10.3 Implied biasedness.
10.4 Volatility risk premium.
11 Volatility Forecasting Records.
11.1 Which volatility forecasting model?
11.2 Getting the right conditional variance and forecast with the 'wrong' models.
