Front Cover -- Forecasting Volatility in the Financial Markets -- Copyright Page -- Table of Contents -- List of contributors -- Preface to Third Edition -- Introduction -- Chapter 1 Volatility modelling and forecasting in finance -- 1.1 Introduction -- 1.2 Autoregressive moving average models -- 1.3 Changes in volatility -- 1.3.1 Volatility in financial time series: stylized facts -- 1.3.2 The basic set-up -- 1.4 ARCH models -- 1.4.1 Generalized ARCH -- 1.4.2 Integrated ARCH -- 1.4.3 Exponential ARCH -- 1.4.4 ARCH-M model -- 1.4.5 Fractionally integrated ARCH -- 1.4.6 Other univariate ARCH formulations -- 1.4.7 Multivariate ARCH models -- 1.5 Stochastic variance models -- 1.5.1 From continuous time financial models to discrete time SV models -- 1.5.2 Persistence and the SV model -- 1.5.3 Long memory SV models -- 1.5.4 Risk-return trade-off in SV models -- 1.5.5 Multivariate SV models -- 1.6 Structural changes in the underlying process -- 1.6.1 Regime switching models -- 1.6.2 Extensions of the regime switching models -- 1.7 Threshold models -- 1.7.1 Self-exciting threshold models -- 1.7.2 Open loop threshold models -- 1.7.3 Closed loop threshold models -- 1.7.4 Smooth threshold autoregressive models -- 1.7.5 Identification in SETAR models -- 1.7.6 A threshold AR(1) model -- 1.7.7 A threshold MA model -- 1.7.8 Threshold models and asymmetries in volatility -- 1.7.9 Testing for non-linearity -- 1.7.10 Threshold estimation and prediction of TAR models -- 1.8 Volatility forecasting -- 1.8.1 Volatility forecasting based on time-series models -- 1.8.2 Volatility forecasting based on option ISD (Implied Standard Deviation) -- 1.9 Conclusion -- References and further reading -- Notes -- Chapter 2 What good is a volatility model? -- Abstract -- 2.1 Introduction -- 2.1.1 Notation -- 2.1.2 Types of volatility models.

2.2 Stylized facts about asset price volatility -- 2.2.1 Volatility exhibits persistence -- 2.2.2 Volatility is mean reverting -- 2.2.3 Innovations may have an asymmetric impact on volatility -- 2.2.4 Exogenous variables may influence volatility -- 2.2.5 Tail probabilities -- 2.2.6 Forecast evaluation -- 2.3 An empirical example -- 2.3.1 Summary of the data -- 2.3.2 A volatility model -- 2.3.3 Mean reversion and persistence in volatility -- 2.3.4 An asymmetric volatility model -- 2.3.5 A model with exogenous volatility regressors -- 2.3.6 Aggregation of volatility models -- 2.4 Conclusions and challenges for future research -- References -- Notes -- Chapter 3 Applications of portfolio variety -- Abstract -- 3.1 Introduction -- 3.2 Some applications of variety -- 3.3 Empirical research on variety -- 3.4 Variety and risk estimation -- 3.5 Variety as an explanation of active management styles -- 3.6 Summary -- References -- Chapter 4 A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices -- 4.1 Introduction -- 4.2 Data -- 4.3 Theory and empirical methodology -- 4.3.1 Realized variance -- 4.3.2 Optimal sampling frequency -- 4.3.3 Estimation -- 4.3.4 Forecasting -- 4.4 Initial data analysis -- 4.5 Estimation and forecasting results -- 4.5.1 GARCH/EGARCH estimation -- 4.5.2 GARCH/EGARCH forecasting -- 4.6 Conclusion -- References -- Notes -- Appendices -- Appendix 4.1: VBA code for RV computation -- Appendix 4.2: EViews output for specification of mean equation -- Chapter 5 An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility -- Abstract -- 5.1 Introduction -- 5.1.1 Measuring volatility -- 5.1.2 Features of volatility -- 5.2 Volatility forecasting methods -- 5.2.1 ARIMA models -- 5.2.2 ARCH/GARCH -- 5.3 Assessing forecasting performance.

5.4 The performance of different forecasting models - empirical results -- 5.5 MSE of linear filter forecasts - theory -- 5.5.1 MSE of moving average methods -- 5.5.2 MSE under GARCH returns -- 5.6 Linear filter vs GARCH forecasts -- 5.6.1 MSE of linear filter forecasts -- 5.6.2 MSE of GARCH forecasts - simulation -- 5.6.3 Discussion of MSE results -- 5.6.4 The effects of leptokurtosis -- 5.7 Other methods of measuring true volatility -- 5.8 Empirical section - UK stock-market volatility -- 5.8.1 Data -- 5.8.2 Methodology -- 5.8.3 Results -- 5.8.4 Summary of results -- 5.9 Conclusion -- References -- Notes -- Chapter 6 Stochastic volatility and option pricing -- Summary -- 6.1 Introduction -- 6.2 The stochastic volatility (SV) model -- 6.2.1 The discrete-time stochastic autoregressive volatility model -- 6.2.2 The continuous-time stochastic volatility model -- 6.2.3 The jump-diffusion model with stochastic volatility -- 6.3 Option pricing under stochastic volatility -- 6.3.1 Pricing options under SV and jump: closed form solutions -- 6.3.2 Pricing options under SV and jump: Monte Carlo simulation -- 6.3.3 Implications of stochastic volatility and jumps on option prices -- 6.4 Estimation of stochastic volatility models -- 6.4.1 Indirect inference: a general estimation approach -- 6.4.2 Efficient method-of-moments (EMM): an efficient estimation approach -- 6.5 Forecasting stochastic volatility -- 6.5.1 Underlying volatility and implied volatility -- 6.5.2 Forecasting volatility based on standard volatility models -- 6.5.3 Forecasting underlying volatility using implied volatility -- 6.6 Conclusion -- 6.7 Appendix: derivation of option pricing formula for general jump-diffusion process with stochastic volatility and stochastic interest rates -- References -- Notes -- Chapter 7 Modelling slippage: an application to the bund futures contract.

7.1 Introduction -- 7.2 Data description -- 7.3 Slippage -- 7.4 Empirical evidence and econometric challenges -- 7.5 Conclusion -- 7.6 Appendix Regression results -- References -- Notes -- Chapter 8 Real trading volume and price action in the foreign exchange markets -- Summary -- 8.1 The currency market -- 8.2 Description of the EBS system -- 8.3 Empirical study of recorded transactions -- 8.4 Number of transactions, volume and volatility -- 8.5 Liquidity cost (Profit) -- 8.6 Liquidity cost and volume -- 8.7 Liquidity cost and time of the day -- 8.8 Final remarks -- References -- Notes -- Chapter 9 Implied risk-neutral probability density functions from option prices: a central bank perspective -- 9.1 Introduction -- 9.2 The relationship between option prices and RND functions -- 9.3 The Black-Scholes formula and its RND function -- 9.4 The implied volatility smile curve -- 9.5 Estimating implied terminal RND functions -- 9.6 Application of the two-lognormal mixture approach to options on short-term interest rates -- 9.7 Monetary policy uses of the information contained in implied RND functions -- 9.7.1 Validating the two-lognormal mixture distribution approach -- 9.7.2 Assessing monetary conditions -- 9.7.3 Assessing monetary credibility -- 9.7.4 Assessing the timing and effectiveness of monetary operations -- 9.7.5 Identifying market anomalies -- 9.8 Using the implied RND function as a volatility forecasting tool -- 9.8.1 Calculating RND-based uncertainty measures -- 9.8.2 Results -- 9.9 Conclusions -- References -- Notes -- Chapter 10 Hashing GARCH: a reassessment of volatility forecasting performance -- Summary -- 10.1 Introduction -- 10.2 ARCH forecasts and properties -- 10.3 Forecasting performance evaluation -- 10.3.1 Statistics-based evaluation -- 10.3.2 Utility-based evaluation -- 10.3.3 Profit-based/preference-free evaluation.

10.4 The pathology of ARCH forecast evaluation -- 10.4.1 A simulation experiment -- 10.5 Some general results -- 10.5.1 The compound normal -- 10.5.2 The Gram-Charlier class -- 10.6 Conclusions -- 10.7 Appendix -- 10.7.1 Mean and variance of ln(v2) -- 10.7.2 Proof of proposition 2 -- References -- Notes -- Chapter 11 Implied volatility forecasting: a comparison of different procedures including fractionally integrated models with applications to UK equity -- Summary -- 11.1 Introduction -- 11.2 Data -- 11.3 Models for volatility -- 11.3.1 GARCH models -- 11.3.2 Log-ARFIMA models -- 11.3.3 Moving average methods -- 11.4 Out-of-sample forecasting performance tests -- 11.4.1 Forecasting procedure -- 11.4.2 Results -- 11.5 Conclusion -- References -- Notes -- Chapter 12 GARCH predictions and the predictions of option prices -- 12.1 Prediction of GARCH models -- 12.2 Numerical results -- 12.3 Application to option pricing models -- References -- Notes -- Chapter 13 Volatility forecasting in a tick data model -- Summary -- 13.1 Introduction -- 13.2 The modelling framework -- 13.3 The functional form of v -- 13.4 Discussion and conclusions -- References -- Chapter 14 An econometic model of downside risk -- Summary -- 14.1 Introduction -- 14.2 Overview of semivariance -- 14.2.1 Introduction to semivariance -- 14.2.2 Limitations of variance as a risk measure -- 14.2.3 Limitations of semivariance -- 14.2.4 Asymmetry of returns and semivariance -- 14.3 Risk modelling techniques -- 14.3.1 The ARCH class of models -- 14.4 Dynamic models of semivariance -- 14.4.1 ARCH-semivariance (ARCH-SV) -- 14.4.2 Extension of model and other issues -- 14.4.3 A generalized ARCH-SV model -- 14.4.4 Estimation of the model -- 14.4.5 Extensions of the GARCH-SV model (GARCH-SV(M)) -- 14.4.6 The differential impact of lagged volatility.

14.4.7 Semivariance and asymmetric ARCH models.