Volatility forecasting

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1

Volatility Forecasting

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3,4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.

Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution

Includes bibliographical references.This thesis focuses on forecasting the volatility of daily returns using a double Markov switching GARCH model with a skewed Student-t error distribution. The model was applied to individual shares obtained from the Johannesburg Stock Exchange (JSE). The Bayesian approach which uses Markov Chain Monte Carlo was used to estimate the unknown parameters in the model. The double Markov switching GARCH model was compared to a GARCH(1,1) model. Value at risk thresholds and violations ratios were computed leading to the ranking of the GARCH and double Markov switching GARCH models. The results showed that double Markov switching GARCH model performs similarly to the GARCH model based on the ranking technique employed in this thesis

Copula stochastic volatility in oil returns: approximate Bayesian computation with volatility prediction

Modeling the volatility of energy commodity returns has become a topic of increased interest in recent years, because of the important role it plays in today's economy. In this paper we propose a novel copula-based stochas- tic volatility model for energy commodity returns that allows for asymmetric volatility persistence. We employ Approximate Bayesian Computation (ABC), a powerful tool to make inferences and predictions for such highly-nonlinear model. We carry out two simulation studies to illustrate that ABC is an appropriate alternative to standard MCMC-based methods when the state transition process is challenging to implement. Finally, we model the volatility of WTI and Brent oil futures' returns with the proposed copula-based stochastic volatility model and show that such model outperforms symmetric alternatives in terms of in- and out-of-sample volatility prediction accuracThe first author acknowledges financial support from Spanish Ministry of Science, Innovation and Universities, grant number ECO2017–83255-C3–2-P. The second and third authors acknowledge financial support from Agencia Estatal de Investigación (PID2019–108311GB-I00/AEI/10.13039/501100011033