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.

Essays on Asset Pricing and Portfolio Choice

The first chapter offers an explanation for the properties of the nominal term structure of interest rates and time- varying bond risk premia based on a model with rare consumption disaster risk. In the model, expected inflation follows a mean reverting process but is also subject to possible large (positive) shocks when consumption disasters occur. The possibility of jumps in inflation increases nominal yields and the yield spread, while time-variation in the inflation jump probability drives time-varying bond risk premia. Predictability regressions offer independent evidence for the model\u27s ability to generate realistic implications for both the stock and bond markets. The second chapter studies the cross-section of stock returns. Why do value stocks have higher expected returns than growth stocks, in spite of having lower risk? Why do these stocks exhibit positive abnormal performance while growth stocks exhibit negative abnormal performance? This paper offers a rare-events based explanation, that can also account for facts about the aggregate market. Patterns in time-series predictability offer independent evidence for the model\u27s conclusions. The third chapter studies an asset allocation problem. It shows that learning about the parameters of the return process induces a large negative hedging demand in an investor who is optimally rebalancing her portfolio, even after she has observed 83 years of market asset data. For example, an investor with a 5-year investment horizon decreases the percentage of wealth she allocates to the stock index by over 20 percent when she takes learning into account. Furthermore, I show that the initial estimation sample length needs to be at least 500 years in order for the effect of learning to vanish

Dynamic interest rate and credit risk models

This thesis studies the pricing of Treasury bonds, the pricing of corporate bonds and the modelling of portfolios of defaultable debt. By drawing on the related literature, Chapter 1 provides economic background and motivation for the study of each of these topics. Chapter 2 studies the use of Gaussian affine dynamic term structure models (GDTSMs) for forming forecasts of Treasury yields and conditional decompositions of the yield curve into expectation and risk premium components. Specifically, it proposes market prices of risk that can generate bond price time series that are consistent with the important empirical result of Cochrane and Piazzesi (2005), that a linear combination of forward rates can forecast excess returns to bonds. Since the GDTSM here falls into the essentially affine class (Duffee (2002)), it is analytically tractable. Chapter 3 studies conditional risk premia in a commonly applied default intensity based model for pricing corporate bonds. Here, I refer to such models as completely affine defaultable dynamic term structure models (DDTSMs). There are two main contributions. First, I show that completely affine DDTSMs imply that the compensation for the risk associated with shocks to default intensities (the credit spread risk premium) is related to the volatility of default intensities. Second, I run regressions to show that this relationship holds in a set of corporate bond data. Finally, Chapter 4 proposes a new dynamic model for default rates in large debt port- folios. The model is similar in principle to Duffie, Saita, and Wang (2007) and Duffie, Eckner, Horel, and Saita (2009) in that the default intensity depends on the observed macroeconomic state and unobserved frailty variables. However, the model is designed for use with more commonly available aggregate, rather than individual, default data. Fitting the model to aggregate charge-off rates in US corporate, real-estate and non- mortgage retail sectors, it is found that interest rates, industrial production and unemployment rates have quantitatively plausible effects on aggregate default rates