This thesis broadly covers three different topics in empirical cross-sectional asset pricing and consists of three papers. The first paper prices the cross-sectional delta-hedged option and straddle returns in a consumption-based asset pricing model. Delta-hedged options are particularly sensitive to the underlying asset's volatility, which is in turn determined by the fundamental consumption volatility. The strong connection between delta-hedged options and consumption volatility provides us with powerful test assets to identify the consumption volatility premium and hence the preference of the representative agent. As indicated by our results, exposures to consumption growth, expected consumption growth, and consumption volatility are all significantly priced in the cross-section of delta-hedged option and straddle returns. Consumption growth and expected consumption growth command positive risk premiums, whereas consumption volatility commands a negative risk premium, suggesting that investors prefer early resolution of uncertainty. Our results further suggest that consumption risk exposures provide rational foundations for well-known relations between option moneyness or idiosyncratic underlying-stock volatility and the cross-section of delta-hedged option or straddle returns. The second paper relies on a hazard-model prediction of failure as proxy for firm-level distress risk. The paper discovers a significantly negative relation between firm-level distress risk and the cross-section of corporate bond returns, which is analogous to the often negative relation between distress risk and stock returns found in prior studies ("distress anomaly"). Our finding casts doubts on theories arguing that the distress anomaly arises due to shareholders shifting financial risk onto debtholders in distress. In accordance, proxy variables suggested by such theories do not condition the distress risk-bond return relation. Theories suggesting that distressed firms own valuable disinvestment options and thus have a low levered asset risk are more promising to explain the anomaly, with some of the proxy variables suggested by these theories conditioning the former relation. The third paper evaluates the prediction performance of machine learning methods in predicting the cross-sectional bond returns out-of-sample. Recent studies show that machine learning methods, especially neural networks, perform well in predicting the cross-sectional stock returns when the number of predictors is large. Prior research indicate that bond returns can be predicted by not only macroeconomic factors, bond market factors, and bond-level characteristics, but also stock market factors and stock-level characteristics. Therefore, the number of predictors in the bond market is even larger than that in the stock market, and the advantage of machine learning will be more pronounced in forecasting bond returns. In this work, I show that machine learning methods perform much better than the simple linear model in predicting bond returns out-of-sample.
|Date of Award||31 Dec 2020|
- The University of Manchester
|Supervisor||Hening Liu (Supervisor) & Kevin Aretz (Supervisor)|