Browsing by Subject "portfolio management"

This dissertation consists of three essays that shed light on various problems in empirical asset pricing and portfolio management by applying high frequency econometric techniques. Chapter 1, An Efficient Factor from Basis “Anomalies”, examines the challenges brought by the massive asset-pricing “anomalies” and develops a novel method to construct a highly ex-post efficient portfolio that prices asset returns in a one-factor model, Relative Asset Pricing Model (RAP). The one single empirical factor outperforms and drives out 11 of the most acclaimed multi-factors combined. It provides evidence that the massive amount of asset pricing “anomalies” are in fact manifested by non-linear effects of three basic stock characteristics, size, book-to-market and momentum. It also demonstrates that an arbitrary number of trading signals can be engineered to pass existing asset pricing tests as new “unique anomalies”, even though they are purely the projections of the efficient factor beta onto a set of characteristics. Chapter 2, Good Volatility, Bad Volatility and the Cross Section of Stock Returns, documents that relative good-minus-bad jump measure extracted from high frequency intra-day data have strong cross-sectional return predictability. Chapter 3, Factors and Their Economic Value in Volatility Forecast, develops a simple and reliable volatility forecast model in large cross-section that incorporates volatility factor structure and add significant values to investors in portfolio optimization.