Essays on hedge fund illiquidity, return predictability, and time-varying risk exposure - Kruttli, M (2015)
This thesis consists of three papers that make independent contributions to the field of financial economics. As such, the papers, Chapter 2, Chapter 3, and Chapter 4, can be read independently of each other. In Chapter 2, we construct a simple measure of the aggregate illiquidity of hedge fund portfolios, and show that it has strong in- and out-of-sample forecasting power for 72 portfolios of international equities, U.S. corporate bonds, and currencies, over the 1994 to 2011 period. The forecasting ability of hedge fund illiquidity for asset returns is, in most cases, greater than, and provides independent information relative to, well-known predictive variables for each of these asset classes. We construct a simple equilibrium model to rationalise our findings and empirically verify auxiliary predictions of the model. In Chapter 3, I analyse the risk-shifting of hedge funds. Since the information on hedge fund holdings is very restricted, researchers have used the variance of returns as a proxy for risk. I propose a new method for measuring the time-varying variance. I use this method to investigate whether equity long-short hedge funds engage in risk-shifting driven by their past performance relative to their peers. I find that hedge funds which have strongly underperformed or outperformed their peers in recent months increase their exposure to the core strategy, i.e. the equity long-short strategy, and to non-core strategies. The risk shifting is mitigated for hedge funds with long redemption periods. Chapter 4 contributes to the equity premium prediction literature. I improve the forecast performance of typical single variable predictive regressions used in the equity premium prediction literature through Bayesian priors derived from consumption-based asset pricing models. To implement these model-based priors, I develop a Bayesian procedure which is rooted in the macroeconometrics literature. I find that the model-based priors can increase the explanatory power, measured by the out-of-sample R2, of the single variable predictive regressions by several percentage points.