PhDThis thesis comprises three studies on asset pricing under market frictions. In Chapter One, I derive a model-free formula to estimate the contribution of frictions to expected returns (CFER) within a formal asset pricing setting. I show that properly scaled deviations from put-call parity reliably estimate CFER. The estimated CFER is sizable, it predicts stock returns and it has superior properties than previously proposed measures of deviations from put-call parity. I nd that its predictive power stems from capturing the e ect of market frictions, rather than from omitted factors and informed option trading. My ndings suggest that market frictions, especially transaction costs, have a sizable e ect even on large optionable stocks. In Chapter Two, I study the e ect of CFER on the risk-neutral expected asset return and the return predictive ability of the risk-neutral skewness (RNS). I show that a non-zero CFER is equivalent to the violation of the martingale restriction (MR), that is, the deviation of the risk-neutral expected return from the risk-free rate. I theoretically and empirically show that Bakshi et al.'s (2003) formula for RNS incurs a bias when the underlying asset violates MR. I document that the ability of RNS to predict stock returns stems from its estimation bias due to the violation of MR, implying that its predictability is driven by the presence of market frictions. In Chapter Three, I examine the mean-variance portfolio strategy, which uses the estimated CFER as the mean stock return input. This CFER-based strategy outperforms various strategies including the equally-weighted portfolio, minimum variance portfolios and existing option-based portfolio strategies, especially when constraints on portfolio weights are imposed. My empirical results complement Jagannathan and Ma's (2002, 2003) theoretical nding that constraints on portfolio weights help to improve the performance of mean-variance portfolio by mitigating measurement errors in the expected stock returns.Bank of Japa
