Essays on Risk-Return Relation and Asset Pricing

Abstract

In this dissertation, I empirically examine stock market valuations, with a particular focus on the risk-return relation and the respective roles of cash flow and discount rate news.In Chapter 1, I empirically investigate the risk-return relation under the investors' subjective volatility expectations, which deviate from the rational expectations. I first derive the objective risk premium under the slow-moving subjective volatility expectations based on the theoretical model of Lochstoer and Muir (2022) and show that the slow-moving volatility expectation generates a lead-lag specification in the objective risk-return relation. Then, I develop and estimate an empirical model by employing the log-linear present value framework. The empirical results using U.S. monthly excess stock returns suggest that the slow-moving feature of volatility expectations and the lead-lag structure in the objective risk-return relation are both significantly identified from the data. The parameter estimates suggest that while the objective risk-return relation can be negative, the subjective risk-return relation remains strongly positive, aligning with the key prediction and assumption in Lochstoer and Muir (2022). Moreover, I find that incorporating subjective expectations that deviate from rational expectations helps explain the variation in the Sharpe ratio. In Chapter 2, I examine the subjective risk-return relation from the observed stock return data in the presence of information rigidity in investors' volatility expectations. Based on the present-value approach of Campbell and Shiller (1988), I develop an empirical model for excess stock returns by introducing the sticky information model of Mankiw and Reis (2002) into aggregate subjective volatility expectations, while using realized volatility to capture time-varying risk. The estimation results based on U.S. monthly excess stock returns and realized volatility suggest that a significant information rigidity component and a positive and statistically significant subjective risk-return relation are identified from the observed stock return data. Meanwhile, the restriction among parameters implied by the present-value approach is rejected, indicating that other factors may influence stock return variations. I suggest that investors' overextrapolative belief may help explain the rejection of the restriction. I also find a state-dependent information rigidity: it increases during a period with lower macroeconomic volatility. Consistent with the findings of Coibion and Gorodnichenko (2015), the estimation results indicate that the degree of information rigidity increased during the Great Moderation period. Chapter 3 explores the regime dependency and time variation of the relative importance of cash flow news and discount rate news in explaining excess stock return variance. To this end, I apply the variance decomposition method of Campbell and Ammer (1993) to the threshold VAR (TVAR) and the time-varying parameter VAR with stochastic volatilities (TVP-VAR-SV). To identify the regimes in the stock market, I use the Chicago Fed's financial condition index and investor sentiment index constructed by Baker and Wurgler (2006). The variance decomposition results using TVAR suggest that the contribution of discount rate news increases during tight financial conditions or high investor sentiment regimes. The result of TVP-SV-VAR indicates that cash flow news has become more important than discount rate news after the 1990s. I propose possible explanations for the results. First, the regime-dependent relative importance may be associated with the change in attention allocations of investors and the asymmetric stock return predictability across the regimes. Second, the reversal of the relative importance after the 1990s may be attributed to the less volatile discount rate news caused by increased information rigidity and changes in the return-earnings relationship after the onset of the Great Moderation.