| Summary: | 博士 === 國立臺灣大學 === 財務金融學研究所 === 97 === Abstract
Chapter 1 proposes a general continuous-time stochastic volatility model, the stochastic volatility feedback (SVF) model, to investigate the empirical and economic importance of the leverage and volatility feedback e ects, both of which are the two main explanations for volatility asymmetry. We empirically estimate our model by the Gallant and Tauchen''s (1996) e cient method of moment (EMM) approach using S&P 500 index returns. To assess the empirical importance of the leverage and volatility feedback e ects, we calculate the simulated cross-correlations between returns and volatility under various speci cations and the make a comparison to its realistic ones. To evaluate the economic importance, we perform the dynamic asset allocation model under various settings for the budget constraint and then compare the economic performances for the corresponding optimal investment strategies. Our ndings are as follows. (1) On comparing the magnitude of the two e ects, the volatility feedback e ect dominates the leverage e ect empirically. The volatility feedback e ect is the key determinant to replicate the sample cross-correlations between returns and volatility. The conventional stochastic volatility models with/without jumps fail to replication; (2) the volatility feedback e ect drives the intertemporal hedging demand, in contrast, the leverage e ect has a minor e ect on it; (3) a longer investment horizon or a higher current volatility enhance the volatility feedback e ect; (4) ignoring the volatility feedback e ect would su er from tremendous economic loss.
In chapter 2, we study the dynamic asset allocation problem in a general framework where the representative investor''s preferences include narrow framing/loss aversion parameters
while the intertemporal budget constraint contains the empirically-validated volatility feedback e ect. The optimal dynamic allocation of wealth among investment,
consumption, and savings can be solved in an analytically tractable way and is found to be profoundly in
uenced by the interactions between narrow framing/loss aversion and the volatility feedback e ect. In other words, a more elaborated speci cation of the intertemporal budget constraint including the volatility feedback e ect is indispensable to a complete analysis of narrow framing/loss aversion preferences in a dynamic asset allocation problem. In particular, we nd that introducing narrow framing/loss aversion without the volatility feedback e ect does help reduce investment and transfer the weights to savings, which implies that narrow framing/loss aversion can explain the equity premium puzzle as previous literature has suggested. However, the direction of weight shifts reverses noticeably once the volatility feedback e ect is incorporated. It seems that the preferences of narrow framing/loss aversion alone may not explain the equity premium puzzle in our general framework where the intertemporal budget constraint is more carefully speci ed.
In chapter 3, the classical nance theory claimed that irrational investors, who misperceive asset returns, would buy high and sell low, causing them to lose their wealth.
The recent researches, such as De Long, Shleifer, Summers and Waldman (1991), and Kogan, Ross, Wang and Wester eld (2006) advocated that irrational investors might form
portfolio allocations performing a higher growth rates that outgrow that of the Bayesian investor in market quilibrium. This paper addresses this issue in terms of investor''s asset
allocation. We conduct the empirical analysis of the investor''s asset allocation decision considering psychological biases. We specify a regime-switching dynamics of the investment opportunity set and use regime predicting and updating procedures to characterize
investor sentiment. Our ndings are as follows. (i) In comparison with the Bayesian investor, the optimistic one would like to bet on good states of the economy, more aggressively chasing /shorting the assets with higher rewards in good/bad times. In contrast, the pessimistic investor would take the opposite positions to the optimistic one; (ii) the optimistic investor has the best empirical performance of the portfolio allocation and
in turn outperforms the Bayesian one. The other irrational investors underperform the Bayesian one in most cases; (iii) while the predictability in the investment opportunity set is removed, the outperformance of the optimistic investor disappears. It suggests that the
bene t from the intertemporal hedging induced by the return predictability is a potential explanation to the domination of the optimistic investor in our analysis.
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