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Essays in asset pricing.

Li, Ming.

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Essays in asset pricing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays in asset pricing./
作者:	Li, Ming.
面頁冊數:	142 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-05, Section: A, page: 1889.
Contained By:	Dissertation Abstracts International66-05A.
標題:	Economics, Finance. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3175191
ISBN:	0542134462

Essays in asset pricing.
Li, Ming.

Essays in asset pricing. - 142 p.

Source: Dissertation Abstracts International, Volume: 66-05, Section: A, page: 1889.

Thesis (Ph.D.)--University of California, Los Angeles, 2005.

A large body of empirical literature documents that across several types of assets returns exhibit momentum over short horizons and mean reversion in the long run. This pattern suggests the existence of unexploited predictable excess returns. In this paper we show that these patterns can be rationalized in a robust economy. In such an economy, the representative agent has a baseline model of the returns' generating process, but she fears that her model is misspecified. Her preferences reflect such a fear of misspecification. We characterize a linear robust equilibrium, which is analogous to the standard competitive rational expectations equilibrium. The key difference is that in a robust equilibrium there is a range over which predictable excess returns---under the data generating process---will not be exploited. Furthermore, momentum and mean reversion can naturally arise. In this sense our model in the first chapter provides micro-foundations to a class of behavioral finance models.

ISBN: 0542134462Subjects--Topical Terms:

626650
Economics, Finance.

Essays in asset pricing.
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245 1 0 $a Essays in asset pricing.
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500 $a Source: Dissertation Abstracts International, Volume: 66-05, Section: A, page: 1889.
500 $a Chair: Aaron Tornell.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2005.
520 $a A large body of empirical literature documents that across several types of assets returns exhibit momentum over short horizons and mean reversion in the long run. This pattern suggests the existence of unexploited predictable excess returns. In this paper we show that these patterns can be rationalized in a robust economy. In such an economy, the representative agent has a baseline model of the returns' generating process, but she fears that her model is misspecified. Her preferences reflect such a fear of misspecification. We characterize a linear robust equilibrium, which is analogous to the standard competitive rational expectations equilibrium. The key difference is that in a robust equilibrium there is a range over which predictable excess returns---under the data generating process---will not be exploited. Furthermore, momentum and mean reversion can naturally arise. In this sense our model in the first chapter provides micro-foundations to a class of behavioral finance models.
520 $a In the second chapter, I propose that the value-at-risk control under model uncertainty implies an endogenous time-varying variance model for the asset pricing. With proper assumption, this time-varying variance model can also be reduced to the Generalized Autoregressive Conditional Heteroskedasticity model (GARCH). This result provides basic economic foundation for the widely use of LARCH model in the financial markets. Time-varying variance model may also serve to understand the risk premium puzzle.
520 $a In the third chapter, I develop a continuous-time model of default and liquidity risks. The liquidity risk is defined as an exogenous need of selling the defaultable securities. In the model, both the default and the liquidity risks are described by jump processes. The model is a generalization of the fractional-market-value model introduced by Duffle & Singleton (1999). I derive the term structures for the defaultable securities. By treating all risk factors as unobservable processes, I adopt a Hidden Markov Model setting for estimation. Using corporate bond yield data from Fixed Income Securities Database, I estimate the parameters in the model by the Expectation-Maximization algorithm and the Kalman filtering. Then I examine the in-sample and out-of-sample performance of the model.
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