Understanding risk: Investigation of the impact of volatility on financial market.

详细信息   

• 作者：Mishra ; Santosh Kumar.
• 学历：Doctor
• 年：2003
• 导师：Gonzalez-Rivera,Gloria,eadvisorLee,Tae-Hwy,eadvisor
• 毕业院校：University of California
• CBH：3109662
• Country：USA
• 语种：English
• FileSize：7474458
• Pages：144

文摘

My work includes a) a new semiparametric model for volatility,b) proposal of a new measure of risk based on ranking,and c) an empirical application which analyses the impact of several existing volatility models on asset returns and derivative pricing. Volatility is unobservable； thus the suitability of any existing volatility model depends on the type of agent facing the risk different loss functions for different type of agents). Chapter 1 deals with this issue. We analyze the predictive performance of various volatility models for stock returns. To compare their performance,we choose loss functions for which volatility estimation is of paramount importance. Loss functions are based on option pricing,utility function,a goodness-of-fit measure for a Value-at-Risk VaR) calculation,and predictive likelihood. Existence of several linear and nonlinear volatility models guides us towards a generic data dependent model,which can capture various types of nonlinearity. Chapter 2 proposes a new combined estimator,called semiparametric estimator,which incorporates the parametric and nonparametric estimators of the conditional variance in a multiplicative way. We develop the asymptotic theory for the nonlinearity parameters and suggest a test,which tests for the adequacy of a parametric model in capturing volatility dynamics. When the parametric part is GARCH our model allows for smooth time-varying persistence,which is driven by the magnitude and sign of the past innovation. In an empirical application,we analyze the five emerging market stock indices in our framework. We find that the persistence of a shock is longer for negative innovation compared to similar positive innovation. Chapter 3 proposes an extension of the meaning of volatility by introducing a measure,namely the Varying Cross-sectional Risk VCR) that is based on a ranking of returns. VCR is defined as the probability of a sharp jump over time in the position of an asset return within the cross-sectional return distribution of the assets that constitute the market,which is represented by the Standard and Poors 500 Index SP500). We model the joint dynamics of the cross-sectional position and the asset return by analyzing 1) the marginal probability distribution of a sharp jump in the cross-sectional position within the context of a duration model,and 2) the probability distribution of the asset return conditional on a jump,for which we specify different dynamics in returns depending upon whether or not a jump has taken place. The performance of our model is assessed in an out-of-sample exercise. Abstract shortened by UMI.)