STATISTICAL ANALYSIS OF FINANCIAL TIME SERIES AND RISK MANAGEMENT

STATISTICAL ANALYSIS OF FINANCIAL TIME SERIES AND RISK MANAGEMENT Public Deposited

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Ru, Hongyu
- Affiliation: College of Arts and Sciences, Department of Statistics and Operations Research

Abstract

The dissertation studies the dynamic of volatility, skewness, and value at risk for financial returns. It contains three topics. The first one is the asymptotic properties of the conditional skewness model for asset pricing. We start with a simple consumption-based asset pricing model, and make a connection between the asset pricing model and the regularity conditions for a quantile regression. We prove that the quantile regression estimators are asymptotically consistent and normally distributed under certain assumptions for the asset pricing model. The second one is about dynamic quantile models for risk management. We propose a financial risk model based on dynamic quantile regressions, which allows us to estimate conditional volatility and skewness jointly. We compare this approach with ARCHtype models by simulation. We also propose a density fitting approach by matching conditional quantiles and parametric densities to obtain the conditional distributions of returns. The third one is a simulation study of a consumption based asset pricing model. We show that larger returns and Sharp ratio can be obtained by introducing conditional asymmetry in the asset pricing model.

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