Essays on jumps and common jumps in financial volatility
Creators: name: Liao, Yin
Publisher: name: The Australian National University
Year: 2011
Subjects: subject: HG106.L53 2011
subject: Finance Mathematical models
subject: Jump processes
subject: Monte Carlo method
subject: Capital investments China

description: This dissertation consists of three essays that contribute to the literature on jumps in financial volatility. Jumps have far-reaching implications for financial endeavors such as asset pricing, risk management, and portfolio allocation, and therefore it is important to document their occurrence and develop techniques and models that can be used to study their behavior. This dissertation firstly examines the different roles that jumps and the continuous component of an asset's price process can play in the forecasting of financial volatility. It then develops separate factor models for jumps and the continuous component and combines these models to generate an overall forecasting framework for multivariate financial volatility. Finally, it offers a new econometric method to test for common jumps in a panel of highfrequency financial data. This dissertation contains both theoretical and empirical contributions, and since the empirical work is based on Chinese stocks, it provides an interesting and useful analysis of jump behavior and financial volatility in an emerging market. The first essay of this dissertation explores the forecasting ability of jumps for financial volatility, particularly with respect to risk prediction. This essay studies dynamic patterns in jumps using high-frequency data for three Chinese medical stocks. Relative to stocks in the developed US market, the essay finds that jumps occur in the Chinese stock market more frequently. Further, jumps and in particular jump size are more predictable. The essay compares the Value-at-Risk (VaR) forecasting performance of a newly proposed Heterogeneous Autoregressive Realized Volatility (HAR-CJN) model of Andersen et al (2010), which allows for jumps, with that of two commonly used realized volatility models, which do not take account of jumps. Two-step VaR backtesting shows that compared with the Autoregressive Moving Average (ARMA) model and the Heterogeneous Autoregressive (HAR) model of Corsi (2009), the HAR-CJN model is able to provide a better performance in downside risk prediction for the three stocks, particularly for the stock that experiences relatively larger jumps. These findings suggest that explicitly considering jumps in a volatility model largely increases the capacity of a model to capture the extreme tail behavior of asset return distributions. Meanwhile, due to the stronger role that jumps play in emerging markets, accounting for jumps in volatility forecasting might be more important for emerging markets than that for developed markets. Since realized volatility forecasting can benefit from separately modeling and forecasting the two variation sources: jumps, and continuous variation of asset prices, the second essay of this dissertation extends the separation to a multivariate setting. It proposes a tobit multivariate factor model for the jumps coupled with a standard multivariate factor model for the continuous sample path to jointly forecast volatility in the above-mentioned three Chinese mainland stocks. Out-of-sample forecast analysis shows that separate multivariate factor models for the two volatility processes outperform a single multivariate factor model of realized volatility, and that a single multivariate factor model of realized volatility outperforms univariate models, even if they separately account for jumps. Jump detection is a prerequisite for modeling and forecasting jumps, but most literature on jump detection is limited to univariate and bivariate settings. The third essay of this dissertation proposes a new test for common intraday jumps in a panel of high-frequency financial data. The novelty of this test is that the estimator of the covariance structure of a panel of asset prices is based on intraday first-high-low-last observations of asset prices. The essay studies the finite sample behavior of the proposed first-high-lowlast price based test using Monte Carlo simulation, and finds that it is more powerful than the existing return-based co-jump test proposed by Bollerslev et al (2008). When applied to a panel of high-frequency data from the Chinese mainland stock market, the first-high-low-last price based test identifies more common jumps than the return-based test in this emerging market.