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Testing for Jumps and Modeling Volatility in Asset Prices

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dc.contributor.author Bjursell, Johan
dc.creator Bjursell, Johan
dc.date 2009-04-30
dc.date.accessioned 2009-07-25T17:49:28Z
dc.date.available NO_RESTRICTION en
dc.date.available 2009-07-25T17:49:28Z
dc.date.issued 2009-07-25T17:49:28Z
dc.identifier.uri https://hdl.handle.net/1920/4573
dc.description.abstract Observers of financial markets have long noted that asset prices are very volatile and commonly exhibit jumps (price spikes). Thus, the assumption of a continuous process for asset price behavior is often violated in practice. Although empirical studies have found that the impact of such jumps is transitory, the shortterm effect in the volatility may nonetheless be considerable with important financial implications for the valuation of derivatives, asset allocation and risk management. This dissertation contributes to the literature in two areas. First, I evaluate the small sample properties of a nonparametric method for identifying jumps. I focus on the implication of adding noise to the prices and recent methods developed to contend with such market frictions. Initially, I examine the properties and convergence results of the power variations that constitute the jump statistics. Then I document the asymptotic results of these jump statistics. Finally, I estimate their size and power. I examine these properties using a stochastic volatility model incorporating alternative noise and jump processes. I find that the properties of the statistics remain close to the asymptotics when methods for managing the effects of noise are applied judiciously. Improper use leads to invalid tests or tests with low power. Empirical evidence demonstrates that the nonparametric method performs well for alternative models, noise processes, and jump distributions. In the second essay, I present a study on market data from U.S. energy futures markets. I apply a nonparametric method to identify jumps in futures prices of crude oil, heating oil and natural gas contracts traded on the New York Mercantile Exchange. The sample period of the intraday data covers January 1990 to January 2008. Alternative methods such as staggered returns and optimal sampling frequency methods are used to remove the effects of microstructure noise which biases the tests against detecting jumps. I obtain several important empirical results: (i) The realized volatility of natural gas futures exceeds that of heating oil and crude oil. (ii) In these commodities, large volatility days are often associated with large jump components and large jump components are often associated with weekly announcements of inventory levels. (iii) The realized volatility and smooth volatility components in natural gas and heating oil futures are higher in winter months than in summer months. Moreover, cold weather and inventory surprises cause the volatility in natural gas and heating oil to increase during the winter season. (iv) The jump component produces a transitory surge in total volatility, and there is a strong reversal in volatility on days following a significant jump day. (v) I find that including jump and seasonal components as explanatory variables significantly improves the modeling and forecasting of the realized volatility.
dc.language.iso en_US en
dc.subject Realized and Bipower Variations en_US
dc.subject Jump Test Statistics en_US
dc.subject Monte Carlo Simulation en_US
dc.subject Energy Futures Stochastic Price Behavior en_US
dc.subject Energy Inventory Announcement Effects en_US
dc.subject Modeling Volatility and Jumps en_US
dc.title Testing for Jumps and Modeling Volatility in Asset Prices en
dc.type Dissertation en
thesis.degree.name Doctor of Philosophy in Computational Sciences and Informatics en
thesis.degree.level Doctoral en
thesis.degree.discipline Computational Sciences and Informatics en
thesis.degree.grantor George Mason University en


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