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dc.contributor.advisor Bollerslev, Tim
dc.contributor.advisor Tauchen, George
dc.contributor.advisor Gallant, A. Ronald
dc.contributor.advisor Eraker, Bjorn
dc.contributor.author Huang, Xin
dc.date 2007
dc.date.accessioned 2007-05-07T19:07:04Z
dc.date.available 2007-05-07T19:07:04Z
dc.date.issued 2007-05-07T19:07:04Z
dc.identifier.uri http://hdl.handle.net/10161/194
dc.description Dissertation
dc.description.abstract This dissertation consists of three related chapters that study financial market volatility, jumps and the economic factors behind them. Each of the chapters analyzes a different aspect of this problem. The first chapter examines tests for jumps based on recent asymptotic results. Monte Carlo evidence suggests that the daily ratio z-statistic has appropriate size, good power, and good jump detection capabilities revealed by the confusion matrix comprised of jump classification probabilities. Theoretical and Monte Carlo analysis indicate that microstructure noise biases the tests against detecting jumps, and that a simple lagging strategy corrects the bias. Empirical work documents evidence for jumps that account for seven percent of stock market price variance. Building on realized variance and bi-power variation measures constructed from high-frequency financial prices, the second chapter proposes a simple reduced form framework for modelling and forecasting daily return volatility. The chapter first decomposes the total daily return variance into three components, and proposes different models for the different variance components: an approximate long-memory HAR-GARCH model for the daytime continuous variance, an ACH model for the jump occurrence hazard rate, a log-linear structure for the conditional jump size, and an augmented GARCH model for the overnight variance. Then the chapter combines the different models to generate an overall forecasting framework, which improves the volatility forecasts for the daily, weekly and monthly horizons. The third chapter studies the economic factors that generate financial market volatility and jumps. It extends the recent literature by separating market responses into continuous variance and discontinuous jumps, and differentiating the market’s disagreement and uncertainty. The chapter finds that there are more large jumps on news days than on no-news days, with the fixed-income market being more responsive than the equity market, and non-farm payroll employment being the most influential news. Surprises in forecasts impact volatility and jumps in the fixed-income market more than the equity market, while disagreement and uncertainty influence both markets with different effects on volatility and jumps. JEL classification: C1, C2, C5, C51, C52, F3, F4, G1, G14 en
dc.format.extent 4659460 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.subject Stochastic Volatility en
dc.subject Jump en
dc.subject Realized Variance en
dc.subject Bipower Variation en
dc.subject Macroeconomic News Announcements en
dc.subject Economic Derivatives en
dc.title Financial Market Volatility and Jumps en
dc.type Dissertation en
dc.department Economics

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