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Financial market volatility and jumps.

Huang, Xin.

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Financial market volatility and jumps.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Financial market volatility and jumps./
作者:	Huang, Xin.
面頁冊數:	185 p.
附註:	Adviser: Tim Bollerslev.
Contained By:	Dissertation Abstracts International68-03A.
標題:	Economics, Finance. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3255448

Financial market volatility and jumps.
Huang, Xin.

Financial market volatility and jumps. - 185 p.

Adviser: Tim Bollerslev.

Thesis (Ph.D.)--Duke University, 2007.

JEL classification. C1, C2, C5, C51, C52, F3, F4, G1, G14.Subjects--Topical Terms:

626650
Economics, Finance.

Financial market volatility and jumps.
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100 1 $a Huang, Xin. $3 1286797
245 1 0 $a Financial market volatility and jumps.
300 $a 185 p.
500 $a Adviser: Tim Bollerslev.
500 $a Source: Dissertation Abstracts International, Volume: 68-03, Section: A, page: 1104.
502 $a Thesis (Ph.D.)--Duke University, 2007.
520 $a JEL classification. C1, C2, C5, C51, C52, F3, F4, G1, G14.
520 $a Keywords. Stochastic Volatility, Jump, Realized Variance, Bipower Variation, Macroeconomic News Announcements, Economic Derivatives.
520 $a 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.
520 $a 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.
520 $a 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.
590 $a School code: 0066.
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650 4 $a Economics, General. $3 1017424
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791 $a Ph.D.
792 $a 2007
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