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Financial econometric modeling of ri...

Song, Jeongseok.

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Financial econometric modeling of risk in commodity markets.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Financial econometric modeling of risk in commodity markets./
作者:	Song, Jeongseok.
面頁冊數:	162 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-09, Section: A, page: 3503.
Contained By:	Dissertation Abstracts International65-09A.
標題:	Economics, Finance. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3146112
ISBN:	0496050869

Financial econometric modeling of risk in commodity markets.
Song, Jeongseok.

Financial econometric modeling of risk in commodity markets. - 162 p.

Source: Dissertation Abstracts International, Volume: 65-09, Section: A, page: 3503.

Thesis (Ph.D.)--Michigan State University, 2004.

This dissertation is composed of three interrelated body chapters. Its goal is to identify underlying sources of return volatility movement and analyze important problems in the economics of commodity markets by applying various time series econometric models to commodity market price data.

ISBN: 0496050869Subjects--Topical Terms:

626650
Economics, Finance.

Financial econometric modeling of risk in commodity markets.
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100 1 $a Song, Jeongseok. $3 1937236
245 1 0 $a Financial econometric modeling of risk in commodity markets.
300 $a 162 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-09, Section: A, page: 3503.
500 $a Adviser: Richard T. Baillie.
502 $a Thesis (Ph.D.)--Michigan State University, 2004.
520 $a This dissertation is composed of three interrelated body chapters. Its goal is to identify underlying sources of return volatility movement and analyze important problems in the economics of commodity markets by applying various time series econometric models to commodity market price data.
520 $a Chapter 2 investigates stochastic properties of daily cash price changes for six commodities: corn, soybeans, live cattle, live hogs, unleaded gasoline, and gold. We use the FIGARCH conditional variance model and the semi-parametric local Whittle estimation method to explore the daily cash return volatility behavior. We apply the long memory models to the temporally aggregated daily cash returns and compare the volatility dynamics at various sample frequencies.
520 $a Chapter 3 is concerned with commodity futures return volatility at daily and higher sample frequencies. In particular, strong intra-day periodicity in the high frequency return volatility is observed. We examine the high frequency futures return volatility pattern after removing the intra-day seasonality using the Flexible Fourier Form (FFF) filter and compare the volatility movement with the daily futures return volatility process.
520 $a Chapter 4 introduces a newly suggested volatility measure, the realized volatility, and applies the volatility measure to commodity futures market price data. The realized volatility is calculated as the sum of high frequency squared returns and exhibits some ideal statistical properties. Taking advantage of the stochastic properties of the realized volatility measure allows us to study important economic determinants for commodity futures market risk behavior.
590 $a School code: 0128.
650 4 $a Economics, Finance. $3 626650
650 4 $a Economics, Agricultural. $3 626648
690 $a 0508
690 $a 0503
710 2 0 $a Michigan State University. $3 676168
773 0 $t Dissertation Abstracts International $g 65-09A.
790 1 0 $a Baillie, Richard T., $e advisor
790 $a 0128
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3146112