語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
FindBook
Google Book
Amazon
博客來
Three Essays on Energy and Agricultural Price Analysis.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Three Essays on Energy and Agricultural Price Analysis./
作者:
Farhangdoost, Sara.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:
132 p.
附註:
Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:
Dissertations Abstracts International83-11B.
標題:
Futures. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29058021
ISBN:
9798426867635
Three Essays on Energy and Agricultural Price Analysis.
Farhangdoost, Sara.
Three Essays on Energy and Agricultural Price Analysis.
- Ann Arbor : ProQuest Dissertations & Theses, 2021 - 132 p.
Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Thesis (Ph.D.)--West Virginia University, 2021.
This item must not be sold to any third party vendors.
This dissertation consists of three essays on energy and agricultural commodity price analysis: 1) Natural Gas Price Forecasting in a Changing World; 2) The Effect of EIA Storage Announcement on Natural Gas Returns: A Comprehensive Analysis; and 3) Forecasting the U.S. Season-Average Farm Price of Corn: Derivation of an Alternative Futures based Forecasting Model.The first essay evaluates the performances of various individual and composite forecasting models when predicting natural gas prices in the United States. The empirical results show that forecast generated by the Energy Information Administration Short-Term Energy Outlook provides a more accurate price prediction at longer forecasting horizons (6- and 12-month ahead) while futuresbased forecasts perform better in the short-run (1- and 3-month ahead). Projections based on timeseries models perform well at longer forecast horizons when price volatility is relatively low. Further, the Hotelling model performs well for 1- and 3-month ahead forecast horizons. Our findings further support the additional benefit of composite forecasts based on individual methods for more accurate predictions; however, the performance is not uniform at different forecasting horizons.The second essay examines how natural gas prices react to inventory surprises contained in Energy Information Administration's weekly inventory report. Results indicate that natural gas prices are more responsive to 1) negative (more-than-expected) surprise storage news as compared to positive (less-than-expected) surprises, 2) news released during the injection season as compared to the withdrawal season, and 3) inventory surprises occurring in periods of tight supply in withdrawal season compared to when the market has an abundant supply. Finally, we find that EIA's inventory report has exerted a smaller impact on natural gas prices over time. Possible contributing factors to this declining impact include the increasing availability of alternative information providers in the market, the relatively over-supply of natural gas during the period of analysis since the rise of unconventional production, and a more integrated regional market that can transport natural gas from production to consumption regions more efficiently.The third essay investigates an alternative futures-based procedure to forecast the season-average farm price (SAFP) for U.S. corn, an under-researched price forecast. With the exceptionally volatile conditions experienced in the corn market since 2006, the need for price forecasting has become more critical. The new model developed in this essay performs better than two widely watched season-average price forecasts (World Agricultural Supply and Demand Estimates and the Hoffman futures-based forecasts) at the beginning of the post-harvest season, and just as well as those forecasts at the beginning of the forecast cycle and in the later post-harvest season. We attribute the performance of the proposed model's forecasts to its assignment of heterogeneous weights to both futures and cash prices depending on the underlying market conditions. Improved performance of the proposed model's forecasts is especially noticeable when the market is more volatile.
ISBN: 9798426867635Subjects--Topical Terms:
657649
Futures.
Three Essays on Energy and Agricultural Price Analysis.
LDR
:04343nmm a2200373 4500
001
2348951
005
20220920134630.5
008
241004s2021 ||||||||||||||||| ||eng d
020
$a
9798426867635
035
$a
(MiAaPQ)AAI29058021
035
$a
(MiAaPQ)WVirginia11183
035
$a
AAI29058021
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Farhangdoost, Sara.
$3
3688336
245
1 0
$a
Three Essays on Energy and Agricultural Price Analysis.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2021
300
$a
132 p.
500
$a
Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500
$a
Advisor: Etienne, Xiaoli.
502
$a
Thesis (Ph.D.)--West Virginia University, 2021.
506
$a
This item must not be sold to any third party vendors.
520
$a
This dissertation consists of three essays on energy and agricultural commodity price analysis: 1) Natural Gas Price Forecasting in a Changing World; 2) The Effect of EIA Storage Announcement on Natural Gas Returns: A Comprehensive Analysis; and 3) Forecasting the U.S. Season-Average Farm Price of Corn: Derivation of an Alternative Futures based Forecasting Model.The first essay evaluates the performances of various individual and composite forecasting models when predicting natural gas prices in the United States. The empirical results show that forecast generated by the Energy Information Administration Short-Term Energy Outlook provides a more accurate price prediction at longer forecasting horizons (6- and 12-month ahead) while futuresbased forecasts perform better in the short-run (1- and 3-month ahead). Projections based on timeseries models perform well at longer forecast horizons when price volatility is relatively low. Further, the Hotelling model performs well for 1- and 3-month ahead forecast horizons. Our findings further support the additional benefit of composite forecasts based on individual methods for more accurate predictions; however, the performance is not uniform at different forecasting horizons.The second essay examines how natural gas prices react to inventory surprises contained in Energy Information Administration's weekly inventory report. Results indicate that natural gas prices are more responsive to 1) negative (more-than-expected) surprise storage news as compared to positive (less-than-expected) surprises, 2) news released during the injection season as compared to the withdrawal season, and 3) inventory surprises occurring in periods of tight supply in withdrawal season compared to when the market has an abundant supply. Finally, we find that EIA's inventory report has exerted a smaller impact on natural gas prices over time. Possible contributing factors to this declining impact include the increasing availability of alternative information providers in the market, the relatively over-supply of natural gas during the period of analysis since the rise of unconventional production, and a more integrated regional market that can transport natural gas from production to consumption regions more efficiently.The third essay investigates an alternative futures-based procedure to forecast the season-average farm price (SAFP) for U.S. corn, an under-researched price forecast. With the exceptionally volatile conditions experienced in the corn market since 2006, the need for price forecasting has become more critical. The new model developed in this essay performs better than two widely watched season-average price forecasts (World Agricultural Supply and Demand Estimates and the Hoffman futures-based forecasts) at the beginning of the post-harvest season, and just as well as those forecasts at the beginning of the forecast cycle and in the later post-harvest season. We attribute the performance of the proposed model's forecasts to its assignment of heterogeneous weights to both futures and cash prices depending on the underlying market conditions. Improved performance of the proposed model's forecasts is especially noticeable when the market is more volatile.
590
$a
School code: 0256.
650
4
$a
Futures.
$3
657649
650
4
$a
Forecasting.
$3
547120
650
4
$a
Seasons.
$3
523975
650
4
$a
Ethanol.
$3
1613481
650
4
$a
Agricultural commodities.
$3
3562094
650
4
$a
Hurricanes.
$3
551051
650
4
$a
Neural networks.
$3
677449
650
4
$a
Artificial intelligence.
$3
516317
650
4
$a
Energy.
$3
876794
650
4
$a
Statistics.
$3
517247
690
$a
0338
690
$a
0503
690
$a
0800
690
$a
0501
690
$a
0791
690
$a
0438
690
$a
0463
710
2
$a
West Virginia University.
$3
1017532
773
0
$t
Dissertations Abstracts International
$g
83-11B.
790
$a
0256
791
$a
Ph.D.
792
$a
2021
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29058021
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9471389
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入