追蹤溫度的投資組合

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姓名

張秀雲(Hsiu-yun Chang) 查詢紙本館藏

畢業系所

財務金融學系

論文名稱

追蹤溫度的投資組合
(Temperature Tracking Portfolios)

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至系統瀏覽論文 ( 永不開放)

摘要(中)

一個追蹤溫度的投資組合是由一群與天氣相關商品的報酬率根據不同的權重比例投資組合而成的。我們從一個氣候衍生性商品發行商的角度，提供了另一個方法來為其商品做定價。從實驗觀察可以發現：一個追蹤溫度的投資組合的確可以用來預測下一期的溫度。因此，我們可以利用這種追蹤型的投資組合來解決天氣是無法交易的特性；而且實際上，發行商或者是投資人也可以利用此一追蹤型的投資組合來對溫度衍生性商品做避險的操作。本論文利用美國過去十年的歷史資料做研究，並且做樣本外的預測估計，結果發現利用追蹤溫度的投資組合所預測的溫度與現實世界的溫度高度相關。因此，利用追蹤溫度的投資組合對未來的溫度衍生性商品做避險的效果是不錯的。

摘要(英)

A temperature tracking portfolio is a portfolio of some correlated weather products with returns that can track temperature variables. This paper provides another way for pricing temperature derivatives from the viewpoint of an issuer. Empirical observations show that temperature tracking portfolios can be useful for forecasting temperature. Therefore, the tracking portfolios can solve the problem that weather is non-tradable and can be used for issuers or investors to hedge temperature derivatives in real time. In this paper, we use the past ten years’ data to investigate how well a tracking portfolio can be. The results of forecasting effect in this paper are very good. It shows that the temperature we forecasted is similar to the real one.

關鍵字(中)

★ 追蹤溫度的投資組合
★ 溫度衍生性商品

關鍵字(英)

★ Tracking Portfolios
★ Temperature Derivatives

論文目次

1. Introduction...........................................................................................1
1.1 Motivation........................................................................................1
1.2 Literature Review...........................................................................2
2. Methodology..........................................................................................6
2.1 Stepwise regression.........................................................................6
2.2 Tracking portfolios..........................................................................8
3. Data Description.................................................................................10
3.1 Description of Temperature Derivatives.....................................10
3.2 The characteristic of 18 main cities in the U.S...........................11
3.3 Description of tracking variables................................................12
4. Results..................................................................................................14
4.1 Results of year data type..............................................................14
4.2 Results of seasonal data type.......................................................14
5. Out-of-sample forecasting..................................................................18
5.1 Rolling Regression........................................................................18
5.2 Application.....................................................................................20
6. Conclusions..........................................................................................23
References................................................................................................25
Appendix A. Auto correlation for 18 cities in U.S................................27
Appendix B. Correlation between 18 cities in U.S…………………...28
Appendix C. The name of the temperature correlated products.......29

參考文獻

Brody, D.C., Syroka, J., and Zervos, M., 2002, Dynamical pricing of weather derivatives, Quantitative Finance, 2, 189-198.
Campbell, S. D., and Diebold, F.X., 2005, Weather Forecasting for Weather Derivatives, Journal of the American Statistical Association, Vol 100, No. 469,
Cao, M., and Wei, J., 2004, Weather Derivatives Valuation and Market Price of Weather Risk, Journal of Futures Markets, 24, No.11, 1065-1089.
Hanley, M., 1999, Hedging the force of nature, Risk Professional, issue 1, 21-25.
Lamont, O. A., 2001, Economic tracking portfolios, Journal of Econometrics, No. 105, 161-184.
Muller, A., and Grandi, M., 2000, Weather Derivatives: A Risk Management Tool for Weather-sensitive Industries, Geneva Papers on Risk and Insurance, Vol. 25, No. 2, 273-287.
Oetomo, T. and Stevenson, M., 2006, Hot or Cold? : A comparison of different approaches to the pricing of weather derivatives, Working Paper.
Richards, T. J., Manfredo, M. R., and Sanders, D. R., 2004, Pricing Weather Derivatives, American Journal of Agriculture Economics, 86(4), 1005-1017
Taylor, J.W., and Buizza, R., 2006, Density forecasting for weather derivative pricing, International journal of forecasting, 22, 29-42.

指導教授

周賓凰(Pin-Huang Chou)

審核日期

2007-7-13

推文