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Localizing Temperature Risk

Research output: Contribution to journalArticlepeer-review

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Localizing Temperature Risk. / Härdle, Wolfgang Karl ; López Cabrera, Brenda; Okhrin, Ostap; Wang, Weining.

In: Journal of the American Statistical Association, Vol. 111, No. 516, 04.01.2017, p. 1491-1508.

Research output: Contribution to journalArticlepeer-review

Harvard

Härdle, WK, López Cabrera, B, Okhrin, O & Wang, W 2017, 'Localizing Temperature Risk', Journal of the American Statistical Association, vol. 111, no. 516, pp. 1491-1508. https://doi.org/10.1080/01621459.2016.1180985

APA

Härdle, W. K., López Cabrera, B., Okhrin, O., & Wang, W. (2017). Localizing Temperature Risk. Journal of the American Statistical Association, 111(516), 1491-1508. https://doi.org/10.1080/01621459.2016.1180985

Vancouver

Härdle WK, López Cabrera B, Okhrin O, Wang W. Localizing Temperature Risk. Journal of the American Statistical Association. 2017 Jan 4;111(516):1491-1508. https://doi.org/10.1080/01621459.2016.1180985

Author

Härdle, Wolfgang Karl ; López Cabrera, Brenda ; Okhrin, Ostap ; Wang, Weining. / Localizing Temperature Risk. In: Journal of the American Statistical Association. 2017 ; Vol. 111, No. 516. pp. 1491-1508.

Bibtex - Download

@article{1a47d6a3278942e79e484917e08c0816,
title = "Localizing Temperature Risk",
abstract = "On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. To apply the pricing tools of financial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with seasonality and intertemporal autocorrelation. Empirical work based on seasonality and autocorrelation correction reveals that the obtained residuals are heteroscedastic with a periodic pattern. The object of this research is to estimate this heteroscedastic function so that, after scale normalization, a pure standardized Gaussian variable appears. Earlier works investigated temperature risk in different locations and showed that neither parametric component functions nor a local linear smoother with constant smoothing parameter are flexible enough to generally describe the variance process well. Therefore, we consider a local adaptive modeling approach to find, at each time point, an optimal smoothing parameter to locally estimate the seasonality and volatility. Our approach provides a more flexible and accurate fitting procedure for localized temperature risk by achieving nearly normal risk factors. We also employ our model to forecast the temperaturein different cities and compare it to a model developed in 2005 by Campbell and Diebold. Supplementary materials for this article are available online.",
author = "H{\"a}rdle, {Wolfgang Karl} and {L{\'o}pez Cabrera}, Brenda and Ostap Okhrin and Weining Wang",
year = "2017",
month = jan,
day = "4",
doi = "10.1080/01621459.2016.1180985",
language = "English",
volume = "111",
pages = "1491--1508",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor and Francis Ltd.",
number = "516",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Localizing Temperature Risk

AU - Härdle, Wolfgang Karl

AU - López Cabrera, Brenda

AU - Okhrin, Ostap

AU - Wang, Weining

PY - 2017/1/4

Y1 - 2017/1/4

N2 - On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. To apply the pricing tools of financial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with seasonality and intertemporal autocorrelation. Empirical work based on seasonality and autocorrelation correction reveals that the obtained residuals are heteroscedastic with a periodic pattern. The object of this research is to estimate this heteroscedastic function so that, after scale normalization, a pure standardized Gaussian variable appears. Earlier works investigated temperature risk in different locations and showed that neither parametric component functions nor a local linear smoother with constant smoothing parameter are flexible enough to generally describe the variance process well. Therefore, we consider a local adaptive modeling approach to find, at each time point, an optimal smoothing parameter to locally estimate the seasonality and volatility. Our approach provides a more flexible and accurate fitting procedure for localized temperature risk by achieving nearly normal risk factors. We also employ our model to forecast the temperaturein different cities and compare it to a model developed in 2005 by Campbell and Diebold. Supplementary materials for this article are available online.

AB - On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. To apply the pricing tools of financial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with seasonality and intertemporal autocorrelation. Empirical work based on seasonality and autocorrelation correction reveals that the obtained residuals are heteroscedastic with a periodic pattern. The object of this research is to estimate this heteroscedastic function so that, after scale normalization, a pure standardized Gaussian variable appears. Earlier works investigated temperature risk in different locations and showed that neither parametric component functions nor a local linear smoother with constant smoothing parameter are flexible enough to generally describe the variance process well. Therefore, we consider a local adaptive modeling approach to find, at each time point, an optimal smoothing parameter to locally estimate the seasonality and volatility. Our approach provides a more flexible and accurate fitting procedure for localized temperature risk by achieving nearly normal risk factors. We also employ our model to forecast the temperaturein different cities and compare it to a model developed in 2005 by Campbell and Diebold. Supplementary materials for this article are available online.

U2 - 10.1080/01621459.2016.1180985

DO - 10.1080/01621459.2016.1180985

M3 - Article

VL - 111

SP - 1491

EP - 1508

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

IS - 516

ER -