Tail Risk in Weather Derivatives
Weather derivative markets, particularly Chicago Mercantile Exchange (CME) Heating Degree Day (HDD) and Cooling Degree Day (CDD) futures, face challenges from complex temperature dynamics and spatially heterogeneous co-extremes that standard Gaussian models overlook. Using daily data from 13 major U...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Commodities |
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| Online Access: | https://www.mdpi.com/2813-2432/4/2/11 |
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| author | Tuoyuan Cheng Saikiran Reddy Poreddy Kan Chen |
| author_facet | Tuoyuan Cheng Saikiran Reddy Poreddy Kan Chen |
| author_sort | Tuoyuan Cheng |
| collection | DOAJ |
| description | Weather derivative markets, particularly Chicago Mercantile Exchange (CME) Heating Degree Day (HDD) and Cooling Degree Day (CDD) futures, face challenges from complex temperature dynamics and spatially heterogeneous co-extremes that standard Gaussian models overlook. Using daily data from 13 major U.S. cities (2014–2024), we first construct a two-stage baseline model to extract standardized residuals isolating stochastic temperature deviations. We then estimate the Extreme Value Index (EVI) of HDD/CDD residuals, finding that the nonlinear degree-day transformation amplifies univariate tail risk, notably for warm-winter HDD events in northern cities. To assess multivariate extremes, we compute Tail Dependence Coefficient (TDC), revealing pronounced, geographically clustered tail dependence among HDD residuals and weaker dependence for CDD. Finally, we compare Gaussian, Student’s <i>t</i>, and Regular Vine Copula (R-Vine) copulas via joint VaR–ES backtesting. The R-Vine copula reproduces HDD portfolio tail risk, whereas elliptical copulas misestimate portfolio losses. These findings highlight the necessity of flexible dependence models, particularly R-Vine, to set margins, allocate capital, and hedge effectively in weather derivative markets. |
| format | Article |
| id | doaj-art-c5b6847ab4f84b6f85e72fe44cd35540 |
| institution | OA Journals |
| issn | 2813-2432 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Commodities |
| spelling | doaj-art-c5b6847ab4f84b6f85e72fe44cd355402025-08-20T02:24:34ZengMDPI AGCommodities2813-24322025-06-01421110.3390/commodities4020011Tail Risk in Weather DerivativesTuoyuan Cheng0Saikiran Reddy Poreddy1Kan Chen2Risk Management Institute, National University of Singapore, Singapore 119244, SingaporeRisk Management Institute, National University of Singapore, Singapore 119244, SingaporeRisk Management Institute, National University of Singapore, Singapore 119244, SingaporeWeather derivative markets, particularly Chicago Mercantile Exchange (CME) Heating Degree Day (HDD) and Cooling Degree Day (CDD) futures, face challenges from complex temperature dynamics and spatially heterogeneous co-extremes that standard Gaussian models overlook. Using daily data from 13 major U.S. cities (2014–2024), we first construct a two-stage baseline model to extract standardized residuals isolating stochastic temperature deviations. We then estimate the Extreme Value Index (EVI) of HDD/CDD residuals, finding that the nonlinear degree-day transformation amplifies univariate tail risk, notably for warm-winter HDD events in northern cities. To assess multivariate extremes, we compute Tail Dependence Coefficient (TDC), revealing pronounced, geographically clustered tail dependence among HDD residuals and weaker dependence for CDD. Finally, we compare Gaussian, Student’s <i>t</i>, and Regular Vine Copula (R-Vine) copulas via joint VaR–ES backtesting. The R-Vine copula reproduces HDD portfolio tail risk, whereas elliptical copulas misestimate portfolio losses. These findings highlight the necessity of flexible dependence models, particularly R-Vine, to set margins, allocate capital, and hedge effectively in weather derivative markets.https://www.mdpi.com/2813-2432/4/2/11heating degree daycooling degree dayextreme value theoryvine copulavalue-at-riskexpected shortfall |
| spellingShingle | Tuoyuan Cheng Saikiran Reddy Poreddy Kan Chen Tail Risk in Weather Derivatives Commodities heating degree day cooling degree day extreme value theory vine copula value-at-risk expected shortfall |
| title | Tail Risk in Weather Derivatives |
| title_full | Tail Risk in Weather Derivatives |
| title_fullStr | Tail Risk in Weather Derivatives |
| title_full_unstemmed | Tail Risk in Weather Derivatives |
| title_short | Tail Risk in Weather Derivatives |
| title_sort | tail risk in weather derivatives |
| topic | heating degree day cooling degree day extreme value theory vine copula value-at-risk expected shortfall |
| url | https://www.mdpi.com/2813-2432/4/2/11 |
| work_keys_str_mv | AT tuoyuancheng tailriskinweatherderivatives AT saikiranreddyporeddy tailriskinweatherderivatives AT kanchen tailriskinweatherderivatives |