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Localizing Temperature Risk

Author

Listed:
  • Wolfgang Karl Härdle
  • Brenda López Cabrera
  • Ostap Okhrin
  • Weining Wang
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.

Suggested Citation

  • Wolfgang Karl Härdle & Brenda López Cabrera & Ostap Okhrin & Weining Wang, 2016. "Localizing Temperature Risk," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1491-1508, October.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:516:p:1491-1508
    DOI: 10.1080/01621459.2016.1180985
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    References listed on IDEAS

    as
    1. Chen, Ying & Härdle, Wolfgang Karl & Pigorsch, Uta, 2010. "Localized Realized Volatility Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1376-1393.
    2. Ulrich Horst & Matthias Müller, 2007. "On the Spanning Property of Risk Bonds Priced by Equilibrium," Mathematics of Operations Research, INFORMS, vol. 32(4), pages 784-807, November.
    3. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    4. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    5. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    6. Wolfgang Karl Härdle & Brenda López Cabrera, 2012. "The Implied Market Price of Weather Risk," Applied Mathematical Finance, Taylor & Francis Journals, vol. 19(1), pages 59-95, February.
    7. P. Čížek & W. Härdle & V. Spokoiny, 2009. "Adaptive pointwise estimation in time-inhomogeneous conditional heteroscedasticity models," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 248-271, July.
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    More about this item

    JEL classification:

    • G19 - Financial Economics - - General Financial Markets - - - Other
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • N23 - Economic History - - Financial Markets and Institutions - - - Europe: Pre-1913
    • N53 - Economic History - - Agriculture, Natural Resources, Environment and Extractive Industries - - - Europe: Pre-1913
    • Q59 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Other

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