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A regime switching model for temperature modeling and applications to weather derivatives pricing

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Abstract

In this study, we propose a regime-switching model for temperature dynamics, where the parameters depend on a Markov chain. We improve upon the traditional models by modeling jumps in temperature dynamics via the chain itself. Moreover, we compare the performance of the proposed model with the existing models. The results indicate that the proposed model outperforms in the short time forecast horizon while the forecast performance of the proposed model is in line with the existing models for the long time horizon. It is shown that the proposed model is a relatively better representation of temperature dynamics compared to the existing models. Furthermore, we derive prices of weather derivatives written on several temperature indices.

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References

  1. Alaton, P., Djehiche, B., Stillberger, D.: On modelling and pricing weather derivatives. Appl. Math. Finance 9(1), 1–20 (2002)

    Article  MATH  Google Scholar 

  2. Alexandridis, A.K., Zapranis, A.D.: The weather derivatives market. In: Weather Derivatives, pp. 1–20. Springer, New York, NY (2013). https://doi.org/10.1007/978-1-4614-6071-8_1

    MATH  Google Scholar 

  3. Benth, F.E., Šaltytė Benth, J.: Stochastic modelling of temperature variations with a view towards weather derivatives. Appl. Math. Finance 12(1), 53–85 (2005)

    Article  MATH  Google Scholar 

  4. Benth, F.E., Šaltytė Benth, J.: The volatility of temperature and pricing of weather derivatives. Quant. Finance 7(5), 553–561 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  5. Benth, F.E., Šaltytė Benth, J.: Weather derivatives and stochastic modelling of temperature. Int. J. Stoch. Anal. (2011). https://doi.org/10.1155/2011/576791

    Article  MathSciNet  MATH  Google Scholar 

  6. Benth, F.E., Šaltytė Benth, J.: Modeling and Pricing in Financial Markets for Weather Derivatives, vol. 17. World Scientific, Singapore (2013)

    MATH  Google Scholar 

  7. Benth, F.E., Šaltytė Benth, J., Koekebakker, S.: Putting a price on temperature. Scand. J. Stat. 34(4), 746–767 (2007)

    MathSciNet  MATH  Google Scholar 

  8. Bo, L., Capponi, A.: Optimal investment under information driven contagious distress. SIAM J. Control Optim. 55(2), 1020–1068 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  9. Brody, D.C., Syroka, J., Zervos, M.: Dynamical pricing of weather derivatives. Quant. Finance 2(3), 189–198 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  10. Buffington, J., Elliott, R.J.: American options with regime switching. Int. J. Theor. Appl. Finance 5(05), 497–514 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  11. Cappé, O., Moulines, E., Rydén, T.: Inference in Hidden Markov Models. Springer, New York (2005)

    Book  MATH  Google Scholar 

  12. Carr, P., Madan, D.: Option valuation using the fast Fourier transform. J. Comput. Finance 2(4), 61–73 (1999)

    Article  Google Scholar 

  13. CME: Weather products, http://www.cmegroup.com/trading/weather/ (2016). Accessed July 2016

  14. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B (Methodol.) 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  15. Diebold, F.X.: Comparing predictive accuracy, twenty years later: a personal perspective on the use and abuse of Diebold–Mariano tests. J. Bus. Econ. Stat. 33(1), 1–1 (2015)

    Article  MathSciNet  Google Scholar 

  16. Diebold, F.X., Mariano, R.S.: Comparing predictive accuracy. J. Bus. Econ. Stat. 20, 134–144 (2012)

    Article  MathSciNet  Google Scholar 

  17. Dornier, F., Queruel, M.: Caution to the wind. Energy Power Risk Manag. 13(8), 30–32 (2000)

    Google Scholar 

  18. Dufour, F., Elliott, R.: Filtering with discrete state observations. Appl. Math. Optim. 40(2), 259–272 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  19. Elias, R., Wahab, M., Fang, L.: A comparison of regime-switching temperature modeling approaches for applications in weather derivatives. Eur. J. Oper. Res. 232(3), 549–560 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  20. Elliott, R.J., Aggoun, L., Moore, J.B.: Hidden Markov Models: Estimation and Control, vol. 29. Springer, Berlin (1995)

    MATH  Google Scholar 

  21. Enders, W., Doan, T.: Estimating a threshold autoregression. In: RATS programming manual 2nd Edition. pp. 159–169 (2014)

  22. Folland, G.: Real Analysis: Modern Techniques and Their Applications Pure and Applied Mathematics. Wiley, New York (1984)

    MATH  Google Scholar 

  23. Franses, P.H., Van Dijk, D.: Non-linear Time Series Models in Empirical Finance. Cambridge University Press, Cambridge (2000)

    Book  Google Scholar 

  24. Glasserman, P.: Monte Carlo Methods in Financial Engineering, vol. 53. Springer, Berlin (2003)

    Book  MATH  Google Scholar 

  25. Guo, X.: Information and option pricings. Quant. Finance 1(1), 38–44 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  26. Hamilton, J.D.: A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57(2), 357–384 (1989). https://doi.org/10.2307/1912559

    Article  MathSciNet  MATH  Google Scholar 

  27. Hamilton, J.D.: Analysis of time series subject to changes in regime. J. Econom. 45(1), 39–70 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  28. Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)

    Article  Google Scholar 

  29. Janczura, J., Weron, R.: http://ideas.repec.org/s/wuu/hscode.html (2011). Accessed 21 Oct 2015

  30. Janczura, J., Weron, R.: Efficient estimation of Markov regime-switching models: an application to electricity spot prices. AStA Adv. Stat. Anal. 96(3), 385–407 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  31. Kallsen, J., Shiryaev, A.N.: The cumulant process and Esscher’s change of measure. Finance Stoch. 6(4), 397–428 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  32. Kim, C.J.: Dynamic linear models with Markov-switching. J. Econom. 60(1), 1–22 (1994). https://doi.org/10.1016/0304-4076(94)90036-1

    Article  MathSciNet  MATH  Google Scholar 

  33. Mamon, R.S., Elliott, R.J.: Hidden Markov Models in Finance, vol. 104. Springer, Berlin (2007)

    MATH  Google Scholar 

  34. Mamon, R.S., Elliott, R.J.: Hidden Markov Models in Finance. Springer, Berlin (2014)

    Book  MATH  Google Scholar 

  35. Mraoua, M., Bari, D.: Temperature stochastic modeling and weather derivatives pricing: empirical study with Moroccan data. Afr. Stat. 2(1), 22–43 (2007)

    MathSciNet  MATH  Google Scholar 

  36. Naik, V.: Option valuation and hedging strategies with jumps in the volatility of asset returns. J. Finance 48(5), 1969–1984 (1993)

    Article  Google Scholar 

  37. NCDC: National climatic data center, http://www.ncdc.noaa.gov/oa/ncdc.html (2014). Accessed 15 April 2014

  38. Nelson, C.R., Kim, C.J.: State-Space Models with Regime-Switching. MIT Press, Cambridge (1999)

    Google Scholar 

  39. Shen, Y., Fan, K., Siu, T.K.: Option valuation under a double regime-switching model. J. Futures Mark 34(5), 451–478 (2014)

    Article  Google Scholar 

  40. Sotomayor, L.R., Cadenillas, A.: Explicit solutions of consumption-investment problems in financial markets with regime switching. Math. Finance Int. J. Math. Stat. Financ. Econ. 19(2), 251–279 (2009)

    MathSciNet  MATH  Google Scholar 

  41. Swishchuk, A., Cui, K.: Weather derivatives with applications to Canadian data. J. Math. Finance 3, 81–95 (2013)

    Article  Google Scholar 

  42. Tong, H.: Threshold models. In: Threshold Models in Non-linear Time Series Analysis. Lecture Notes in Statistics, vol 21. Springer, New York, NY Springer (1983). https://doi.org/10.1007/978-1-4684-7888-4_3

    Google Scholar 

  43. Zapranis, A., Alexandridis, A.: Modelling the temperature time-dependent speed of mean reversion in the context of weather derivatives pricing. Appl. Math. Finance 15(4), 355–386 (2008)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

We would like to thank the reviewers for useful comments and suggestions.

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Correspondence to Aysun Türkvatan.

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Appendix

Appendix

Threshold Autoregressive (TAR) model. Consider the 2-state version of the threshold autoregressive (TAR) model proposed by [42] given by

$$\begin{aligned} y_{t} = (1-I_{t})(\alpha _{1}+ \phi _{1}y_{t-1}) + I_{t}(\alpha _{2}+ \phi _{2}y_{t-1}) + \epsilon _{t}, \end{aligned}$$

with

$$\begin{aligned} I_{t}={\left\{ \begin{array}{ll}1,\quad \text {if} \ y_{t-1}\ge \tau ,\\ 0, \quad \text {if} \ y_{t-1}< \tau , \end{array}\right. } \end{aligned}$$

where \( \tau \) is the value of the threshold, see [21]. The estimation results of the 2-state TAR model for the deseasonalized temperature process are presented in Table 18, where the standard errors are given in parenthesis. Figure 16 shows the deseasonalized temperature data together with the TAR model threshold obtained from the estimation of the TAR model.

Table 18 Estimation of the TAR model for the deseasonalized temperature process
Fig. 16
figure 16

The TAR model for the deseasonalized temperature data together with the TAR model threshold

Proof of Lemma 2. Remember that, by Eq. (16), under \( \mathbb {P}, \)

$$\begin{aligned} dY_{t}=\kappa \left( Y_{t}-S_{t}\right) dt+\sigma _{t}dW_{t}+\sum _{j=1}^{N}\beta ^{j}_{t}d\mathcal {N}^{j}_{t}. \end{aligned}$$

By Lemma 1,

$$\begin{aligned} dW^{\theta }_{t} = dW_{t}-\theta _{t}\sigma _{t}dt \end{aligned}$$
(57)

and

$$\begin{aligned} d\mathcal {M}^{\theta j}_{t} = d\mathcal {N}^{j}_{t}- e^{\theta _{t}\beta _{t}^{j}}a_{t}^{j}dt. \end{aligned}$$
(58)

Hence, under \( \mathbb {Q}^{\theta } \) we can write

$$\begin{aligned} dY_{t}&=\kappa \left( Y_{t}-S_{t}\right) dt + \left( \theta _{t}\sigma _{t}^{2} + \sum _{j=1}^{N}e^{\theta _{t}\beta _{t}^{j}}\beta ^{j}_{t}a_{t}^{j}\right) dt +\sigma _{t}dW^{\theta }_{t} +\sum _{j=1}^{N}\beta ^{j}_{t}d\mathcal {M}^{\theta j}_{t} \\&=\kappa \left( Y_{t}-S_{t}\right) dt + R_{t}dt +\sigma _{t}dW^{\theta }_{t} +\sum _{j=1}^{N}\beta ^{j}_{t}d\mathcal {M}^{\theta j}_{t}, \end{aligned}$$

and by applying Itô’s Formula, the result follows. \(\square \)

Proof of Lemma 3. By Eq. (13), we have

$$\begin{aligned} \int _{\tau _{1}}^{\tau _{2}}T_{t}dt = \int _{\tau _{1}}^{\tau _{2}} \varLambda _{u}du + \int _{\tau _{1}}^{\tau _{2}} Y_{u}du. \end{aligned}$$
(59)

From Eq. (46), we can write

$$\begin{aligned} Y_{\tau _{2}}&=Y_{\tau _{1}} + \kappa \int _{\tau _{1}}^{\tau _{2}}Y_{u}du - \kappa \int _{\tau _{1}}^{\tau _{2}}S_{u}du + \int _{\tau _{1}}^{\tau _{2}}R_{u}du \\&\quad + \int _{\tau _{1}}^{\tau _{2}} \sigma _{u}dW^{\theta }_{u} + \int _{\tau _{1}}^{\tau _{2}} \sum _{j=1}^{N} \beta ^{j}_{u}d\mathcal {M}^{\theta j}_{u} \end{aligned}$$

and, thus,

$$\begin{aligned} \int _{\tau _{1}}^{\tau _{2}}Y_{u}du&=\kappa ^{-1}\left( Y_{\tau _{2}}-Y_{\tau _{1}}\right) + \int _{\tau _{1}}^{\tau _{2}}S_{u}du -\kappa ^{-1} \int _{\tau _{1}}^{\tau _{2}}R_{u}du \nonumber \\&\quad -\kappa ^{-1} \int _{\tau _{1}}^{\tau _{2}} \sigma _{u}dW^{\theta }_{u} -\kappa ^{-1}\int _{\tau _{1}}^{\tau _{2}} \sum _{j=1}^{N} \beta ^{j}_{u}d\mathcal {M}^{\theta j}_{u}. \end{aligned}$$
(60)

Now, by Eq. (47), we have

$$\begin{aligned} \kappa ^{-1}\left( Y_{\tau _{2}}-Y_{\tau _{1}}\right)&= \kappa ^{-1}\left( e^{\kappa (\tau _{2}-s)}-e^{\kappa (\tau _{1}-s)}\right) {Y}_{s} \nonumber \\&\quad - \int _s^{\tau _{2}} e^{\kappa (\tau _{2}-u)}S_{u}du + \int _s^{\tau _{1}} e^{\kappa (\tau _{1}-u)}S_{u}du \nonumber \\&\quad +\kappa ^{-1} \int _s^{\tau _{2}} e^{\kappa (\tau _{2}-u)}R_{u}du - \kappa ^{-1} \int _s^{\tau _{1}}e^{\kappa (\tau _{1}-u)} R_{u}du \nonumber \\&\quad + \kappa ^{-1} \int _s^{\tau _{2}}e^{\kappa (\tau _{2}-u)}\sigma _{u}dW^{\theta }_{u} - \kappa ^{-1} \int _s^{\tau _{1}}e^{\kappa (\tau _{1}-u)}\sigma _{u} dW^{\theta }_{u} \nonumber \\&\quad + \kappa ^{-1} \int _s^{\tau _{2}}e^{\kappa (\tau _{2}-u)}\sum _{j=1}^{N} \beta ^{j}_{u}d\mathcal {M}^{\theta j}_{u} - \kappa ^{-1} \int _s^{\tau _{1}} e^{\kappa (\tau _{1}-u)}\sum _{j=1}^{N} \beta ^{j}_{u}d\mathcal {M}^{\theta j}_{u}. \end{aligned}$$
(61)

Hence, by Eq. (61), we can write Eq. (60) as

$$\begin{aligned} \int _{\tau _{1}}^{\tau _{2}}Y_{u}du&= \kappa ^{-1}\left( e^{\kappa (\tau _{2}-s)}-e^{\kappa (\tau _{1}-s)}\right) {Y}_{s} \nonumber \\&\quad - \int _s^{\tau _{2}} \left( e^{\kappa (\tau _{2}-u)} - 1\right) S_{u}du + \int _s^{\tau _{1}} \left( e^{\kappa (\tau _{1}-u)} - 1\right) S_{u}du \nonumber \\&\quad +\kappa ^{-1} \int _s^{\tau _{2}} \left( e^{\kappa (\tau _{2}-u)} - 1\right) R_{u}du - \kappa ^{-1} \int _s^{\tau _{1}} \left( e^{\kappa (\tau _{1}-u)} - 1\right) R_{u}du \nonumber \\&\quad + \kappa ^{-1} \int _s^{\tau _{2}} \left( e^{\kappa (\tau _{2}-u)} - 1\right) \sigma _{u}dW^{\theta }_{u} - \kappa ^{-1} \int _s^{\tau _{1}} \left( e^{\kappa (\tau _{1}-u)} - 1\right) \sigma _{u} dW^{\theta }_{u} \nonumber \\&\quad + \kappa ^{-1} \int _s^{\tau _{2}} \left( e^{\kappa (\tau _{2}-u)} - 1\right) \sum _{j=1}^{N} \beta ^{j}_{u}d\mathcal {M}^{\theta j}_{u} \nonumber \\&\quad - \kappa ^{-1} \int _s^{\tau _{1}} \left( e^{\kappa (\tau _{1}-u)} - 1\right) \sum _{j=1}^{N} \beta ^{j}_{u}d\mathcal {M}^{\theta j}_{u}. \end{aligned}$$
(62)

Therefore, by putting Eq. (62) into Eq. (59), the result follows. \(\square \)

Proof of Theorem 1. Remember that by Eq. (4)

$$\begin{aligned} F_{\mathrm{CAT}}(s,\tau _{1},\tau _{2}; T)= \mathbb {E}^{\theta } \left[ \int _{\tau _{1}}^{\tau _{2}} T_{t}dt | \mathcal {G}_{s}\right] . \end{aligned}$$
(63)

By Eq. (48) and Fubini’s Theorem, we have

$$\begin{aligned} \mathbb {E}^{\theta } \left[ \int _{\tau _{1}}^{\tau _{2}} T_{t}dt | \mathcal {G}_{s}\right]&= \int _{\tau _{1}}^{\tau _{2}} \varLambda _{u}du + \kappa ^{-1}\left( e^{\kappa (\tau _{2}-s)}-e^{\kappa (\tau _{1}-s)}\right) Y_{s} \nonumber \\&\quad - \int _{s}^{\tau _{2}} \left( e^{\kappa (\tau _{2}-u)} - 1\right) \mathbb {E}^{\theta }\left[ S_{u} | \mathcal {G}_{s}\right] du \nonumber \\&\quad + \int _{s}^{\tau _{1}} \left( e^{\kappa (\tau _{1}-u)} - 1\right) \mathbb {E}^{\theta }\left[ S_{u} | \mathcal {G}_{s}\right] du \nonumber \\&\quad +\kappa ^{-1} \int _{s}^{\tau _{2}} \left( e^{\kappa (\tau _{2}-u)} - 1\right) \mathbb {E}^{\theta }\left[ R_{u} | \mathcal {G}_{s}\right] du \nonumber \\&\quad - \kappa ^{-1} \int _{s}^{\tau _{1}} \left( e^{\kappa (\tau _{1}-u)} - 1\right) \mathbb {E}^{\theta }\left[ R_{u} | \mathcal {G}_{s}\right] du, \end{aligned}$$
(64)

since by the martingale property

$$\begin{aligned}&\mathbb {E}^{\theta } \left[ \int _{s}^{\tau _{2}} \left( e^{\kappa (\tau _{2}-u)} - 1\right) \sigma _{u}dW^{\theta }_{u} | \mathcal {G}_{s}\right] =0,\\&\mathbb {E}^{\theta } \left[ \int _{s}^{\tau _{1}} \left( e^{\kappa (\tau _{1}-u)} - 1\right) \sigma _{u}dW^{\theta }_{u} | \mathcal {G}_{s}\right] =0,\\&\mathbb {E}^{\theta } \left[ \int _{s}^{\tau _{2}} \left( e^{\kappa (\tau _{2}-u)} - 1\right) \sum _{j=1}^{N} \beta ^{j}_{u}d\mathcal {M}^{\theta j}_{u} | \mathcal {G}_{s}\right] =0,\\&\mathbb {E}^{\theta } \left[ \int _{s}^{\tau _{1}} \left( e^{\kappa (\tau _{1}-u)} - 1\right) \sum _{j=1}^{N} \beta ^{j}_{u}d\mathcal {M}^{\theta j}_{u} | \mathcal {G}_{s}\right] =0. \end{aligned}$$

It can be easily shown that

$$\begin{aligned} \mathbb {E}^{\theta }\left[ \zeta _{u}| \mathcal {G}_{s}\right] = e^{\mathbf {A}^{\theta }(u-s)} \zeta _{s}. \end{aligned}$$

Thus, we have

$$\begin{aligned} \mathbb {E}^{\theta }\left[ S_{u} | \mathcal {G}_{s}\right]&= \mathbb {E}^{\theta }\left[ \langle \bar{S}, \zeta _{u} \rangle | \mathcal {G}_{s}\right] \nonumber \\&= \langle \bar{S}, \mathbb {E}^{\theta }\left[ \zeta _{u}| \mathcal {G}_{s}\right] \rangle \nonumber \\&= \langle \bar{S}, e^{\mathbf {A}^{\theta }(u-s)} \zeta _{s} \rangle \end{aligned}$$
(65)

and

$$\begin{aligned} \mathbb {E}^{\theta }\left[ R_{u} | \mathcal {G}_{s}\right]&= \mathbb {E}^{\theta }\left[ \langle \bar{R}, \zeta _{u} \rangle | \mathcal {G}_{s}\right] \nonumber \\&= \langle \bar{R}, \mathbb {E}^{\theta }\left[ \zeta _{u}| \mathcal {G}_{s}\right] \rangle \nonumber \\&= \langle \bar{R}, e^{\mathbf {A}^{\theta }(u-s)} \zeta _{s} \rangle . \end{aligned}$$
(66)

Therefore, by inserting Eqs. (65) and (66) into Eq. (64), the result follows. \(\square \)

Proof of Lemma 4. Let be

$$\begin{aligned} Z_{t} :=e^{-\kappa t}Y_{t} \end{aligned}$$

and apply Itô’s Formula to \( e^{iuZ_{t}}\). Then,

$$\begin{aligned} e^{iuZ_{t}}&= e^{iuZ_{s}} + \int _{s}^{t} e^{iuZ_{r}}\left( iue^{-\kappa r}(-\kappa S_{r}+ R_{r}) - \dfrac{1}{2}u^{2}e^{-2 \kappa r}\sigma _{r}^{2} \right) dr \\&+ \int _{s}^{t} e^{iuZ_{r}} \sum _{j=1}^{N} e^{\theta _{r}\beta _{r}^{j}} (e^{iue^{-\kappa r }\beta _{r}^{j}} -1 -iue^{- \kappa r}\beta _{r}^{j} )a_{r}^{j}dr \\&+ \int _{s}^{t} e^{iuZ_{r}} iue^{- \kappa r} \sigma _{r}dW_{r}^{\theta } + \int _{s}^{t} e^{iuZ_{r-}} \sum _{j=1}^{N} (e^{iue^{-\kappa r }\beta _{r}^{j}} -1 ) d \mathcal {M}^{\theta j}_{r} \\&= e^{iuZ_{s}} + \int _{s}^{t} e^{iuZ_{r}} \langle g(r, u), \zeta _{r}\rangle dr \\&+ \int _{s}^{t} e^{iuZ_{r}} iue^{- \kappa r} \sigma _{r}dW_{r}^{\theta } + \int _{s}^{t} e^{iuZ_{r-}} \sum _{j=1}^{N} (e^{iue^{-\kappa r }\beta _{r}^{j}} -1 ) d \mathcal {M}^{\theta j}_{r}. \end{aligned}$$

We define for each \( s \le t \in [0, \mathsf {T}], \)\( \mathcal {\bar{G}}_{s, t}:= \mathcal {F}_{s}\vee \mathcal {F}^{\zeta }_{t}, \) which represents the enlarged \( \sigma \)-field generated by \( \mathcal {F}_{s} \) and \( \mathcal {F}^{\zeta }_{t}\). Moreover, write \( {\bar{\mathbf {G}}}:= ( \mathcal {\bar{G}}_{s, t}: \ s, t \in [0, \mathsf {T}] ) \) for the corresponding complete enlarged filtration. Let \( \varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (u) \) denote the characteristic function of \( Z_{t} \) conditional on \( \mathcal {\bar{G}}_{s, t}; \) that is,

$$\begin{aligned} \varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (u) := \mathbb {E}^{\theta } \left[ e^{iuZ_{t}} | \mathcal {\bar{G}}_{s, t} \right] . \end{aligned}$$
(67)

Then, from above we obtain

$$\begin{aligned} d\varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (u)= \varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (u) \left( \langle g(t, u), \zeta _{t} \rangle dt + \sum _{j=1}^{N} (e^{iue^{-\kappa t }\beta _{t}^{j}} -1 ) d \mathcal {M}^{\theta j}_{t}\right) . \end{aligned}$$
(68)

For notational convenience, we simply write \( \mathbf {B}^{\theta }(t)=\mathbf {B}^{\theta }(t,u). \) Let \( \mathbf {D}^{\theta }(t) := (d_{jl}^{\theta }(t))_{j,l=1, \ldots , N}, \) where

$$\begin{aligned} d_{jl}^{\theta }(t) = \left\{ \begin{array}{ll} e^{iue^{-\kappa t }\beta _{jl}},&{}\quad \text {for} \ l \ne j, \\ \dfrac{\sum _{j=1, j \ne l}^{N} e^{iue^{-\kappa t }\beta _{jl}}a_{jl}^{\theta }}{\sum _{j=1, j \ne l}^{N} a_{jl}^{\theta }},&{}\quad \text {for} \ l=j. \end{array} \right. \end{aligned}$$

Notice that \( d_{jl}^{\theta }(t) = b_{jl}^{\theta }(t)/a_{jl}^{\theta }, \) for each \( j, l= 1, \ldots , N\). Define \( \mathbf {D}_{0}^{\theta }(t) := \mathbf {D}^{\theta }(t) - diag[d^{\theta }(t)], \) where \( d^{\theta }(t) = (d_{11}^{\theta }(t), \ldots , d_{NN}^{\theta }(t))' \in \mathbb {R}^{N}\). Then, we can write

$$\begin{aligned} \sum _{j=1}^{N} (e^{iue^{-\kappa t }\beta _{t}^{j}} -1 ) d\mathcal {M}^{\theta j}_{t} = \left( \mathbf {D}_{0}^{\theta }(t) \zeta _{t-} + \zeta _{t-} -\mathbf {1} \right) ' d\mathcal {M}^{\theta }_{t}, \end{aligned}$$
(69)

where \( \mathcal {M}^{\theta }_{t}=(\mathcal {M}^{\theta 1}_{t}, \ldots , \mathcal {M}^{\theta N}_{t})' \in \mathbb {R}^{N}\). It can be easily shown that, by Remark 1, we can write

$$\begin{aligned} \zeta _{t} = \zeta _{0}+\int _{0}^{t} (\mathbf {I}-\zeta _{s-}\mathbf {1}')d\mathcal {N}_{s} \end{aligned}$$

with

$$\begin{aligned} \mathcal {M}^{\theta }_{t} = \mathcal {N}_{t} - \int _{0}^{t} \mathbf {A}^{\theta }_{0}\zeta _{s-}ds. \end{aligned}$$

Therefore, Eq. (68) can be written as

$$\begin{aligned} d\varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (u)= \varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (u) \left( \langle g(t, u), \zeta _{t} \rangle dt + \left( \mathbf {D}_{0}^{\theta }(t) \zeta _{t-} + \zeta _{t-} -\mathbf {1} \right) ' d\mathcal {M}^{\theta }_{t}\right) . \end{aligned}$$
(70)

We define

$$\begin{aligned} h(t, u) := \zeta _{t}\varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (u), \qquad t \in [0, \mathsf {T}]. \end{aligned}$$
(71)

By Itô’s Formula we obtain

$$\begin{aligned} h(t, u)&= h(s, u) + \int _{s}^{t}\varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u)\left( \mathbf {A}^{\theta } \zeta _{r-}dr + dV_{r}^{\theta }\right) \nonumber \\&\quad + \int _{s}^{t}\zeta _{r-}d\varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) + \sum _{s< r \le t } \varDelta \zeta _{r} \varDelta \varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u)\nonumber \\&= h(s, u) + \int _{s}^{t}\left( diag[g(r, u)] + \mathbf {A}^{\theta } \right) h(r, u)dr \nonumber \\&\quad + \int _{s}^{t}\varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) dV_{r}^{\theta } \nonumber \\&\quad + \int _{s}^{t} h(r-, u) \left( \mathbf {D}_{0}^{\theta }(r) \zeta _{r-} + \zeta _{r-} -\mathbf {1} \right) ' d\mathcal {M}^{\theta }_{r} \nonumber \\&\quad + \sum _{s < r \le t } \varDelta \zeta _{r} \varDelta \varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u). \end{aligned}$$
(72)

Here, we used \( \varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (u) \zeta _{t} \langle g(t, u), \zeta _{t} \rangle = diag[ g(t, u)] \zeta _{t}\varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (u)\).

Now, by using

$$\begin{aligned} (\mathbf {I}-\zeta _{t}\mathbf {1}')diag[\varDelta \mathcal {N}_{t}]\zeta _{t}= \mathbf {0}, \end{aligned}$$

we can write

$$\begin{aligned}&\sum _{s< r \le t } \varDelta \zeta _{r} \varDelta \varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) \nonumber \\&= \sum _{s< r \le t} (\mathbf {I}-\zeta _{r-}\mathbf {1}')\varDelta \mathcal {N}_{r}\varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u)\left( \mathbf {D}_{0}^{\theta }(r) \zeta _{r-} + \zeta _{r-} -\mathbf {1} \right) ' \varDelta \mathcal {N}_{r} \nonumber \\&= \sum _{s< r \le t} \varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) (\mathbf {I}-\zeta _{r-}\mathbf {1}')diag[\varDelta \mathcal {N}_{r}]\left( \mathbf {D}_{0}^{\theta }(r) \zeta _{r-} + \zeta _{r-} -\mathbf {1} \right) \nonumber \\&= \sum _{s < r \le t} \varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) (\mathbf {I}-\zeta _{r-}\mathbf {1}')diag[\varDelta \mathcal {N}_{r}]\left( \mathbf {D}_{0}^{\theta }(r) \zeta _{r-} -\mathbf {1} \right) \nonumber \\&= \int _{s}^{t}\varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) (\mathbf {I}-\zeta _{r-}\mathbf {1}')diag[\mathbf {A}_{0}^{\theta }\zeta _{r-}]\left( \mathbf {D}_{0}^{\theta }(r) \zeta _{r-} -\mathbf {1} \right) dr \nonumber \\&\quad + \int _{s}^{t} \varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) (\mathbf {I}-\zeta _{r-}\mathbf {1}')diag[d\mathcal {M}^{\theta }_{r}]\left( \mathbf {D}_{0}^{\theta }(r) \zeta _{r-} -\mathbf {1} \right) . \end{aligned}$$
(73)

It can be easily shown that

$$\begin{aligned} diag[\mathbf {A}_{0}^{\theta }\zeta _{t}]\left( \mathbf {D}_{0}^{\theta }(t) \zeta _{t} -\mathbf {1} \right) = \left( \mathbf {B}^{\theta }_{0}(t) - \mathbf {A}^{\theta }_{0}\right) \zeta _{t}. \end{aligned}$$

Hence, we can write

$$\begin{aligned}&\int _{s}^{t}\varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) (\mathbf {I}-\zeta _{r-}\mathbf {1}')diag[\mathbf {A}_{0}^{\theta }\zeta _{r-}]\left( \mathbf {D}_{0}^{\theta }(r) \zeta _{r-} -\mathbf {1} \right) dr \nonumber \\&\quad = \int _{s}^{t}\varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) (\mathbf {I}-\zeta _{r-}\mathbf {1}')\left( \mathbf {B}^{\theta }_{0}(r) - \mathbf {A}^{\theta }_{0}\right) \zeta _{r-}dr \nonumber \\&\quad = \int _{s}^{t} \left( \mathbf {B}^{\theta }(r)-\mathbf {A}^{\theta }\right) h(r, u)dr, \end{aligned}$$
(74)

since

$$\begin{aligned} (\mathbf {I}-\zeta _{t}\mathbf {1}')\mathbf {B}^{\theta }_{0}(t)\zeta _{t} = \mathbf {B}^{\theta }(t)\zeta _{t} \end{aligned}$$

and

$$\begin{aligned} (\mathbf {I}-\zeta _{t}\mathbf {1}')\mathbf {A}^{\theta }_{0}\zeta _{t}= \mathbf {A}^{\theta }\zeta _{t}. \end{aligned}$$

Thus, by combining Eqs. (73) and (74), we get

$$\begin{aligned}&\sum _{s < r \le t } \varDelta \zeta _{r} \varDelta \varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) \nonumber \\&\quad = \int _{s}^{t} \left( \mathbf {B}^{\theta }(r)-\mathbf {A}^{\theta }\right) h(r, u)dr \nonumber \\&\qquad + \int _{s}^{t} \varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) (\mathbf {I}-\zeta _{r-}\mathbf {1}')diag[d\mathcal {M}^{\theta }_{r}]\left( \mathbf {D}_{0}^{\theta }(r) \zeta _{r-} -\mathbf {1} \right) . \end{aligned}$$
(75)

Thus, by Eqs. (75), (72) becomes

$$\begin{aligned} h(t, u)&= h(s, u) + \int _{s}^{t}\left( diag[g(r, u)] + \mathbf {B}^{\theta }(r) \right) h(r, u)dr \nonumber \\&\quad + \int _{s}^{t}\varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) dV_{r}^{\theta } \nonumber \\&\quad + \int _{s}^{t} h(r-, u) \left( \mathbf {D}_{0}^{\theta }(r) \zeta _{r-} + \zeta _{r-} -\mathbf {1} \right) ' d\mathcal {M}^{\theta }_{r} \nonumber \\&\quad + \int _{s}^{t} \varPhi _{Z_{r}|\mathcal {\bar{G}}_{s, r}} (u) (\mathbf {I}-\zeta _{r-}\mathbf {1}')diag[d\mathcal {M}^{\theta }_{r}]\left( \mathbf {D}_{0}^{\theta }(r) \zeta _{r-} -\mathbf {1} \right) . \end{aligned}$$
(76)

Therefore, by Fubini’s Theorem and using the martingale property, we have

$$\begin{aligned} \mathbb {E}^{\theta } \left[ h(t, u) | \mathcal {G}_{s}\right] = h(s, u) + \int _{s}^{t}\left( diag[g(r, u)] + \mathbf {B}^{\theta }(r) \right) \mathbb {E}^{\theta } \left[ h(r, u)| \mathcal {G}_{s}\right] dr. \end{aligned}$$

Thus, we get

$$\begin{aligned} d\mathbb {E}^{\theta } \left[ h(t, u) | \mathcal {G}_{s}\right] = \left( diag[g(t, u)] + \mathbf {B}^{\theta }(t) \right) \mathbb {E}^{\theta } \left[ h(t, u) | \mathcal {G}_{s}\right] dt \end{aligned}$$

and after solving we have

$$\begin{aligned} \mathbb {E}^{\theta } \left[ h(t, u) | \mathcal {G}_{s}\right] = e^{iuZ_{s}}\zeta _{s}\exp \left( \int _{s}^{t}\left( diag[g(r, u)] + \mathbf {B}^{\theta }(r) \right) dr\right) . \end{aligned}$$
(77)

Notice that

$$\begin{aligned} \varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (u)&= \langle \zeta _{t}\varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (u), \mathbf {1} \rangle \nonumber \\&= \langle h(t, u), \mathbf {1} \rangle , \end{aligned}$$
(78)

since \( \langle \zeta _{t}, \mathbf {1} \rangle = 1\).

Now, by the tower property and Eq. (78), we have

$$\begin{aligned} \mathbb {E}^{\theta } \left[ e^{iuY_{t}} | \mathcal {G}_{s}\right]&= \mathbb {E}^{\theta } \left[ \mathbb {E}^{\theta } \left[ e^{iuY_{t}} | \mathcal {\bar{G}}_{s, t}\right] | \mathcal {G}_{s}\right] \\&= \mathbb {E}^{\theta } \left[ \mathbb {E}^{\theta } \left[ e^{iue^{kt}Z_{t}} | \mathcal {\bar{G}}_{s, t}\right] | \mathcal {G}_{s}\right] \\&= \mathbb {E}^{\theta } \left[ \varPhi _{Z_{t}|\mathcal {\bar{G}}_{s, t}} (ue^{\kappa t}) | \mathcal {G}_{s}\right] \\&= \mathbb {E}^{\theta } \left[ \langle h(t, ue^{\kappa t}), \mathbf {1} \rangle | \mathcal {G}_{s}\right] \\&= \langle \mathbb {E}^{\theta } \left[ h(t, ue^{\kappa t}) | \mathcal {G}_{s}\right] , \mathbf {1} \rangle . \end{aligned}$$

Therefore the result follows by Eq. (77). \(\square \)

Proof of Theorem 2. Remember that by Eq. (5)

$$\begin{aligned} F_{\mathrm{CDD}}(s,\tau _{1},\tau _{2}; T) = \mathbb {E}^{\theta } \left[ \int _{\tau _{1}}^{\tau _{2}}\max ( T_{t}-c, 0)dt | \mathcal {G}_{s}\right] . \end{aligned}$$

Now by Lemma 5, we can write

$$\begin{aligned} \max ( T_{t}-c, 0) = \dfrac{1}{2 \pi } \int _{\mathbb {R}} \hat{f}_{\epsilon }(u) \exp \left( (\epsilon + iu)T_{t}\right) du. \end{aligned}$$
(79)

Thus, by Eq. (79) and Fubini’s Theorem,

$$\begin{aligned}&\mathbb {E}^{{\theta }} \left[ \max ( T_{t}-c, 0) | \mathcal {G}_{s}\right] \nonumber \\&\quad = \dfrac{1}{2 \pi } \int _{\mathbb {R}} \hat{f}_{\epsilon }(u) \mathbb {E}^{{\theta }} \left[ \exp \left( (\epsilon + iu) T_{t} \right) | \mathcal {G}_{s}\right] du \nonumber \\&\quad = \dfrac{1}{2 \pi } \int _{\mathbb {R}} \hat{f}_{\epsilon }(u) \exp \left( (\epsilon + iu)\varLambda _{t}\right) \mathbb {E}^{{\theta }} \left[ \exp \left( (\epsilon + iu) Y_{t}\right) | \mathcal {G}_{s}\right] du \nonumber \\&\quad = \dfrac{1}{2 \pi } \int _{\mathbb {R}} \hat{f}_{\epsilon }(u) \exp \left( (\epsilon + iu)\varLambda _{t}\right) \mathbb {E}^{{\theta }} \left[ \exp \left( i (u - i\epsilon ) Y_{t}\right) | \mathcal {G}_{s}\right] du \nonumber \\&\quad = \dfrac{1}{2 \pi } \int _{\mathbb {R}} \hat{f}_{\epsilon }(u) \exp \left( (\epsilon + iu)\varLambda _{t}\right) \varPhi _{Y_{t}|\mathcal {G}_{s}} (u - i\epsilon ) du, \end{aligned}$$
(80)

where \( \varPhi _{Y_{t}|\mathcal {G}_{s}} (u) = \mathbb {E}^{\theta } \left[ e^{iuY_{t}} | \mathcal {G}_{s}\right] \) is given by Eq. (54).

Therefore, by Fubini’s Theorem and Eq. (80), we obtain

$$\begin{aligned}&\mathbb {E}^{\theta } \left[ \int _{\tau _{1}}^{\tau _{2}}\max ( T_{t}-c, 0)dt | \mathcal {G}_{s}\right] \\&\quad = \int _{\tau _{1}}^{\tau _{2}}\mathbb {E}^{{\theta }} \left[ \max ( T_{t}-c, 0) | \mathcal {G}_{s}\right] dt \\&\quad = \int _{\tau _{1}}^{\tau _{2}}\dfrac{1}{2 \pi } \int _{\mathbb {R}} \hat{f}_{\epsilon }(u) \exp \left( (\epsilon + iu) \varLambda _{t}\right) \varPhi _{Y_{t}|\mathcal {G}_{s}} (u - i\epsilon )dudt \\&\quad = \dfrac{1}{2 \pi } \int _{\mathbb {R}} \hat{f}_{\epsilon }(u) \int _{\tau _{1}}^{\tau _{2}}\exp \left( (\epsilon + iu) \varLambda _{t}\right) \varPhi _{Y_{t}|\mathcal {G}_{s}} (u - i\epsilon )dtdu. \end{aligned}$$

We notice that, by Lemma 4,

$$\begin{aligned}&\varPhi _{Y_{t}|\mathcal {G}_{s}} (u-i\epsilon ) \\&\quad = \left\langle \zeta _{s}\exp \left( (\epsilon + iu) e^{\kappa (t-s)}Y_{s} + \int _{s}^{t} \left( diag [g(r, (u-i\epsilon )e^{kt})] \right. \right. \right. \\&\qquad \left. \left. \left. +\, \mathbf {B}^{\theta }(r,(u-i\epsilon )e^{kt}) \right) dr \right) , \mathbf {1} \right\rangle . \end{aligned}$$

Therefore, the result follows. \(\square \)

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Türkvatan, A., Hayfavi, A. & Omay, T. A regime switching model for temperature modeling and applications to weather derivatives pricing. Math Finan Econ 14, 1–42 (2020). https://doi.org/10.1007/s11579-019-00242-0

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