### Abstract

A State Price Density (SPD) is the density function of a risk neutral equivalent martingale measure for option pricing, and is indispensable for exotic option pricing and portfolio risk management. Many approaches have been proposed in the last two decades to calibrate a SPD using financial options from the bond and equity markets. Among these, non and semiparametric methods were preferred because they can avoid model mis-specification of the underlying. However, these methods usually require a large data set to achieve desired convergence properties. One faces the problem in estimation by e.g., kernel techniques that there are not enough observations locally available. For this situation, we employ a Bayesian quadrature method because it allows us to incorporate prior assumptions on the model parameters and hence avoids problems with data sparsity. It is able to compute the SPD of both call and put options simultaneously, and is particularly robust when the market faces the data sparsity issue. As illustration, we calibrate the SPD for weather derivatives, a classical example of incomplete markets with financial contracts payoffs linked to non-tradable assets, namely, weather indices. Finally, we study related weather derivatives data and the dynamics of the implied SPDs.

Original language | English |
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Pages (from-to) | 106-125 |

Number of pages | 20 |

Journal | Insurance: Mathematics and Economics |

Volume | 64 |

DOIs | |

State | Published - 1 Sep 2015 |

### Keywords

- Bayesian
- CDD
- Data sparsity
- HDD
- Quadrature
- State Price Density
- Temperature derivatives
- Weather derivatives

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## Cite this

*Insurance: Mathematics and Economics*,

*64*, 106-125. https://doi.org/10.1016/j.insmatheco.2015.05.001