Modeling and Forecasting Average Temperature for Weather Derivative Pricing

Zhiliang Wang,Lingyong Li,Peng Li,Min Liu,Chunyan Huang

doi:10.1155/2015/837293

Abstract

The main purpose of this paper is to present a feasible model for the daily average temperature on the area of Zhengzhou and apply it to weather derivatives pricing. We start by exploring the background of weather derivatives market and then use the 62 years of daily historical data to apply the mean-reverting Ornstein-Uhlenbeck process to describe the evolution of the temperature. Finally, Monte Carlo simulations are used to price heating degree day (HDD) call option for this city, and the slow convergence of the price of the HDD call can be found through taking 100,000 simulations. The methods of the research will provide a frame work for modeling temperature and pricing weather derivatives in other similar places in China.

Highlights

Weather derivative is a new risk management tool which can be widely used in the financial market to avoid the impact of bad weather effects and control the weather risks
Weather derivatives are different from traditional financial derivatives as their underlying asset such as temperature, humidity, and precipitation, which cannot be traded in the market, so ordinary pricing models such as Black and Scholes formula is not applicable in pricing weather derivatives
For the sake of clarity, we focus our analysis on derivative products whose underlying is the level of cumulated daily temperatures over a given period, the heating degree day (HDD) and cooling degree days (CDDs)

Summary

Introduction

Weather derivative is a new risk management tool which can be widely used in the financial market to avoid the impact of bad weather effects and control the weather risks. Dischel [15] argued that the classical Black-Scholes-Merton pricing approach cannot be directly applied in weather derivatives pricing He is the first person who proposed a continuous stochastic model in temperature forecasting. Goncu [20] proposes a seasonal volatility model that estimates daily average temperatures of Beijing, Shanghai and Shenzhen using the mean-reverting Ornstein-Uhlenbeck process and derive analytical approximation formulas for the sensitivities of these contracts. Their results verify the convergence of the Monte Carlo and approximation estimators.

Basic Concepts of Weather Derivative

Modeling Temperature

Option Pricing with Monte Carlo Simulation

Conclusions