On Modelling and Pricing Rainfall Derivatives with Seasonality

Leobacher, Gunther; Ngare, Philip

doi:10.1080/13504861003795167

Cited by 39 publications

(35 citation statements)

References 4 publications

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“…Weather derivatives are financial methods used to hedge risks caused by bad weather condition or weather fluctuation. It states how payment will be settled between the parties involved based on the existing weather conditions during the contract period [31]. Commonly weather derivatives are swaps, futures and options based on different weather measures [2].…”

Section: Introductionmentioning

confidence: 99%

Pricing weather derivative using Markov Chain Analogue Year daily rainfall model

2020

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In this paper, a Markov Chain Analogue Year model is used to describe the occurrence process of daily rainfall and the amount of daily rainfall on wet days is presented using Gamma, Exponential and mixed Exponential distributions. Combining the occurrence and the amount process models together, we developed Markov Chain Analogue Year Gamma model, Markov Chain Analogue Year Exponential model and Markov Chain Analogue Year Mixed Exponential model. The result shows that all the three models perform very well in the simulation of precipitation process in the study area and their performances are nearly the same with a slight difference. Using the results of these models, option prices for Teff and wheat crops for different months in their growing season are calculated. The calculated option price has an excellent accuracy with maximum absolute error of 0.54.instance rainfall fluctuation alone could contribute a loss of 2 billion USD in the sector [6]. In general, agriculture takes the first rank in the Ethiopian economy and it is extremely dependent on natural rainfall [4,51]. Therefore, modeling and pricing rainfall derivative is very important for the country in order to reduce such risks. This paper has two main objectives, the first objective is to present a daily rainfall model and the second objective is to use the daily rainfall model to obtain rainfall index, that is, cumulative rainfall (CR) and based on the CR, we calculate an option price for rainfall derivatives (for Teff and wheat crops). The paper is structured as follows: in Sect. 2 we have discussed materials and methods, model formulation, major crops and their response to water stress and description of pricing formula for rainfall derivatives, in Sect. 3 the obtained results are discussed under result and discussion, and finally in Sect. 4 we have made some concluding remarks.

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Section: Introductionmentioning

confidence: 99%

Pricing weather derivative using Markov Chain Analogue Year daily rainfall model

2020

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“…Simulating daily precipitation data is an important task with a wide range of applications, e.g., agriculture Williamson, 1990a, 1990b), ecology (Kittel et al ., 1995), hydrological systems (Pickering et al ., 1988) and finance (Leobacher and Ngare, 2011). Traditionally, the simulation has been governed by two independent stochastic models; one for precipitation occurrence and the other for daily precipitation amount.…”

Section: Introductionmentioning

confidence: 99%

Stochastic simulation of daily precipitation: A copula approach

Choi¹,

2014

Journal of the Korean Data and Information Science Society

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The traditional methods of simulating daily precipitation have paid little attention to the inherent dependence structure between the total precipitation amount and the precipitation frequency for a fixed period of time. To address this issue, we propose a new simulation algorithm using copula in order to incorporate the dependence into the traditional methods. The algorithm consists of two parts: First, while reflecting the observed dependence, we generate the total precipitation amount (S) and the frequency (N ) during the period of interest; then we simulate the daily precipitation whose aggregation matches the pair of (N, S) generated in the first part. Our result shows that the proposed method substantially improves the traditional methods.

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“…The arbitrage free approach on the other hand is a method to change the measure of the underlying asset using the Esscher transform, taking the user from the real world to the risk neutral world through a probabilistic shift. Prior to contracts trading on the Chicago Mercantile Exchange (CME), indifference pricing was the initial technique [6,13], since contracts began trading in 2010, the arbitrage free approach has now become the standard pricing technique [5,17,15]. This paper derives contract prices for U.S.A. cities using the most recent pricing technique of the arbitrage free approach.…”

Section: Introductionmentioning

confidence: 99%

Pricing Rainfall Based Futures Using Genetic Programming

Cramer

Kampouridis

Freitas

et al. 2017

Applications of Evolutionary Computation

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Abstract. Rainfall derivatives are in their infancy since starting trading on the Chicago Mercentile Exchange (CME) since 2011. Being a relatively new class of financial instruments there is no generally recognised pricing framework used within the literature. In this paper, we propose a novel framework for pricing contracts using Genetic Programming (GP). Our novel framework requires generating a risk-neutral density of our rainfall predictions generated by GP supported by Markov chain Monte Carlo and Esscher transform. Moreover, instead of having a single rainfall model for all contracts, we propose having a separate rainfall model for each contract. We compare our novel framework with and without our proposed contract-specific models for pricing against the pricing performance of the two most commonly used methods, namely Markov chain extended with rainfall prediction (MCRP), and burn analysis (BA) across contracts available on the CME. Our goal is twofold, (i) to show that by improving the predictive accuracy of the rainfall process, the accuracy of pricing also increases. (ii) contract-specific models can further improve the pricing accuracy. Results show that both of the above goals are met, as GP is capable of pricing rainfall futures contracts closer to the CME than MCRP and BA. This shows that our novel framework for using GP is successful, which is a significant step forward in pricing rainfall derivatives.

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On Modelling and Pricing Rainfall Derivatives with Seasonality

Cited by 39 publications

References 4 publications

Pricing weather derivative using Markov Chain Analogue Year daily rainfall model

Pricing weather derivative using Markov Chain Analogue Year daily rainfall model

Stochastic simulation of daily precipitation: A copula approach

Pricing Rainfall Based Futures Using Genetic Programming

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