Search results

The purpose of this paper is twofold. First, the author proposes a financial engineering framework to model commodity prices based on market demand processes and demand functions. This framework explains the relation between demand, volatility and the leverage effect of commodities. It is also shown how the proposed framework can be used to price derivatives on commodity prices. Second, the author estimates the model parameters for agricultural commodities and discuss the implications of the results on derivative prices. In particular, the author see how leverage effect (or inverse leverage effect) is related to market demand.

This paper uses a power demand function along with the Cox, Ingersoll and Ross mean-reverting process to find the price process of commodities. Then by using the Ito theorem the constant elastic volatility (CEV) model is derived for the market prices. The partial differential equation that the dynamics of derivative prices satisfy is found and, by the Feynman-Kac theorem, the market derivative prices are provided within a Monte-Carlo simulation framework. Finally, by using a maximum likelihood estimator, the parameters of the CEV model for the agricultural commodity prices are found.

The results of this paper show that derivative prices on commodities are heavily affected by the elasticity of volatility and, consequently, by market demand elasticity. The empirical results show that different groups of agricultural commodities have different values of demand and volatility elasticity.

The results of this paper can be used by practitioners to price derivatives on commodity prices and by insurance companies to better price insurance contracts. As in many countries agricultural insurances are subsidised by the government, the results of this paper are useful for setting more efficient policies.

Approaches that use the methodology of financial engineering to model agricultural prices and compute the derivative prices are rather new within the literature and still need to be developed for further applications.

The purpose of this paper is to introduce a continuous time version of the speculative storage model of Deaton and Laroque (1992) and to use for pricing derivatives, in particular insurances on agricultural prices.

The methodology of financial engineering is used in order to find the partial differential equations that the dynamics of derivative prices have to satisfy. Furthermore, by using the Monte-Carlo method (and Feynman-Kac theorem) the insurance prices is computed.

Results of this paper show that insurance prices (and derivative prices in general) are heavily influenced by market structure, in particular, the demand function specifications. Furthermore, through an empirical analysis, the performance of the continuous time speculative storage model is compared with the geometric Brownian motion model. It is shown that the speculative storage model outperforms the actual data.

Since the agricultural insurances in many countries are subsidised by government, the results of this paper can be used by policy makers to measure changes in agricultural insurance premiums in scenarios that market experiences changes in demand. In the same manner, insurance companies and investors can use the results of this paper to better price agricultural derivatives.

The issue of agricultural insurance pricing (in general derivative pricing) is of great concern to policy makers, investors and insurance companies. To the author’s knowledge, an approach which uses the methodology of financial engineering to compute the insurance prices (in general derivatives) is new within the literature.

The purpose of this paper is to argue that a stationary-differenced autoregressive (AR) process with lag greater than 1, AR(q > 1), has certain properties that are consistent with a fractional Brownian motion (fBm). What the authors are interested in is the investigation of approaches to identifying the existence of persistent memory of one form or another for the purposes of simulating commodity (and other asset) prices. The authors show in theory, and with application to agricultural commodity prices the relationship between AR(q) and quasi-fBm.

In this paper the authors develop mathematical relationships in support of using AR(q > 1) processes for simulating quasi-fBm.

From theory the authors show that any AR(q) process is a stationary, self-similar process, with a lag structure that captures the essential elements of scaling and a fractional power law. The authors illustrate through various means the approach, and apply the quasi-fractional AR(q) process to agricultural commodity prices.

While the results can be applied to most time series of commodity prices, the authors limit the evaluation to the Gaussian case. Thus the approach does not apply to infinite-variance models.

The approach to using the structure of an AR(q > 1) model to simulate quasi-fBm is a simple approach that can be applied with ease using conventional Monte Carlo methods.

The authors believe that the approach to simulating quasi-fBm using standard AR(q > 1) models is original. The approach is intuitive and can be applied easily.