Calibration of Stochastic Convenience Yield Models For Crude Oil Using the Kalman Filter.

Delft – 22-02-08www.ing.com

Adriaan Krul

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Contents

Introduction

Convenience yield follows Ornstein-Uhlenbeck process

Analytical results

Convenience yield follows Cox-Ingersoll-Ross process

Analytical results

Numerical results

Conclusion

Further research

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Introduction

A future contract is an agreement between two parties to buy or sell an asset at a certain time in the future for a certain price.

Convenience yield is the premium associated with holding an underlying product or physical good, rather than the contract of derivative product.

Commodities – Gold, Silver, Copper, Oil

We use futures of light crude oil ranging for a period from 01-02-2002 until 25-01-2008 on each friday to prevent weekend effects.

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Stochastic convenience yield; first approach

We assume that the spot price of the commodity follows an geometrical brownian motion and that the

convenience yield follows an Ornstein-Uhlenbeck process. I.e., we have the joint-stochastic process

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In combination with the transformation x = ln S we have

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Analytical results

Expectation of the convenience yield

Variance of the convenience yield

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Analytical results

PDE of the future prices

Closed form solution of the future prices

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Stochastic convenience yield; second approach

We assume that the spot price of the commodity follows an geometrical brownian motion and that the convenience yield follows a Cox-Ingersoll-Ross process. I.e., we have the joint-stochastic process

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Together with the transformation x = ln S, we have

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Analytical results

Expectation of the convenience yield

Variance of the convenience yield

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Analytical results

PDE of the future prices

Closed form solution of the future prices

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Kalman filter

Since the spot price and convenience yield of commodities are non-observable state-variables, the Kalman Filter is the appropriate method to model these variables.

The main idea of the Kalman Filter is to use observable variables to reconstitute the value of the non-observable variables. Since the future prices are widely observed and traded in the market, we consider these our observable variables.

The aim of this thesis is to implement the Kalman Filter and test both the approaches and compare them with the market data.

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Kalman Filter for approach one.

Recall that the closed form solution of the future price

was given by

From this the measurement equation immediately follows

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From

we can write

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Kalman filter for approach one

The difference between the closed form solution and the

measurement equation is the error term epsilon.

This error term is included to account for possible

errors. To get a feeling of the size of the error, suppose

that the OU process generates the yields perfectly and that

the state variables can be observed

form the market directly. The error term could then be

thought of as market data, bid-ask spreads etc.

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Kalman filter for approach one

Recall the join-stochastic process

the transition equation follows immediately

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For simplicity we write

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Kalman filter for approach two

From

it follows

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Kalman filter for approach two

Recall the join-stochastic process

the transition equation follows immediately

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For simplicity we write

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How does the Kalman Filter work?

We use weekly observations of the light crude oil marketfrom 01-02-2002 until 25-01-2008. At each observation weconsider 7 monthly contracts. The systems matrices consists of the unknown parameter set. Choosing an initial set we can calculate the transition and measurement equation and update them via the Kalman Filter. Then the log-likelihood function is maximized and the innovations (error between the market price and the numerical price) is minimized.

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How to choose the initial state.

For the initial parameter set we randomly choose the value of the parameters within a respectable bound.For the initial spot price at time zero we retained it as the future price with the first maturity and the convenience yield is initially calculated via

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Numerical results for approach one

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Log future prices versus state variable x

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Implied convenience yield versus state variable delta

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Innovation for F1

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Kalman forecasting applied on the log future prices

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Kalman Forecasting applied on the state variables

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Conclusion

We implemented the Kalman Filter for the OU process. Both the convenience yield as well as the state variable x (log of the spot price) seems to follow the implied yield and the market price (resp.) quite good. Also, different initial values for the parameter set will eventually converge to the optimized set with the same value of the log-likelihood. This is a good result and tests the robustness of the method.

The main difference between the systems matrices of both processes is the transition error covariance-variance matrix Vt. In the CIR model, this matrix forbids negativity of the CY. We simply replaced any negative element of the CY by zero, but since it is negative for a large number of observations, this will probably give rise to large standard errors in the optimized parameter set.

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Conclusion

The Kalman Forecasting seems to work only if there is no sudden drop in the data. To improve the Kalman Forecasting we could update it every 10 observations.

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Further research

• Implement the Kalman Filter for the CIR model

• Inserting a jump constant in the convenience yield

• Compare both stochastic models

• Pricing of options on commodities, using the optimized parameter set