OIL PRICE VOLATILITY : INFLUENCE OF THE TRADER'S BEHAVIOUR

E. Hache, F. Lantz

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Keywords : oil prices, volatility, Markov-switching model, threshold effect

1. Overview

This paper is concerned with the evolution of volatility in oil prices (Over the counter and Contracts prices) and the determination of a "threshold effect" which could explain the trader's behaviour in the market. In New York Mercantile Exchange (NYMEX), we identify two categories of agents ‘commercial’ or ‘non-commercial’. Commercial agents are the representatives of companies that actually consume the commodity itself whilst those are characterised as ‘non-commercial’ are agents that they do not have an immediate use of the commodity itself, thus cannot derive the convenience yield. The net position of the different agents exhibits substantial differences both in the oil and the products markets. There has been a long debate on whether "speculators" improves the efficiency in the oil markets or destabilizes the prices in the oil markets. In one hand they can help offset the activity of conventional operators who push the market in a certain direction for the purpose of hedging, while they also ensure higher volumes, and thus greater liquidity, which helps to ensure a supply-demand balance. In the other hand they can provide wrong signals and trigger "overreacted" behavior on the market without link with the market fundamentals.

2. Methodology

The statistical analysis is carried out in the context of VAR’s allowing for regime change in terms of a Markov process. The question we address is whether the net position in the futures markers of a particular type of trader can have some predictive power for spot and futures markets returns. We concentrate on the WTI crude oil spot and futures prices. We adopt a multivariate Markov switching model MS-VECM that allows for regime switching mean equation parameters and variance-covariance matrix.

To estimate this system we proceed as follows:

- We first estimate the long-run relationship (if any) using the Maximum Likelihood procedure suggested by Johansen. No regime switching is taken into account when obtaining estimates of the cointegrating vector.

- We then estimate the dynamic system above using the EM algorithm, that allows for two regimes, high and low volatility.

We then proceed with the Markov chain estimation , allowing for two regimes . The determination of the lag length is based on the Bayesian Information Criterion.The regimes are identified in terms of the volatility in the spot market. To confirm the dates of switching regime, we perform the Perron structural change tests - allowing additive outlier (AO) and Innovational outlier (IO).

Then we test on whether the positions that the different types of traders adopt in the market can be used as signals for the prediction of the appropriate regime.

Finally we test if there is a threshold effect for trader's position which could explain their market commitment.

3. Empirical results

The data consists of weekly observations from 1993.01.05 to 2009.03.31 of ‘price’ and ‘volume’ quotations for WTI as reported in NYMEX. Specifically the data series used in this paper are the WTI crude oil spot and futures prices (for different horizons), the net futures market positions (all maturities) for the two types of agent operating in the market, the traded volume in the futures (by maturity) and the open-interest contracts (by maturity).

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