The aim of the study is to discuss the relationship of the crude oil price, speculative activity and fundamental factors. An empirical study was conducted with a VEC model. Two cointegrating vectors were identified. The first vector represents the speculative activity. We argue that the number of short non-commercial positions increases with the crude oil stock and price, decreases with the higher number of long non-commercial positions. A positive trend of crude oil prices may be a signal for traders outside the industry to invest in the oil market, especially as access to information could be limited for them. The second vector represents the crude oil price under the fundamental approach. The results support the hypothesis that the crude oil price is dependent on futures trading. The higher is a number of commercial long positions, the greater is the pressure on crude oil price to increase.
We develop a fully Bayesian framework for analysis and comparison of two competing approaches to modelling daily prices on different markets. The first approach, prevailing in financial econometrics, amounts to assuming that logarithms of prices behave like a multivariate random walk; this approach describes logarithmic returns most often by the VAR(1) model with MGARCH (or sometimes MSV) disturbances. In the second approach, considered here, it is assumed that daily price levels are linked together and, thus, the error correction term is added to the usual VAR(1)–MGARCH or VAR(1)–MSV model for logarithmic returns, leading to a reduced rank VAR(2) specification for logarithms of prices. The model proposed in the paper uses a hybrid MSV-MGARCH structure for VAR(2) disturbances. In order to keep cointegration modelling as simple as possible, we restrict to the case of two prices representing two different markets. The aim of the paper is to show how to check if a long-run relationship between daily prices exists and whether taking it into account influences our inference on volatility and short-run relations between returns on different markets. In the empirical example the daily values of the S&P500 index and the WTI oil price in the period 19.12.2005 – 30.09.2011 are jointly modelled. It is shown that, although the logarithms of the values of S&P500 and WTI oil price seem to be cointegrated, neglecting the error correction term leads to practically the same conclusions on volatility and conditional correlation as keeping it in the model.