کارایی بازار نفت خام و مدل سازی: دیدگاه الگوی همبستگی چندمعیاری

Empirical research on market inefficiencies focuses on the detection of autocorrelations in price time series. In the case of crude oil markets, statistical support is claimed for weak efficiency over a wide range of time-scales. However, the results are still controversial since theoretical arguments point to deviations from efficiency as prices tend to revert towards an equilibrium path. This paper studies the efficiency of crude oil markets by using lagged detrended fluctuation analysis (DFA) to detect delay effects in price autocorrelations quantified in terms of a multiscaling Hurst exponent (i.e., autocorrelations are dependent of the time scale). Results based on spot price data for the period 1986–2009 indicate important deviations from efficiency associated to lagged autocorrelations, so imposing the random walk for crude oil prices has pronounced costs for forecasting. Evidences in favor of price reversion to a continuously evolving mean underscores the importance of adequately incorporating delay effects and multiscaling behavior in the modeling of crude oil price dynamics.

Fair valuation of projects and securities for the crude oil industry, including exploration, extraction, distribution and chemical transformation, requires accurate stochastic modeling to describe the observed complex dynamics of prices and returns. For instance, the famous Black-Scholes option pricing formula assumes that the commodity price follows a geometric Brownian motion (Smith and McCardle, 1998). In this model, prices are expected to grow at some constant drift rate with the variance in future spot prices increasing in proportion to time. If prices increase (resp., decrease) more than anticipated in one time period, all future forecasts are increased (resp., decreased) proportionally. The attractiveness of this modeling approach is that it leads to closed-form solutions that can be easily used in practice. The underlying idea behind the Brownian motion assumption is that, after removing a constant drift, the dynamics of the (logarithmic) price differences can be described as an uncorrelated process standing in for any and all sources of uncertainty in the price history of the commodity. In turn, this concept is linked to the idea that valuation fairness is possible because returns cannot be predicted for any time horizon. In financial theory, this is known as the efficient-market hypothesis (EMH), which asserts that financial markets are informationally efficient in the sense that prices on traded assets already reflect all known information1, and instantly change to reflect new information (Fama, 1970). Therefore, according to theory, it is impossible to consistently outperform the market by using any information that the market already knows, except through luck. In its weak form, the EMH states that future prices cannot be predicted by analyzing price from the past. In this way, excess returns cannot be earned in the long run by using investment strategies based on historical share prices or other historical data. In turn, this implies that prices exhibit no serial dependencies, meaning that there are no patterns to asset prices, and so future price movements are determined entirely by information not contained in the price series. Such assumptions should imply that prices must follow a random walk. In recent years, investors and researchers have disputed the EMH both empirically and theoretically. The normal occurrence of human errors in reasoning and information processing (e.g., overconfidence, overreaction and information bias) has been used as a suitable framework by behavioral economist to explain information imperfections in financial and commodity markets ( Kahneman and Tversky, 1979 and Kahneman and Tversky, 2000). On the other hand, empirical analysis has provided mixed results, although evidence hinting to market efficiency is poorly supported ( Chen et al., 2003 and Charles and Darne, 2009). Speculative bubbles can be considered as anomalies where the market often appears to be driven by buyers operating on irrational exuberance, who take little notice of underlying value. These bubbles are typically followed by an overreaction of frantic selling, allowing shrewd investors to buy stocks at bargain prices ( Lo and MacKinlay, 2001). The efficiency of crude oil markets is a subtle issue given that the market configuration involves governments, large-scale producers, consumers and investors. In this way, one could expect that the underlying market dynamics exhibit important deviations from the EMH. This paper focuses on the market efficiency issue by exploring the presence of autocorrelations in historic crude oil price dynamics. In contrast to existing results in the open literature, the present work considers delay effects acting in the formation of crude oil prices. The results found with this approach indicate that deviations from efficiency and the type of model to describe return dynamics are dependent of the forecasting horizon.

In this paper, we used detrended fluctuation analysis (DFA) with lagged autocorrelations to analyze the scaling properties of daily crude oil prices. We focused on the estimation of the multiscaling pattern determined by the variations of the Hurst exponent with respect to time-scales. The results and their discussion suggested the following conclusions: (a) The multiscaling pattern is not continuous, showing two discontinuities at one-quarter and one-year scales. These discontinuities indicate different sources of price fluctuations, from speculative effects to fundamental supply and demand shocks. (b) The crude oil market present important deviations from efficiency. In contrast to previous results that suggested efficient behavior for large time-scales, our results indicate positive or negative autocorrelations that might be masked by delay effects. (c) The application range of a forecasting model is limited by the time horizon. In turn, the multiscaling pattern within the considered time horizon provide important insights in the model structure and sampling (i.e., daily, weekly or monthly) frequency. (d) Negative autocorrelations over a wide time-scale range indicate that mean-reversion with continuously evolving mean is a suitable modeling framework for forecasting purposes. Within this view, our results indicate that changes in the real price of crude oil have historically tended to be permanent, difficult to predict and governed by very different regimes at different periods of time ( Hamilton, 2008). Reaching beyond the multiscaling pattern issue and the implications for the validity of the EMH, our results illustrate the importance of relying on time series analysis for establishing model structures, formulating investment time horizons (quarter, years or longer) and defining sampling frequencies. In principle, results in this line should be combined theoretical considerations for the construction of accurate models incorporating both empirical evidence and economical fundamentals. For instance, future approaches should incorporate the recent perception by market participants of the importance of reserves uncertainty and exhaustion of production fields.