Do futures prices help forecast the spot price?

• DOI: http://doi.org/10.1002/fut.21854
• Published date: 01 December 2017
• Author: Xin Jin
• Date: 01 December 2017

Received: 15 September 2015

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Accepted: 15 March 2017

DOI: 10.1002/fut.21854

RESEARCH ARTICLE

Do futures prices help forecast the spot price?

Xin Jin

Department of Economics, Business School,

University of Aberdeen, Aberdeen, UK

Correspondence

Xin Jin, Department of Economics,

Business School, University of Aberdeen,

Edward Wright Building S67, Dunbar

Street, Old Aberdeen AB243QY, UK.

Email: xjin@abdn.ac.uk

This study proposes a futures-based unobserved components model for commodity

spot prices. Prices quoted at the same time incorporate the same information, but are

affected differently, resulting in the different shapes of futures curves. This model

utilizes information from part of the futures curve to improve forecasting accuracy of

the spot price. Applying this model to oil market data, I find that the model forecasts

outperform the literature benchmark (the no-change forecast) and futures prices

forecasts in multiple dimensions, with smaller average error variation over the sample

period and higher chance of smaller absolute error in each period.

JEL CLASSIFICATION

C38, G13, G17, Q4

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INTRODUCTION

Given the high volatility of commodity prices and the importance of raw materials in production, accurate forecast of the spot

price is of great interest for various purposes. Policy makers and central banks closely track commodity prices, especially crude

oil prices. Price forecasting is also crucial to business decisions in many industries.

One intuitive forecast of the spot price is the futures price. The efficient market hypothesis suggests the futures price as the

best forecast. A large literature has discussed the forecasting ability of futures prices. French (1986), Fama and French (1987),

Bowman and Husain (2004), Coppola (2008), Reichsfeld and Roache (2009), Reeve and Vigfusson (2011), Chinn and Coibion

(2014), among others find evidence confirming the forecasting ability of the futures prices for certain commodities, while

equally large amount of research like Bopp and Lady (1991), Moosa and Al-Loughani (1994), Chernenko, Schwarz, and Wright

(2004), Alquist and Kilian (2010), Alquist, Kilian, and Vigfusson (2013), find little evidence supporting the futures price as the

best forecast. Instead, no-change forecast is suggested as a plausible measure of the expected spot price. While Alquist et al.

(2013) provide a comprehensive overview of various forecasting models including futures-based ones, they do point out the

potential of factor (unobserved components) models for forecasting.

This study contributes to the forecasting and price dynamics literature by proposing a futures-based unobserved components

forecasting model which has superior forecasting accuracy. The improved forecasting accuracy relies on identifying the spot

price stochastic process by exploiting part of the futures curve (the term structure of the futures prices). The shape of the futures

curve is partly determined by the spot price stochastic process, if we consider the futures price as composed of the expected future

spot price and risk premium as in Pindyck (2001).

Prices quoted at the same time for immediate and future delivery of the same commodity all incorporate the same set of

information under the efficient market hypothesis, as argued by earlier work like Working (1942), Tomex and Gray (1970), and

Peck (1985). However, the prices are affected by the same set of information differently. For example, the information set

includes the growing demand for a certain commodity from the emerging economies, and the exploring and drilling activities in

search of it, which would have long-lasting effects on the prices, as well as temporary supply shortage and short-term demand

increase, which would have short-lived effects on the prices. Thus, the prices with further delivery dates are much less affected

J Futures Markets. 2017;37:1205–1225. wileyonlinelibrary.com/journal/fut © 2017 Wiley Periodicals, Inc.

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by the short-term effects of the information compared to the prices with nearer delivery dates, while all prices are affected by the

long-term effects similarly. The difference between the prices with further delivery dates (the far end of the futures curve) and

nearer delivery dates (the near end of the futures curve with the nearest being the spot price) roughly reflects the the short-term

effects of the information, while futures prices with further delivery dates (the far end of the futures curve) roughly reflect the

long-term effects of the information. Thus futures curve helps infer the stochastic process of the spot price.

The model allows for flexibility to fit the rich dynamics of the spot price and futures curves, while it is still relatively easy to

estimate. Applying the model to oil market data, I show that the model forecasts outperform both the no-change and the futures

price forecasts in multiple dimensions. Using 5-year rolling-window out-of-sample forecasting over 20 years at weekly

frequency, the model outperforms the literature benchmark (the no-change forecast) and the futures prices forecasts especially at

longer forecasting horizons from 32 to 48 weeks, as measured by MSE,ME, and MAE. The improvement at longer forecasting

horizons is also statistically significant as tested by the finite-sample unconditional predictive ability test developed in Giacomini

and White (2006). In addition, the model performs better in the 2000s than in the 1990s. The model suggests this could be due to

the changing commodity market conditions.

This study differs from recent research that has already paid attention to the value of futures curves. Coppola (2008) proposes

a VECM model which essentially uses futures curves for forecasting the spot price, where the spot price is modeled as a random

walk to which the effects of shocks will never dissipate. To the contrary, this study argues the effects of shocks to the spot price

could partially dissipate over time.

Motivated by the cost of carry model, the two-factor and three-factor models proposed in Schwartz (1997) and Cortazar and

Schwartz (2003) also bear resemblance to my model as they are essentially estimated using futures curves. However, the models

intuitively imply that the spot price follows a random walk process in the discrete time. The assumption implies the effect of

shocks to the spot price will not dissipate over time. This is fundamentally different from the proposed model in this study.

This study, instead, assumes that the spot price dynamics contains some temporary component. The idea that shocks to the

spot price could be dissipated is not new. Both theoretical and empirical works suggest that commodity price contains both a

long-term component and a short-term component (see Carlson, Khokher, & Titman, 2007; Fama & French, 1988; Pindyck,

1999; Stevens, 2013). Allowing part of the shocks to dissipate, the unobserved components model proposed in this study can then

be seen as the empirical extension of the recent development in the commodity price theory by Carlson et al. (2007) and Stevens

(2013). In terms of the assumed spot price stochastic process, my model is similar to the model of price evolution in Pindyck

(1999), but further relates the futures prices of different maturity terms to the spot price, and thus, is able to exploit the

information from futures curves.

The plan of the study is as follows: Section 2 develops the unobserved components model of crude oil prices and discusses the

model features. Section 3 provides an overview of the data, describes the statistical tests adopted, and presents the estimation

results from oil market. Section 4 presents and evaluates the forecasting ability of the model by comparing it to alternative

benchmarks and over time. Section 5 concludes.

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MODELING OF COMMODITY PRICES

In this section, an unobserved components model is constructed. The unobserved components model is able to take full

advantage of the information in the futures curve and uncover the unobserved factors affecting the prices.

2.1

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The spot and futures prices stochastic process

Shocks to commodity spot price could have effects of different time-persistence. French (1986) discusses the possibility that

“shocks to the current price are dissipated before they can affect the expected price”and observes that in such scenario futures

prices can provide “reliably better forecast.”Such observation indicates that shocks to the spot price would dissipate over time,

rather than being permanently preserved as in a martingale process. Fama and French (1988) discuss the long-term and short-

term components in asset prices although the purpose there is to test market efficiency. Pindyck (1999) also argues that a model

of price evolution should incorporate both a reversion to a long-run trend and a non-stationary stochastic long-run trend after

studying both the empirical features of commodity prices and the theoretical implications of the depletable resource prices. More

recently, Carlson et al. (2007) demonstrate that such price dynamics with both long-term and short-term components could arise

from a hotelling model with production adjustment costs. The impulse response functions of the spot price to shocks in Carlson

et al. (2007) show that part of the shocks are incorporated into price as temporary increments, rather than all shocks having long-

lasting effects on the price. Stevens (2013) also derives similar price dynamics from a hotelling model with storage.

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