Price Discovery Function Of Futures Research Articles

Dynamic Price Discovery Process of Chinese Agricultural Futures Markets: An Empirical Study Based on the Rolling Window Approach

AbstractWe investigate the dynamic evolution of the price discovery function in Chinese agricultural futures markets using a newly developed rolling window cointegration approach. The results show that, compared with wheat and rice, the futures-spot cointegration relationship in the soybean and corn markets tends to be more durable and frequent. Dynamic cointegration analysis indicates that the recent market-oriented reforms in China have boosted the price discovery function of soybean and corn futures markets, whereas price stabilization policies tend to weaken the price discovery function of futures markets. The difference in price discovery function is attributed to differences in market mechanisms and Chinese agricultural policies.

The Impact of Restrictive Measures on the Price Discovery Function of Stock Index Futures – Evidence From CSI 500 Stock Index Futures

Restrictive measures implemented by governments have a great impact on the price discovery function of stock index futures. This study compares the price discovery function of CSI 500 stock index futures and CSI 500 stock index before and after the implementation of restrictive measures based on the reaction speed to new information, the price ratio of new information and the price contribution of both future market and spot market. It also analyzes the difference between the price discovery function of the future market and that of the spot market and thus proposes policy implications accordingly.Utilizing data of CSI 500 stock index futures in the period of the stock market crash, this study compares the price discovery function before and after the implementation of restrictive measures. By means of the VECM model and common factor analysis, it further investigates the difference in the price contribution of the two markets. Contributing to existing literature on the relationship between the future market and the spot market, this study explores the change in the price contribution of the two markets and therein studies the impact of restrictive measures on the price discovery function. Empirical evidence finds that before the implementation of restrictive measures, the price discovery function worked more efficiently, while, however, after the implementation of restrictive measures, the price discovery function did not work. Hence, stock index futures do assist in the price discovery of the spot market. In some special time periods, however, due to the impact of restrictive policies, the price contribution of the spot market exceeded that of the future market, implying that the price discovery function of the CSI 500 stock index future market is unstable.

The Empirical Research on the Price Discovery Function of CSI300 Index Futures based on Birge-Massart Threshold Denoising

This paper analyzes the short-term casual relationship and long-term equilibrium relationship between CSI300 spot and futures prices by using cointegration test and error correlation models with one minute price data revised by the Birge-Massart threshold method in wavelet transform theory. The empirical results suggest that (1) The Birge-Massart threshold method can make a good job for removing the impact from intra-day noise traders’ behaviors; (2) The intra-day noise traders’ behaviors weaken the price discovery function of futures markets. After stripping the influence of intraday noise traders, there are bi-directional lead relations and long-run equilibrium relationship between CSI300 spot and futures prices.

Unbiased Estimation, Price Discovery, and Market Efficiency: Futures Prices and Spot Prices

Abstract In most cases, futures prices are not unbiased estimates of future spot prices. The price discovery function of futures markets should be defined as the lead-lag relationship between current futures prices and current spot prices, not the unbiased estimation of future spot prices. The pricing efficiency and information efficiency of futures markets are different. Three empirical models appropriate to investigate the relationship between futures prices and spot prices were discussed. As an application, these models were used to test the pricing efficiency, the lead-lag relationship, and the information efficiency of the S&P 500 index spot and futures markets from 21 September 1990 to 20 December 2007.

Price Movements and Price Discovery in Futures and Cash Markets

R ISK transfer and price discovery are two of the major contributions of futures markets to the organization of economic activity (Working (1962), Evans (1978, p. 80), and Silber (1981)). Risk transfer refers to hedgers using futures contracts to shift price risk to others. Price discovery refers to the use of futures prices for pricing cash market transactions (Working (1948), Wiese (1978, p. 87), and Lake (1978, p. 161)). The significance of both contributions depends upon a close relationship between the prices of futures contracts and cash commodities. This paper examines the characteristics of price movements in cash (or spot) markets and futures markets for storable commodities. Section II presents an analytical model of simultaneous price dynamics which suggests that, over short intervals of time, the correlation of price changes is a function of the elasticity of arbitrage between the physical commodity and its counterpart futures contract. Greater elasticity fosters more highly correlated price changes, and thereby facilitates the risk transfer function. The elasticity of supply of arbitrage services is constrained by, among other things, storage and transaction costs. Thus, futures contracts will not, in general, provide perfect risk transfer facilities over short time horizons. The essence of the price discovery function of futures markets hinges on whether new information is reflected first in changed futures prices or in changed cash prices (Hoffman (1932, pp. 258259)). The model in section II provides a framework for analyzing whether one market is dominant in terms of information flows and price discovery. In section III we develop a model based on section II which is appropriate for estimating the lead-lag relationship between cash prices and futures prices. Section IV presents empirical estimates of the parameters of the model for seven different storable commodities: wheat, corn, oats, frozen orange juice concentrates, copper, gold, and silver. The cost of arbitrage between cash and futures differs across these commodities. For this reason we are not surprised to find inter-commodity differences in the correlation of short-run price changes and in the substitutability of futures contracts for cash market positions. With respect to the price discovery function of futures markets, we find that while futures markets dominate cash markets, cash prices do not merely echo futures prices; there are reverse information flows from cash markets to futures markets as well.