General Equilibrium Stock Index Futures Prices: Theory and Empirical Evidence

1991 ◽  
Vol 26 (3) ◽  
pp. 287 ◽  
Author(s):  
Michael L. Hemler ◽  
Francis A. Longstaff
Keyword(s):  
General Equilibrium ◽  
Empirical Evidence ◽  
Stock Index ◽  
Stock Index Futures ◽  
Futures Prices ◽  
Index Futures

Index-Futures Arbitrage and the Behavior of Stock Index Futures Prices

1988 ◽  
Vol 1 (2) ◽  
pp. 137-158 ◽  
Author(s):  
A. Craig MacKinlay ◽  
Krishna Ramaswamy
Keyword(s):  
Stock Index ◽  
Stock Index Futures ◽  
Futures Prices ◽  
Index Futures

Futures and Forward Prices - Theory and Hungarian Experience

2004 ◽  
Vol 54 (2) ◽  
pp. 159-174
Author(s):  
M. Radnai
Keyword(s):  
Stock Exchange ◽  
Relative Difference ◽  
Stock Index ◽  
Futures Contracts ◽  
Stock Index Futures ◽  
Futures Prices ◽  
Index Futures ◽  
Forward Prices ◽  
The Difference ◽  
Hungarian Experience

Researchers have examined the difference between forward and futures prices since the introduction of futures contracts. In this paper we derive the explicit formula for stock-index futures prices under the assumptions of lognormal asset prices, determine the relative difference between futures and forward prices, and test the model for BUX contracts traded on the Budapest Stock Exchange between 1997 and 2002.

scholarly journals Chinese Stock Index Futures Price Fluctuation Analysis and Prediction Based on Complementary Ensemble Empirical Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Ruoyang Chen ◽  
Bin Pan
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Stock Index ◽  
Stock Index Futures ◽  
Futures Prices ◽  
Index Futures ◽  
Price Fluctuations ◽  
Mode Decomposition ◽  
Svm Model

Since the CSI 300 index futures officially began trading on April 15, 2010, analysis and predictions of the price fluctuations of Chinese stock index futures prices have become a popular area of active research. In this paper, the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method is used to decompose the sequences of Chinese stock index futures prices into residue terms, low-frequency terms, and high-frequency terms to reveal the fluctuation characteristics over different time scales of the sequences. Then, the CEEMD method is combined with the Particle Swarm Optimization (PSO) algorithm-based Support Vector Machine (SVM) model to forecast Chinese stock index futures prices. The empirical results show that the residue term determines the long-term trend of stock index futures prices. The low-frequency term, which represents medium-term price fluctuations, is mainly affected by policy regulations under the analysis of the Iterated Cumulative Sums of Squares (ICSS) algorithm, whereas short-term market disequilibrium, which is represented by the high-frequency term, plays an important local role in stock index futures price fluctuations. In addition, in forecasting the daily or even intraday price data of Chinese stock index futures, the combination prediction model is superior to the single SVM model, which implies that the accuracy of predicting Chinese stock index futures prices will be improved by considering fluctuation characteristics in different time scales.

LONG MEMORY AND SAMPLING FREQUENCIES: EVIDENCE IN STOCK INDEX FUTURES MARKETS

2006 ◽  
Vol 09 (05) ◽  
pp. 787-799 ◽  
Author(s):  
SHWU-JANE SHIEH
Keyword(s):  
Empirical Evidence ◽  
Futures Markets ◽  
Fractional Integration ◽  
Stock Index ◽  
Futures Contracts ◽  
Stock Index Futures ◽  
Index Futures ◽  
Jones Index ◽  
Persistence Properties ◽  
Q Model

The long-term dependent behavior in the close prices of the S&P 500, Nikkei 225, and Dow Jones index futures contracts are investigated by using the ARFIMA (p, d, q) model to estimate the order of the fractional integration parameters for a large range of sampling frequencies: from one-minute to monthly frequencies. The empirical evidence shows that the close prices exhibit anti-persistence properties for most of the sampling frequencies. This suggests that the contrarian's trading strategies in relation to stock index futures markets have a positive value. Moreover, the empirical evidence indicates that the higher frequency of the data, the stronger degree of contrarian behaviors, particularly for S&P 500 and Dow Jones stock index futures contracts.

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