Exchange Futures Market Research Articles

Functional Time Series Models for K-Step Prediction of Sugar Production: A Case Study of Eswatini, Kenya, and South Africa

Sugar, produced from cane or beet, is a vital energy source and a globally traded commodity. Most business happens in a futures exchange market where speculators, hedgers, and institutional consumers and producers, make decisions based on their understanding of the future supply and demand situation. Sugar is sometimes bought and sold before the cane or beet is planted. Hence, improving sugar production forecast accuracy is vital to maximize gains and enable effective planning. The study considered the annual sugar production total as a function of the monthly output rather than a scalar quantity and analyzed historical data using three functional time series techniques. In particular, the methods used for k-steps and dynamic updating prediction of sugar production include Local Polynomial Regression (LPR), Ridge Regression (RR), and Penalized Least Squares (PLS). The performance of the three models was compared to that of the automatic ARIMA method using the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE) measures to establish the suitability of each technique in sugar production forecasting. The LPR, RR, and PLS outperformed the ARIMA method in all three cases. However, the prediction accuracy was lower for highly volatile datasets from countries that overly depend on rainfall for cane development.

Learning dynamic causal relationships among sugar prices

In this paper, we are interested in exploring the dynamic causal relationships among two sets of three variables in different quarters. One set is futures sugar closing price in Zhengzhou futures exchange market (ZC), spot sugar price in Zhengzhou (ZS) and futures sugar closing price in New York futures exchange market(NC) and the other includes futures sugar opening price in Zhengzhou (ZO), ZS and NC. For each quarter, we first use Bayesian model selection to obtain the optimal causal graph with the highest BD scores and then use Bayesian model averaging approach to explore the causal relationship between every two variables. From the real data analysis, the two conclusions almost coincide, which shows that the two methods are practical.

Causal analysis of futures sugar prices in Zhengzhou

In this paper, we are interested in investigating the causal relationships among futures sugar prices in the Zhengzhou futures exchange market (ZF), the spot sugar prices in Zhengzhou (ZS) and the futures sugar prices in New York futures exchange market (NF). A useful tool called Bayesian network is introduced to analyze the problem. Since there are only three variables in our Bayesian network, the algorithm is straightforward: we display all the 25 possible network structures and adopt certain scoring metrics to evaluate them. We applied five different scoring metrics in total. Firstly, for each metric, we obtained 24 scores, each calculated from one of the 24 possible structures i.e. a Directed Acyclic Graph (DAG). Then we eliminated the network structure which represents the independence of the three variables according to our prior knowledge concerning the futures sugar market. After that, the optimal network structure which implies the causal relationships was selected according to the corresponding scoring metric. Finally, after comparing the results from different scoring metrics, we obtained the relatively affirmative conclusion that ZS causes ZF from both the Bayesian Dirichlet (BD) metric, Bayesian Dirichlet-Akaike Information Criterion (BD-AIC) metric, Bayesian Dirichlet-Bayesian Information Criterion (BD-BIC) metric and Bayesian Information Criterion (BIC) metric. The conclusions that NF causes ZF and ZF causes ZS from the Akaike Information Criterion (AIC) metric and ZF causes ZS from the BIC metric were useful and significant to our investigation.

Dynamical Minority and Majority Games in Korean Bond Futures Exchange Market

Recently, the investigation of minority games has received considerable attention as an interdisciplinary topic between physicists and economists. Among many outstanding topics, the evolutionary minority game, adaptive minority game, multi-choice minority game, $-game model, and grand canonical minority game have been the focus of attention. Challet et al. reported that the dynamical behavior of stock prices are characterized by anomalous fluctuations and exhibited a fat-tailed distribution and a long-range correlation. The dynamical behavior of statistical quantities between different types of trader has been extensively studied in the minority and majority games and $-game. Furthermore, the grand canonical minority game has been shown to reveal important stylized facts regarding financial market phenomenology such as return changes and volatility clusterings. Many researchers have mainly treated Arthur’s bar model, the seller and buyer model in financial markets, and the passenger problem in subways and buses. In various models, several methods of rewarding an agent’s strategies have been discussed and compared the resulting behaviors of the configurations in the minority game theory. De Almeida and Menche studied two options rewarded in standard minority games using adaptive genetic algorithms, and their result was found to be similar to that of standard minority games. Kim et al. analyzed the minority game for patients that is inspired by de Almeida and Menche’s problem. In particular, they applied the minority game theory to the numbers of both general patients and reserved patients in order to discuss numerically standard deviation and global efficiency. Until now, the minority game theory has been extensively applied to novel investigations for universal properties based on statistical concepts and methods in financial models. To our knowledge, it is of fundamental interest to estimate numerically and analytically the statistical quantities using the minority game theory. In this paper, we present the minority game theory for the transaction numbers of two types of KTB futures (KTB409 and KTB503) in the Korean futures exchange market. We discuss market payoffs and statistical quantities in the game theory. Using the minority game payoff, we numerically estimate standard deviation, global efficiency, and autocorrelation for the particular strategy. First of all, let us introduce the dynamical mechanism of both the minority and majority games. We assume that the agents N can decide independently whether to buy or sell the stock in round m. When the information and the strategy respectively take the value ðtÞ and S at time t, the action of the i-th agent is presented in terms of ai;s ðtÞðtÞ. Since an agent can submit an order ai;s ðtÞðtÞ 1⁄4 1 (buy) or ai;s ðtÞðtÞ 1⁄4 1 (sell), and the aggregate value, i.e., the sum of all action of agents, is given by AðtÞ 1⁄4 PN i1⁄40 ai;s ðtÞðtÞ. The statistical quantities such as volatility, global efficiency, and autocorrelation can be also found from the aggregate value. The payoffs Ui;s ðtÞ and Ui;sðtÞ for minority game and majority game models are, respectively, represented in terms of

Multifractal Measures for Bond Futures Prices in Futures Exchange Market

We study the tick dynamical behavior of the bond futures price using the rescaled range analysis (R/S analysis) in Korean futures exchange market. For our case, the multifractal Hurst exponents with long-run memory effects can be obtained from two kinds of Korean treasury bond futures transacted recently. It exists no crossover for the Hurst exponents at charateristic time scales. Particularly, we find that our result for probability distribution of prices is similar to a Lorentz distribution different from fat-tailed properties of bond futures price.

The Efficiency of the Market for Bank Accepted Bills*

The speculative efficiency of the Sydney Futures Exchange's market in bank accepted bills is examined by considering if the futures price is an unbiased predictor of the subsequent spot price and if other publicly available information can improve on this predictor. Data spanning the period 1980(1) to 1986(5) are employed The results are adverse to the efficiency hypothesis in that the futures price in some cases is not an unbiased predictor and neither is it an optimal predictor.