• Korea Trade Review
• /
• v.47 no.3
• /
• pp.211-232
• /
• 2022

Due to the impact of the public health event COVID-19 epidemic, the Chinese futures market showed "Black Swan". This has brought the unpredictable into the economic environment with many commodities falling by the daily limit, while gold performed well and closed in the sunshine(Yan-Li and Rui Qian-Wang, 2020). Volatility is integral part of financial market. As an emerging market and a special precious metal, it is important to forecast return of gold futures price. This study selected data of the SHFE gold futures returns and conducted an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. Comparing the statistics of AIC, SC and H-QC, ARMA (12,9) model was selected as the best model. But serial correlation in the squared returns suggests conditional heteroskedasticity. Next part we established the autoregressive moving average ARMA-GARCH-type model to analysis whether Volatility Clustering and the leverage effect exist in the Chinese gold futures market. we consider three different distributions of innovation to explain fat-tailed features of financial returns. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE), Theil inequality coefficient(TIC) and root mean-squared error (RMSE). The results show that the ARMA(12,9)-TGARCH(2,2) model under Student's t-distribution outperforms other models when predicting the Chinese gold futures return series.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.6
• /
• pp.1661-1671
• /
• 2016

Risk analysis is a systematic study of uncertainties and risks we encounter in business, engineering, public policy, and many other areas. Value at Risk (VaR) is one of the most widely used risk measurements in risk management. In this paper, the Korean Composite Stock Price Index data has been utilized to model the VaR employing the classical ARMA (1,1)-GARCH (1,1) models with normal, t, generalized hyperbolic, and generalized pareto distributed errors. The aim of this paper is to compare the performance of each model in estimating the VaR. The performance of models were compared in terms of the number of VaR violations and Kupiec exceedance test. The GARCH-GPD likelihood ratio unconditional test statistic has been found to have the smallest value among the models.

• The Journal of Fisheries Business Administration
• /
• v.53 no.4
• /
• pp.15-26
• /
• 2022

This paper analyzed the price stabilization before and after the fisheries outlook project for seaweed, flatfish, and abalone. First, the stabilization effect was analyzed through the price variation coefficient before and after the observation project. In terms of the variation coefficient, there was no effect that the price was stabilized through the seaweed outlook project. However, it can be seen that flatfish and abalone have a price-stabilizing effect. Second, as a result of analyzing the price stabilization effect through the improved ARMA-T-GARCH model, it was confirmed that seaweed was not statistically significant while flatfish and abalone had a price stabilization effect by statistically significantly reducing volatility of real prices after the introduction of the fisheries outlook project. Third, as a result of analyzing the factors affecting price stability, it was found that the price of seaweed was stabilized after the WTO, but the Japanese earthquake expanded the price volatility. In the case of flatfish, it was analyzed that the price stabilized after the WTO and the Great Japanese Earthquake. Finally, the price of abalone has stabilized since the WTO and the Great Japanese Earthquake.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.6
• /
• pp.1065-1074
• /
• 2011

During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.