• Korean System Dynamics Review
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• v.8 no.2
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• pp.25-49
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• 2007

An important assumption of the classical linear regression model is that the disturbances appearing in the population regression function are homoskedastic; that is, they all have the same variance. If we persist in using the usual testing procedures despite heteroskedasticity, what ever conclusions we draw or inferences we make be very misleading. The contribution of this paper will be to the concrete procedure of the proper estimation when the heteroskedasticity does exist in the data, because the quality of dependent variable predictions, i.e., the estimated variance of the dependent variable, can be improved by giving consideration to the issues of regional homogeneity and/or heteroskedasticity across the research area. With respect to estimation, specific attention should be paid to the selection of the appropriate strategy in terms of the auxiliary regression model. The paper shows that by testing for heteroskedasticity, and by using robust methods in the presence of with and without heteroskedasticity, more efficient statistical inferences are provided.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.615-624
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• 2015

We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.

• Korea Trade Review
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• v.47 no.3
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• pp.211-232
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• 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 Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.429-442
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• 2015

Volatility forecasting in financial markets is an important issue because it is directly related to the profit of return. The volatility is generally modeled as time-varying conditional heteroskedasticity. A generalized autoregressive conditional heteroskedastic (GARCH) model is often used for modeling; however, it is not suitable to reflect structural changes (such as a financial crisis or debt crisis) into the volatility. As a remedy, we introduce the Markov regime switching GARCH (MRS-GARCH) model. For the empirical example, we analyze and forecast the volatility of the daily Korea Composite Stock Price Index (KOSPI) data from January 4, 2000 to October 30, 2014. The result shows that the regime of low volatility persists with a leverage effect. We also observe that the performance of MRS-GARCH is superior to other GARCH models for in-sample fitting; in addition, it is also superior to other models for long-term forecasting in out-of-sample fitting. The MRS-GARCH model can be a good alternative to GARCH-type models because it can reflect financial market structural changes into modeling and volatility forecasting.

• Journal of Korea Port Economic Association
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• v.34 no.1
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• pp.99-110
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• 2018

This study empirically examines simple methodology to quantify the risk resulted from the uncertainty of bunker price and foreign exchange rate, which cause main resources of the cost in shipping industry during the periods between $1^{st}$ of January 2010 and $31^{st}$ of January 2018. To shed light on the risk measurement in cash flows we tested GBM(Geometric Brownian Motion) frameworks such as the model with conditional heteroskedasticity and jump diffusion process. The main contribution based on empirical results are summarized as following three: first, the risk analysis, which is dependent on a single variable such as freight yield, is extended to analyze the effects of multiple factors such as bunker price and exchange rate return volatility. Second, at the individual firm level, the need for risk management in bunker price and exchange rate is presented as cash flow. Finally, based on the scale of the risk presented by the analysis results, the shipping companies are required that there is a need to consider what is appropriate as a means of risk management.

• The Journal of Asian Finance, Economics and Business
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• v.2 no.3
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• pp.17-22
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• 2015

This paper examines the effect of impulsiveness of euro on Indian stock market. In order to examine the problem, we select rupee-euro exchange rates and S&P CNX NIFTY and BSE30 SENSEX to represent stock price. We select euro as it considered as second most widely used currency at the international level after dollar. The data are collected a daily basis over a period of 3-Apr-2007 to 30-Mar-2012. The statistical and time series properties of each and every variable have examined using the conventional unit root such as ADF and PP test. Adopting a generalized autoregressive conditional heteroskedasticity (GARCH) and exponential GARCH (EGARCH) model, the study suggests a negative relationship between exchange rate and stock prices in India. Even though India is a major trade partner of European Union, the study couldn't find any significant statistical effect of fluctuations in Euro-rupee exchange rates on stock prices. The study also reveals that shocks to exchange rate have symmetric effect on stock prices and exchange rate fluctuations have permanent effects on stock price volatility in India.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.697-707
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• 2017

In this paper, we propose a modified GARCH(p, q)-X model which is obtained by adding the exogenous variables to the modified GARCH(p, q) process. Some limiting properties are shown under various stationary and nonstationary exogenous processes which are generated by another process independent of the noise process. The proposed model extends the GARCH(1, 1)-X model studied by Han (2015) to various GARCH(p, q)-type models such as GJR GARCH, asymptotic power GARCH and VGARCH combined with exogenous process. In comparison with GARCH(1, 1)-X, we expect that many stylized facts including long memory property of the financial time series can be explained effectively by modified GARCH(p, q) model combined with proper additional covariate.

• Asia-Pacific Journal of Business
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• v.9 no.2
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• pp.59-74
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• 2018

It is well known that market transactions do not lead to social optima when externalities exist. Given that previous studies such as RICARDO-AEA(2014) have identified various types of external costs, we must take their magnitudes, or externalities in general, into account in order to make toll prices to achieve social optimum. Little has been done on estimation of externalities in road uses in Korea, to the best of our knowledge. We suggested to use the contingent valuation method (CVM) to estimate overall social benefits and applied it to estimation of benefits of road kill prevention as a pilot study. Our empirical model has considered heteroskedasticity explicitly and its estimation result was that individual drivers were willing to pay 147 KRW on average in addition to current toll prices for prevention of road kills. We provided general discussions of externalities in road use and various internalization measures.

• Environmental and Resource Economics Review
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• v.10 no.4
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• pp.515-545
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• 2001

This paper introduces the least absolute deviations estimation of the contingent valuation model, which corresponds to the semi-parametric estimation of discrete choice models by Manski (1975, 1985) and Lee (1992). The least absolute deviations estimation is more robust to mis-specified distributional assumptions in the estimation of the contingent valuation model, compared to the maximum likelihood estimation. The full identification and strong consistency of the estimation are proved and its application to different formats of contingent valuation survey data is discussed. Simulation studies are designed to evaluate its operational characteristics including computational strategies, small sample properties and the efficiency gain of a follow-up question. The bias and efficiency of least absolute deviations and maximum likelihood estimation are compared in the presence of heteroskedasticity.