• International Area Studies Review
• /
• v.20 no.3
• /
• pp.123-144
• /
• 2016

This paper investigates the linkages between returns both in foreign exchange and stock markets, and uncertainties in two markets using daily data for the period of 16 July 2004 to 30 June 2014 in Nepalese economy. Four hypotheses are tested about how uncertainty influences the stock index and exchange rates. From the empirical results, a bivariate EGARCH-M model is the best to explain the volatility in the two markets. There is a negative relationship from the exchange rates return to stock price return. Empirical results do provide strong empirical confirmation that negative effect of stock index uncertainty and positive effect of exchange rates uncertainty on average stock index. GARCH-in-mean variables in AR modeling are significant and shows that there is positive effect of exchange rates uncertainty and negative effect of stock index uncertainty on average exchange rates. Stock index shocks have longer lived effects on uncertainty in the stock market than exchange rates shock have on uncertainly in the foreign exchange market. The effect of the last period's shock, volatility is more sensitive to its own lagged values.

• Bulletin of the Korean Mathematical Society
• /
• v.38 no.3
• /
• pp.495-504
• /
• 2001

In this paper we consider the (generalized) autoregressive model with conditional heteroscedasticity (ARCH/GARCH models). We willing give conditions under which strict stationarity, ergodicity and the functional central limit theorem hold for the corresponding models.

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

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.1
• /
• pp.237-245
• /
• 2007

Nonlinear asymmetric time series models have the growing interest in econometrics and finance. Threshold model is one of the successful asymmetric model. We consider a smooth transition ARMA model which converges a.s. to a threshold ARMA model and show that the smooth transition ARMA model admits a stationary measure, provided a suitable condition on the coefficients of the autoregressive parts of the different regimes is satisfied. Stationarity of a smooth transition GARCH model is also obtained.

• Journal of Korea Port Economic Association
• /
• v.30 no.3
• /
• pp.1-14
• /
• 2014

This paper aims at measuring how new information is incorporated into volatility estimates. Various GARCH models are compared and estimated with daily BDI(Baltic Dry Index) data. While most researchers agree that volatility is predictable, they differ on how this volatility predictability should be modelled. This study, hence, introduces the asymmetric or leverage volatility models, in which good news and bad news have different predictability for future. We provide the systematic comparison of volatility models focusing on the asymmetric effect of news on volatility. Specifically, three diagnostic tests are provided: the sign bias test, the negative size bias test, and the positive size bias test. From the Ljung-Box test statistic for twelfth-order serial correlation for the level we do not find any significant serial correlation in the unpredictable BDI. The coefficients of skewness and kurtosis both indicate that the unpredictable BDI has a distribution which is skewed to the left and significantly flat tailed. Furthermore, the Ljung-Box test statistic for twelfth-order serial correlations in the squares strongly suggests the presence of time-varying volatility. The sign bias test, the negative size bias test, and the positive size bias test strongly indicate that large positive(negative) BDI shocks cause more volatility than small ones. This paper, also, shows that three leverage models have problems in capturing the correct impact of news on volatility and that negative shocks do not cause higher volatility than positive shocks. Specifically, the GARCH model successfully reveals the shape of the news impact curve and is a useful approach to modeling conditional heteroscedasticity of daily BDI.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.3
• /
• pp.419-425
• /
• 2010

Kernel machine learning is gaining a lot of popularities in analyzing large or high dimensional nonlinear data. We use this technique to estimate a GARCH model for predicting the conditional volatility of stock market returns. GARCH models are usually estimated using maximum likelihood (ML) procedures, assuming that the data are normally distributed. In this paper, we show that GARCH models can be estimated using kernel machine learning and that kernel machine has a higher predicting ability than ML methods and support vector machine, when estimating volatility of financial time series data with fat tail.

• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.1053-1061
• /
• 2013

In this paper, we propose the time series forecasting models for internet traffic with long memory and heteroscedasticity. To control and forecast traffic volume, we first introduce the traffic forecasting models which are determined by the volatility and heteroscedasticity of the traffic. We then analyze and predict the heteroscedasticity and the long memory properties for forecasting traffic volume. Depending on the characteristics of the traffic, Fractional ARIMA model, Fractional ARIMA-GARCH model are applied and compared with the MAPE(Mean Absolute Percentage Error) Criterion.

• 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.

• The Korean Journal of Applied Statistics
• /
• v.22 no.2
• /
• pp.261-270
• /
• 2009

Current financial crisis triggered by shaky U.S. banking system adds to the emphasis on the importance of the volatility in controlling and understanding financial time series data. The ARCH and GARCH models have been useful in analyzing economic time series volatilities. In particular, multivariate GARCH(MGARCH, for short) provides both volatilities and conditional correlations between several time series and these are in turn applied to computations of hedge-ratio and VaR. In this short article, we try to assess various MGARCH models with respect to the back-testing performances in VaR study. To this end, 14 korean stock prices are analyzed and it is found that MGARCH outperforms rolling window, and BEKK and CCC are relatively conservative in back-testing performance.

• Environmental and Resource Economics Review
• /
• v.13 no.2
• /
• pp.175-194
• /
• 2004

Volumes of research have been implemented to estimate and predict the oil price. These models, however, fail in accurately predicting oil price as a model composed of only a few observable variables is limiting. Unobservable variables and news that have been overlooked in past research, yet have a high likelihood of affecting the oil price. Hence, this paper analyses the news impact on the price. The standard GARCH model fails in capturing some important features of the data. The estimated news impact curve for the GARCH model, which imposes symmetry on the conditional variances, suggests that the conditional variance is underestimated for negative shocks and overestimated for positive shocks. Hence, this paper introduces the asymmetric or leverage volatility models, in which good news and bad news have different impact on volatility. They include the EGARCH, AGARCH, and GJR models. The empirical results showed that negative shocks introduced more volatility than positive shocks. Overall, the AGARCH and GJR were the best at capturing this asymmetric effect. Furthermore, the GJR model successfully revealed the shape of the news impact curve and was a useful approach to modeling conditional heteroscedasticity.