• Asian Journal of Business Environment
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• v.9 no.3
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• pp.5-9
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• 2019

Purpose - This paper examines whether fiscal and monetary expansion would affect output in Australia. Research design, data, and methodology - An extended IS-LM model which describes the equilibrium in the goods market and the money market is applied. The real effective exchange rate and the real stock price are included in order to determine whether there may be any substitution or wealth effect. The sample consists of Annual data ranging from 1990 to 2018. The GARCH process is used in empirical work to correct for potential autoregressive conditional heteroscedasticity. Results - Expansionary fiscal policy reduces output; whereas, expansionary monetary policy raises output. In addition, real appreciation of the Australian dollar, a lower U.S. interest rate, a higher real stock price or a lower expected inflation would increase output. The finding that expansionary fiscal policy has a negative impact on real GDP suggests that the negative crowding-out effect on private spending dominates the positive impact. Conclusions - Fiscal prudence needs to be pursued. Real depreciation of the Australian dollar hurts output. Monetary tightening in the U.S. generates a negative effect on Australia's output. A healthy stock market is conducive to economic growth as higher stock prices tend to result in the wealth and other positive effects, increasing consumption and business spending.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.297-308
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• 2020

We focus on the functional autoregressive conditional heteroscedasticity (fARCH) modelling to analyze intraday volatilities based on high frequency financial time series. Multivariate volatility models are investigated to approximate fARCH(1). A formula of multi-step ahead volatilities for fARCH(1) model is derived. As an application, in implementing fARCH(1), a choice of appropriate time interval for the intraday return is discussed. High frequency KOSPI data analysis is conducted to illustrate the main contributions of the article.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.251-257
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• 2020

The study investigates the role of commodity prices and tax purpose recognition on bitcoin prices. Since the introduction of bitcoin in 2008, emphasis has focused on economists, policy-makers and analysts drastically increasing bitcoin's accessibility and commodity values (Dumitrescu & Firică, 2014). This study employs GARCH and EGARCH from ARCH/GARCH family on daily nature data. We measure the volatile behavior of bitcoin by employing auto-regressive conditional heteroscedasticity model with the aim to explore the relationship between major commodities and bitcoin volatility. We focus on major commodities like gold, silver, platinum, and crude oil to be regressed with bitcoin. The daily prices of commodities were retrieved from www.investing.com and bitcoin prices from www.coindesk.com for the period from 29April 2013 to 16 October 2018. Results confirmed the currency's long-term volatile behavior, which is due to its composition and market dynamics, whereas the existence of asymmetric information effect is not confirmed. Tax recognition by other countries may in future help in controlling the volatility as bitcoin is not a country-specific security. But, only silver impacts on volatility in comparison to oil prices and platinum, which is due to its similar features with gold. Eventually, bitcoin can be used for risk diversification and money making.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.913-921
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• 2021

The objective of the study is to investigate the impact of the COVID-19 pandemic on Saudi Arabia stock market. The study relied on the data of the daily closing stock market price index Tadawul All Share Index (TASI), and the number of daily cases infected with COVID-19 during the period from March 15, 2020, to August 10, 2020. The study employs the Vector Auto-Regressive (VAR) model, the Impulse Response Function (IRF) and Autoregressive Conditional Heteroscedasticity (ARCH) models. The results of the correlation matrix and the Impulse Response Function (IRF) show that stock market returns responded negatively to the growth in COVID-19 infected cases during the pandemic. The results of ARCH model confirmed the negative impact of COVID-19 pandemic on KSA stock market returns. The results also showed that the negative market reaction was strong during the early days of the COVID-19 pandemic. The study concluded that stock market in KSA responded quickly to the COVID-19 pandemic; the response varies over time according to the stage of the pandemic. However, the Saudi government's response time and size of the stimulus package have played an important role in alleviating the impacts of the COVID-19 pandemic on Saudi Arabia Stock Market.

• Korean Journal of Agricultural Science
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• v.45 no.4
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• pp.859-870
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• 2018

Dried red peppers are a staple agricultural product used in Korean cuisine and as such, are an important aspect of agricultural producers' income. Correctly forecasting both their supply and demand situations and price is very important in terms of the producers' income and consumer price stability. The primary objective of this study was to compare the performance of time series forecasting models for dried red peppers in Korea. In this study, three models (an autoregressive model with exogenous variables [ARX], AR-exponential generalized autoregressive conditional heteroscedasticity [EGARCH], and ARX-EGARCH) are presented for forecasting the wholesale price of dried red peppers. As a result of the analysis, it was shown that the ARX model and ARX-EGARCH model, each of which adopt both the rolling window and the adding approach and use the agricultural cooperatives price as the exogenous variable, showed a better forecasting performance compared to the autoregressive model (AR)-EGARCH model. Based on the estimation methods and results, there was no significant difference in the accuracy of the estimation between the rolling window and adding approach. In the case of dried red peppers, there is limitation in building the price forecasting models with a market-structured approach. In this regard, estimating a forecasting model using only price data and identifying the forecast performance can be expected to complement the current pricing forecast model which relies on market shipments.

• Wind and Structures
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• v.31 no.3
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• pp.269-285
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• 2020

The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons.

• International Area Studies Review
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• v.20 no.1
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• pp.3-22
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• 2016

In this study, I examine mutual shock spillover effects among interest rate differences, won-dollar foreign exchange change rates, and stock market returns in Korea during the daily sample period from the beginning of 1995 to the October 16, 2015, using the multivariate GARCH (generalized autoregressive conditional heteroscedasticity) BEKK (Baba-Engle-Kraft-Kroner) model framework. Major findings are as follows. Throughout the 6 model estimation results of variance equations determining return spillovers covered from symmetric and asymmetric models of total sample period and two crisis sub-sample periods composed of Korean FX Crisis Times and Global Financial Crisis Times, shock spillovers are shown to exist mainly from stock market return shocks. Stock market shocks including down-shocks from the asymmetric models are shown to transfer to those other two markets most successfully. Therefore it is most important to maintain stable financial markets that a policy design for stock market stabilization such as mitigating stock market volatility.

• Korean Business Review
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• v.12
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• pp.119-138
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• 1999

In this study, we empirically investigate the relations between trading volumes and our proxies for information flows and differences of opnion. Econometric methods to analyze the relations in the equity and KOSPI 200 futures markets include Generalized Method of Moment(GMM) and Generalized Autoregressive Conditional Heteroscedasticity(GARCH) models. Major findings from our empirical analyses are summarized as follows; (i) Trading volume in both the equity and KOSPI 200 futures markets varies positively with proxies for information flows. We find that trading volumes in both markets are closely related to firm-specific information rather than market-wide information. (ii) Trading volumes in the equity and KOSPI 200 futures market have positive relations with our proxies for differences of opinion. (iii) Day-of-the-week effect is clear in both markets. Trading volumes in both the equity and KOSPI 200 futures markets tend to be relatively low early and late in the week. (IV) Futures contract life-cycle effect is clear. In other words, futures trading volume increses in the period around contract expiration. (V) In addition, ARCH effect on trading volumes is reported significant enough to take into account. The disturbance of trading volumes in both markets seem to be conditional heteroscedastic.

• Journal of Korea Trade
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• v.27 no.1
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• pp.139-158
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• 2023

Purpose - The study of co-movements between stock markets is a crucial area of finance and has recently received much interest in a variety of studies, especially in international finance. Stock market co-movements are a major phenomenon in financial markets, but they are not necessarily independent of the real market. Several studies support the idea that bilateral trade linkages significantly impact stock market correlations. Motivated by this perspective, this study investigates whether real market integration due to trade agreements brings about financial market integration in terms of stock market co-movement. Design/methodology - Over the 10 free trade agreements (FTAs) signed by the United States, using a dynamic conditional correlations (DCC) multivariate GARCH (MGRACH) model, we empirically measure the degree of integration by finding DCCs between the US market and the partner country's market. We then track how these correlations evolve over time and compare the results before and after trade agreements. Findings - According to the empirical results, there are positive return spillover effects from the US market to eight counterpart equity markets, except Jordan, Morocco, and Singapore. Especially Mexico, Canada, and Chile have large return spillover effects at the 1% significance level. All partner countries of FTAs generally have positive correlations with the US over the entire period, but the size and variance are somewhat different by country. Meanwhile, not all countries that signed trade agreements with the United States showed the same pattern of stock market co-movement after the agreement. Korea, Mexico, Chile, Colombia, Peru, and Singapore show increasing DCC patterns after trade agreements with the US. However, Canada, Australia, Bahrain, Jordan, and Morocco do not show different patterns before and after trade agreements in DCCs. These countries generally have the characteristic of relatively lower or higher co-movements in stock markets with the US before the signing of the FTAs. Originality/value - To our knowledge, few studies have directly examined the linkages between trade agreements and stock markets. Our approach is novel as it considers the problem of conditional heteroscedasticity and visualizes the change of correlations with time variations. Moreover, analyzing several trade agreements based on the United States enables the results of cross-country pairs to be compared. Hence, this study provides information on the degree of stock market integration with countries with which the United States has trade agreements, while simultaneously allowing us to track whether there have been changes in stock market integration patterns before and after trade agreements.

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