• Management & Information Systems Review
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• v.30 no.2
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• pp.237-255
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• 2011

This empirical study is focused on practical application of Range-Based Volatility which is estimated by opening, high, low, closing price of overall asset. Especially proper forecasting period is what I want to know. There is four useful Range-Based Volatility(RV) such as Parkinson(1980; PK), Garman and Klass(1980; GK) Rogers and Satchell(1991; RS), Yang and Zhang(2008; YZ). So, four RV of KOPSI 200 index during 2000.5.22-2009.9.18 was used for empirical test. The emprirical result as follows. First, the best RV which shows the best forecasting performance is PK volatility among PK, GK, RS, YZ volatility. According to estimating period forcasting performance of RV shows delicate difference. PK has better performance in the period with financial crisis of sub-prime mortgage loan. if not, RS is better. Second, almost result shows better performance on forecasting volatility without sub-prime mortgage loan period. so we can say that forecasting performance is lower when historical volatiltiy is comparatively high. Finally, I find that longer estimating period in AR(1) and MA(1) model can reduce forecasting error. More interesting point is that the result shows rapid decrease form 60 days to 90 days and there is no more after 90 days. So, if we forecast the volatility using Range-Based volaility it is better to estimate with 90 trading period or over 90 days.

• The Korean Journal of Financial Management
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• v.26 no.4
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• pp.191-234
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• 2009

This study examined on the volatility feedback hypothesis through the use of threshold GARCH-in-Mean (GJR-GARCH-M) model developed by Glosten, Jaganathan, and Runkle (1993) in the stock markets of 14 emerging countries during the period of January, 1996 to May, 2009. On this study, I found successful evidences which can support the volatility feedback hypothesis through the following three estimation procedures. First, I found relatively strong positive relationship between the expected market risk premiums and their conditional standard deviations from the GARCH-M model in the basis of daily return on each representative stock market index, which is appropriate to investors' risk-averse preferences. Second, I can also identify the significant asymmetric time-varying volatility originated from the investors' differentiated reactions toward the unexpected market shocks by applying the GJR-GARCH-M model and further find the lasting positive risk aversion coefficient estimators. Third, I derived the negative signs of the regression coefficient of unpredicted volatility on the stock market return by re-applying the GJR-GARCH-M model after I controlled the positive effect of predicted volatility through including the conditional standard deviations from the previous GARCH-M model estimation as an independent explanatory variable in the re-applied new GJR-GARCH-M model. With these consecutive results, the volatility feedback effect was successfully tested to be effective also in the various emerging stock markets, although the leverage hypothesis turned out to be insufficient to be applied to another source of explaining the negative relationship between the unexpected volatility and the ex-post stock market return in the emerging countries in general.

• Journal of Environmental Policy
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• v.10 no.2
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• pp.45-60
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• 2011

An aim of this paper is to test four hypotheses on price volatility in the $CO_2$ emission markets focusing on European Climate Exchange(ECX) in the EU Emission Trading Schemes(EU ETS) and Chicago Climate Exchange(CCX). I expect that, due to an influx of market information, a differently designed exchange market would bring a different price volatility, and various types of emission permits in the same exchange market would result in the same effects on the price volatility. Major findings are that the price volatility is same regardless of the types of emission exchange markets and emission permits comparing the rate of returns. However, comparing the GARCH variance, the volatility between ECX EUAs and CCX-CFIs and the volatility between EUAs(CERs) futures and daily futures are different with the exception of the volatility between EUAs futures and CERs futures. In conclusion, the price volatility depends on the types of exchanges and the types of emission permits.

• International Commerce and Information Review
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• v.7 no.3
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• pp.269-286
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• 2005

The exchange rate volatility has been increased since the time when the floating exchange rate system was introduced in Korea. As a result, the increase of the exchange rate volatility raised the risk in international trades in Korea. The purpose of this study in to study the feature of exchange rate volatility and the main sources of its increase and to confirm whether the exchange rate volatility influence export volume and price of Korea. In the first place, I measured exchange rate volatility with two methods. The one is descriptive statistic method such as the width of daily exchange rate fluctuation and the rate of exchange rate devaluation. The other is the time varying conditional variance of exchange rate. Then, I studied the sources of exchange rate volatility. In the second place, I defined the exchange rate volatility as the time varying conditional variance and estimated it by using elastic a approach model which shows exchange rate is affected by itself and its conditional variance, I estimated its effects on export volumes and prices of electric home appliances, information & communication equal and semi-conductor. The result of this study is as follows. With presumed result EU and Korea because is not the goods which is to substantial competition relationship, The effect where the relative value change of presumed result expression anger and the dollar of import and export function goes mad to the import and export of Korea the income compared to is to export and it is appearing a lot. The EU goods is sold more expensively the Korean goods than from about length being caused by American market of the dollar and the balance of trade of Korea is visible like being visible the improvement of single breadth. Because the relationship of competition is weak but substantially there is to a short term and expression - the effect where the dollar rate fluctuation is big in Korean trade there is a possibility of saying that widely known it is not.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.533-542
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• 2021

The main objective of this article is to investigate the existence of the lunar effect during the full moon period (FM period) and the new moon period (NM period) on the selected Islamic stock market returns and volatilities. For this purpose, the Ordinary Least Squares model, Autoregressive Conditional Heteroscedasticity model, Generalised Autoregressive Conditional Heteroscedasticity model and Generalised Autoregressive Conditional Heteroscedasticity-in-Mean model are employed using the mean daily returns data between January 2010 and December 2019. Next, the log-likelihood, Akaike Information Criterion and Schwarz Information Criterion value are analyzed to determine the best models for explaining the returns and volatility of returns. The empirical results have deduced that, during the NM period, excluding Malaysia, the total mean daily returns for all of the selected countries have increased mean daily returns in contrast to the mean daily returns during the FM period. The volatility shocks are intense and conditional volatility is persistent in all countries. Subsequently, the volatility behavior tends to have lower volatility during the FM period and NM period in the Islamic stock market, except Malaysia. This article also concluded that the ARCH (1) model is the preferred model for stock returns whereas GARCH-M (1, 1) is preferred for the volatility of returns.

• The Journal of Fisheries Business Administration
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• v.49 no.2
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• pp.21-35
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• 2018

Squid is a popular seafood in Korea. However, since the 2000s, the squid production has been declining. The unstable supply of the squid products may cause price fluctuations of fresh and chilled squid. These price fluctuations may be relatively more severe than them of other commodities, because the fresh and chilled squid can not be stored for a long period of time. Thus, this study analyzes the structural characteristics of price volatility and price asymmetry of fresh squid based on off-diagonal GARCH model. Data used to analysis of this study are daily wholesale and retail prices of fresh squid from January 1, 2006 to December 31, 2016 provided in the KAMIS. As theoretical approaches of this study, first of all, the stability of the time series is confirmed by the unit root test. Secondly, the causality between distribution channels is checked by the Granger causality test. Thirdly, the VAR model and the off-diagonal GARCH model are adopted to estimate asymmetry effect and price volatility spillover between distribution channels. Finally, the stability of the model is confirmed by multivariate Q-statistic and ARCH-LM test. In conclusion, fresh squid is found to have shock and volatility spillover between wholesale and retail prices as well as its own price. Also, volatility asymmetry effect is shown in own wholesale or retail price of fresh squid. Finally, this study shows that the decrease in the fresh squid retail price of t-1 period than the increase in the t-1 period has a greater impact on the volatility of the fresh squid wholesale price in t period.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.3015-3024
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• 2018

This study investigates the recent asymmetric effect of news on stock return volatility in Asian five stock markets - Japan, Korea, Singapore, Taiwan, and Malaysia - since 2000. This study uses the GJR-M model which shows a different effect of a good and bad news on volatility. Empirical results show that the unexpected negative return has a more crucial effect on stock return volatility than the unexpected positive one does in all five stock markets. This implies that the bad news of the stock markets gives a more remarkable effect on volatility than good news does. This study finds that it is very important for market participants and regulation practitioners to distinguish between positive and negative return shocks in the stock markets since bad news might have a larger impacts on volatility than good news.

• International Area Studies Review
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• v.21 no.1
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• pp.181-200
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• 2017

This study investigates the impact of exchange rate and exchange rate volatility on the stock prices of eight industries from 2006 to 2015. The first and second exchange rate exposure of these eight industries is estimated with respect to four different exchange rates, namely the US dollar, Japanese yen, European currency unit, and British pound. In exchange rate exposure, stock prices in foods-beverages, paper-wood, electricity-gas, and banks industries are negatively related to exchange rate, whereas stock prices in electrical-electronic equp. and transport-equp. industries are positively related to exchange rate as expected. However stock price in machinery industry is negatively related to exchange rate, which is opposite to the expectation. Negative relationship is found between stock price in chemicals industry and exchange rate. In exchange rate volatility exposure, stock price in paper-wood industry is found to be negatively related to exchange rate volatility. Stock price in banks industry is also negatively related to exchange rate volatility. This result is opposite as expected, because banks are supposed to get more revenue by issuing derivatives related to foreign exchange when exchange rate volatility increases.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.129-139
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• 2023

Forecasting volatility is essential to avoiding the risk caused by the uncertainties of an financial asset. Complicated financial volatility features such as ambiguity between non-stationarity and stationarity, asymmetry, long-memory, sudden fairly large values like outliers bring great challenges to volatility forecasts. In order to address such complicated features implicity, we consider machine leaning models such as LSTM (1997) and GRU (2014), which are known to be suitable for existing time series forecasting. However, there are the problems of vanishing gradients, of enormous amount of computation, and of a huge memory. To solve these problems, a causal temporal convolutional network (TCN) model, an advanced form of 1D CNN, is also applied. It is confirmed that the overall forecasting power of TCN model is higher than that of the RNN models in forecasting VIX, VXD, and VXN, the daily volatility indices of S&P 500, DJIA, Nasdaq, respectively.

• Management & Information Systems Review
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• v.34 no.3
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• pp.17-39
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• 2015

This study examined the structural changes and volatility in the global stock markets using a Markov Regime Switching ARCH model developed by the Hamilton and Susmel (1994). Firstly, the US, Italy and Ireland showed that variance in the high volatility regime was more than five times that in the low volatility, while Korea, Russia, India, and Greece exhibited that variance in the high volatility regime was increased more than eight times that in the low. On average, a jump from regime 1 to regime 2 implied roughly three times increased in risk, while the risk during regime 3 was up to almost thirteen times than during regime 1 over the study period. And Korea, the US, India, Italy showed ARCH(1) and ARCH(2) effects, leverage and asymmetric effects. Secondly, 278 days were estimated in the persistence of low volatility regime, indicating that the mean transition probability between volatilities exhibited the highest long-term persistence in Korea. Thirdly, the coefficients appeared to be unstable structural changes and volatility for the stock markets in Chow tests during the Asian, Global and European financial crisis. In addition, 1-Step prediction error tests showed that stock markets were unstable during the Asian crisis of 1997-1998 except for Russia, and the Global crisis of 2007-2008 except for Korea and the European crisis of 2010-2011 except for Korea, the US, Russia and India. N-Step tests exhibited that most of stock markets were unstable during the Asian and Global crisis. There was little change in the Asian crisis in CUSUM tests, while stock markets were stable until the late 2000s except for some countries. Also there were stable and unstable stock markets mixed across countries in CUSUMSQ test during the crises. Fourthly, I confirmed a close relevance of the volatility between Korea and other countries in the stock markets through the likelihood ratio tests. Accordingly, I have identified the episode or events that generated the high volatility in the stock markets for the financial crisis, and for all seven stock markets the significant switch between the volatility regimes implied a considerable change in the market risk. It appeared that the high stock market volatility was related with business recession at the beginning in 1990s. By closely examining the history of political and economical events in the global countries, I found that the results of Lamoureux and Lastrapes (1990) were consistent with those of this paper, indicating there were the structural changes and volatility during the crises and specificly every high volatility regime in SWARCH-L(3,2) student t-model was accompanied by some important policy changes or financial crises in countries or other critical events in the international economy. The sophisticated nonlinear models are needed to further analysis.