• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7672-7676
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• 2015

This study examines the dynamic relations between housing price and trading volume in a set of apartment markets in Republic of Korea to explore the informational role of trading volume in predicting the price volatility. Using monthly index data, EGARCH model is utilized to test for volume effect. To estimate the EGARCH-based volatility, two different sets of region are applied for the monthly return. Strong evidence has been found towards housing turnover leading price volatility, this supports previous studies on financial sector(s). These findings also support that trading volume in the housing market contains information on investor sentiment which, in turn, has a valuation effect on the price.

• East Asian Economic Review
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• v.19 no.1
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• pp.3-38
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• 2015

Fama's (1984) volatility relations show that the risk premium in foreign exchange markets is more volatile than, and is negatively correlated with the expected rate of depreciation. This paper studies these relations from the perspective of goods markets frictions. Using a sticky-price general equilibrium model, we show that near-random walk behaviors of both exchange rates and consumption, in response to monetary shocks, can be derived endogenously. Based on this approach, the paper provides quantitative results on Fama's volatility relations.

• 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.

• Management & Information Systems Review
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• v.32 no.4
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• pp.189-207
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• 2013

Using high-frequency data for 2 years, this study investigates intraday lead-lag relationship between stock index and stock index futures markets in Korea and China. We found that there are some differences in price discovery and volatility transmission between Korea and China after the stock index futures markets was introduced. Following Stoll-Whaley(1990) and Chan(1992), the multiple regression is estimated to examine the lead-lag patterns between the two markets by Newey-West's(1987) heteroskedasticity and autocorrelation consistent covariance matrix(HAC matrix). Empirical results of KOSPI 200 shows that the futures market leads the cash market and weak evidence that the cash market leads the futures market. New market information disseminates in the futures market before the stock market with index arbitrageurs then stepping in quickly to bring the cost-of-carry relation back into alignment. The regression tests for the conditional volatility which is estimated using EGARCH model do not show that there is a clear pattern of the futures market leading the stock market in terms of the volatility even though controlling nonsynchronous trading effects. This implies that information in price innovations that originate in the futures market is transmitted to the volatility of the cash market. Empirical results of CSI 300 shows that the cash market is found to play a more dominant role in the price discovery process after the Chinese index started a sharp decline immediately after the stock index futures were introduced. The new stock index futures markets does not function well in its price discovery performance at its infancy stage, apparently due to high barriers to entry into this emerging futures markets. Based on EGAECH model, the results uncover strong bi-directional dependence in the intraday volatility of both markets.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.203-213
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• 2016

We investigate volatility spillover aspects of realized volatilities (RVs) for the log returns of the Korea Composite Stock Price Index (KOSPI) and the Hang Seng Index (HSI) from 2009-2013. For all RVs, significant long memories and asymmetries are identified. For a model selection, we consider three commonly used time series models as well as three models that incorporate long memory and asymmetry. Taking into account of goodness-of-fit and forecasting ability, Leverage heteroskedastic autoregressive realized volatility (LHAR) model is selected for the given data. The LHAR model finds significant decompositions of the spillover effect from the HSI to the KOSPI into moderate negative daily spillover, positive weekly spillover and positive monthly spillover, and from the KOSPI to the HSI into substantial negative weekly spillover and positive monthly spillover. An interesting result from the analysis is that the daily volatility spillover from the HSI to the KOSPI is significant versus the insignificant daily volatility spillover of the KOSPI to HSI. The daily volatility in Hong Kong affects next day volatility in Korea but the daily volatility in Korea does not affect next day volatility in Hong Kong.

• International Area Studies Review
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• v.14 no.1
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• pp.99-119
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• 2010

This study explores the natural gas market and the oil market in the U.S. and the European oil market. It focuses on two kinds of analyses; one is to confirm whether there is the predictive power between spot and futures within homogeneous commodity market(or inter-heterogeneous commodity market) through Granger-causality test in terms of the return rate and the volatility. The other is to examine the spot price stabilizing effect of futures price through regression analysis. When it comes to the predictive power of inter-commodity market, there was a conflicting aspect between the return rate of spot and futures. Overall, however, its statistical significance was low. With respect to the volatility, we found that the natural gas market has little influence on the oil market unlike the predictive power of oil market on natural gas market. Concerning the return rate of the predictive power within homogeneous commodity market, we found that the return rate of spot has the predictive power on futures only in the European market. In addition, we identified that there is feedback between spot and futures in the all commodity markets regarding volatility. As a result of the spot price stabilizing effect analysis of futures price, futures volatility increased the spot volatility.

• Journal of Korea Port Economic Association
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• v.34 no.4
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• pp.125-140
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• 2018

This study uses daily price data on three major types of crude oil (Brent, Dubai, and WTI) and BDI from January 2, 2009 to June 29, 2018, to compare the relationship between crude oil prices and BDI for rate of change and volatility. Unlike previous studies, the correlation between BDI and crude oil prices was analyzed both the rate of change and variability, VARs, Granger Causality Test, and the GARCH and DCC models were employed. The correlation analysis, indicated that the crude oil price change rate and volatility affect the BDI change rate and that BDI volatility affects the crude oil price change rate and volatility. The relationship between oil prices and BDI is identified, but their correlation is low, which is likely a result of lower dependence on crude oil as demand for natural gas increases worldwide and demand for renewable energy decreases. These trends could result in lower correlations over time. Therefore, focusing on the changing demand for raw materials in future investments in international shipping(real economy) and oil markets and macroeconomic analysis is necessary.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.57-62
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• 2023

This research is a comparative analysis of the U.S. S&P 500 index using the volatility breakout strategy against the Buy and Hold approach. The volatility breakout strategy is a trading method that exploits price movements after periods of relative market stability or concentration. Specifically, it is observed that large price movements tend to occur more frequently after periods of low volatility. When a stock moves within a narrow price range for a while and then suddenly rises or falls, it is expected to continue moving in that direction. To capitalize on these movements, traders adopt the volatility breakout strategy. The 'k' value is used as a multiplier applied to a measure of recent market volatility. One method of measuring volatility is the Average True Range (ATR), which represents the difference between the highest and lowest prices of recent trading days. The 'k' value plays a crucial role for traders in setting their trade threshold. This study calculated the 'k' value at a general level and compared its returns with the Buy and Hold strategy, finding that algorithmic trading using the volatility breakout strategy achieved slightly higher returns. In the future, we plan to present simulation results for maximizing returns by determining the optimal 'k' value for automated trading of the S&P 500 index using artificial intelligence deep learning techniques.

• Korean System Dynamics Review
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• v.8 no.2
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• pp.175-190
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• 2007

Stock market volatility largely depends on firms' value and growth opportunities. However, with the globalization of world economy, the effect of the synchronization in major countries is gaining its importance. Also, domestically, the business cycle and cash market of the country are additional factors needed to be considered. The main purpose of this research is to attest the application and usefulness of System Dynamics as a general stock market forecasting tool. Throughout this research, System Dynamics suggests a conceptual model for forecasting a KOSPI(Korea Composite Stock Price Index), taking the factors of the composite stock price indexes in traditional researches. In conclusion of this research, System Dynamics was proved to bean appropriate model for forecasting the volatility and direction of a stock market as a whole. With its timely adaptability, System Dynamic overcomes the limit of traditional statistic models.

• The Pure and Applied Mathematics
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• v.21 no.4
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• pp.295-305
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• 2014

Yan & Hanson [8] and Makate & Sattayatham [6] extended Bates' model to the stochastic volatility model with jumps in both the stock price and the variance processes. As the solution processes of finding the characteristic function, they sought such a function f satisfying $$f({\ell},{\nu},t;k,T)=exp\;(g({\tau})+{\nu}h({\tau})+ix{\ell})$$. We add the term of order ${\nu}^{1/2}$ to the exponent in the above equation and seek the explicit solution of f.