• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.

• 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 Korea Trade
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• v.23 no.7
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• pp.147-155
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• 2019

Purpose - As the U.S.-China trade war has become considerably worse, the Chinese government is considering applying non-tariff barriers to trade, especially local contents rule. The main purpose of this research is to check whether it is suitable for Korean investors to invest in the current Chinese capital market. Design/methodology - In order to check the stability of the recent Chinese capital market, we investigated the behavior of foreign equity investment (including Korean equity investment) in the Chinese capital market after China announced the Shanghai-Hong Kong Stock Connect (SH-HK Connect). In this paper, we researched whether international portfolio investment would or would not contribute to an increase the volatility of an emerging market's stock market (Chinese capital market) when foreign investors make investment decisions based on the objective of short-term gains by rushing into countries whose markets are booming and fleeing from countries whose markets are falling. Findings - The empirical results indicate that foreign investors show strong, negative feedback trading behavior with regard to the stock index of the Shanghai Stock Exchange (SSE), and when the performance of foreign investors in the Chinese stock market was fairly good. Also, we found evidence that the behavior of foreign investors significantly decreased volatility in SSE stock returns. Consequently, the SH-HK Connect brought on a win-win effect for both the Chinese capital market and foreign investors. Originality/value - It appeared that the Chinese capital market was very suitable for Korean investors after the China's declaration of the SH-HK Connect. However, the win-win effect was brought on by the Chinese government's aggressive capital control but the capital controls could possibly cause financial turmoil in the Chinese capital market. Therefore, Chinese reform in industrial structure and the financial sector should keep pace with suitable capital control policies.

• Land and Housing Review
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• v.12 no.3
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• pp.27-37
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• 2021

This paper empirically examined the relationship between the housing market and the stock market to investigate the price and the asymmetric volatility spillover effects. The monthly housing price index and the monthly KOSPI were used for analysis. This research employed the EGARCH model. The analysis period was from January 1986 until June 2021 with periodization centered on the Asian Financial Crisis: before and after the crisis - the end of December 1997. The EGARCH model allows analysis of 'good news' and 'bad news' in understanding volatility. The price spillover effect was observed one way from the stock market to the housing market. On the contrary, the spillover effect was not found from the housing market to the stock market. The empirical evidence suggests that there are price and asymmetric volatility effects in the entire period of analysis in both housing and the stock markets. In the housing market, the negative effects of information were found pre-financial crisis while the positive effects, in other periods. However, in the stock market, the negative effects of information were found in the pre- and post-financial crisis periods. This means that the housing market is more affected by 'good news' than 'bad news' when information spreads to the markets while the stock market is more affected by 'bad news' than 'good news'. It is of significance to discover the variable returns by different information.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.59-71
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• 2022

Purpose - We investigate how firms transfer financial risks to employees in a form of flexible employment contracts and layoffs. Design/methodology/approach - Based on the literature on the prevalence of shareholder value ideology and the associated 'risk shift', we examined how stock price volatility is associated with a firm's use and hiring of nonstandard employees, and the number of employees lay-offed. We test our hypotheses using a longitudinal, multi-source, dataset of Korean firms from 2003 to 2011. Findings - We found support for the relationship between stock price volatility and flexible employment contracts and layoffs after controlling for actual risks such as increased debt or decreased sales. However, we found that the relationship is moderated by the power of professional CEOs relative to that of shareholders, in that powerful CEOs are more likely to transfer the external risks, i.e. stock price volatility, to employees. Research implications or Originality - This study contributes the emerging stream of literature that explore the effect of stock market pressures and governance structures on human resource management.

• Management & Information Systems Review
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• v.38 no.3
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• pp.1-19
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• 2019

This paper investigates the asymmetric effects of crude oil price uncertainty on industrial stock returns under different market conditions (bearish and bullish stock markets). We consider a quantile regression method using monthly oil volatility index, KOSPI and 22 industrial stock indices from May 2007 to February 2019. Especially, we take care of the positive and negative changes of the oil volatility index to analyze asymmetric effects of the oil price uncertainty for the bearish and bullish stock market conditions. During the bearish markets, the oil volatility index has relatively strong statistically significant negative effects on the industrial stock returns. These effects gradually decrease when the market conditions became more bullish markets. In particular, positive changes in the oil volatility index yields a further significant decrease in 12 industrial stock returns during the extreme bearish markets. Moreover, during the bullish markets, negative changes in the oil volatility index have statistically significant negative effects on the 12 industrial stock returns. From the empirical results, we see that participants of the Korean stock market are sensitive to bad news in a recession.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.189-195
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• 2014

In this research, we investigate the relationship between volatility and the trading volumes of trader types in the KOSPI 200 index stock market, futures market, and options market. Three types of investors are considered: individual, institutional, and foreign investors. The empirical results show that the volatility of the stock market and futures market are affected by the transaction information from another market. This means that there exists the cross-market effect of trading volume to explain volatility. It turns out that the option market volatility is not explained by any trading volume of trader types. This is because the option market volatility, VKOSPI, is the volatility index that reflects traders' expectation on one month ahead underlying volatility. Third, individual investors tend to increase volatilities, whereas institutions and foreign investors tend to stabilize volatilities. These results can be used in the areas of investment strategies, risk management, and financial market stability.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.101-133
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• 2006

We examine whether new derivatives on KOSPI 200 affect volatility asymmetry of KOSPI 200 portfolio, relative to the carefully matched non-KOSPI 200 portfolio. To test the effect or new derivatives trading, we use GJR-GARCH model and newly developed Volatility Ratio(down-market volatility to up-market volatility ratio). Our results show that KOSPI 200 portfolio experiences lower volatility asymmetry than non-KOSPI 200 portfolio after the trading of new derivatives on KOSPI 200, especially after the introduction of stock index options(KOSPI 200 options). For non-KOSPI portfolio, no significant reduction in volatility asymmetry occurred when trading of stock index options began. Also, we find that in the period of after January 1999, the period of after do-regulations and Financial Crisis in the Korean capital market, volatility asymmetry of stock markets was significantly decreased. This means that level of volatility asymmetry is closely related to the level of market regulations. Further, the results of the paper show that leverage effect and changes in foreign exchange ratio can be good candidates for explaining the stylized volatility asymmetry in the Korean stock market.

• Journal of Information Technology Services
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• v.11 no.3
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• pp.191-203
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• 2012

In the stock market, the investors rely on stock information to trade. Good information may stimulate buying, raising the stock prices and the bad information may result in selling, decreasing the stock prices. In terms of the relationship between information and stock prices, stock prices can be viewed as reaction of investors to all the information flowing into the market. The significant increase of web stock news volume is often associated with the significant changes of stock prices. When the web stock news volume for a firm increases significantly, the stock price movement is often oscillatory. This paper attempts to investigate the relationship between volumes of information from Korean web IT and stock prices in Korean stock market. This research shows that when the web stock news volume increases significantly, volatility, trading volumes and rate of returns are increase too. The results of the study provide us with the new clues to the microstructure of the stock market from the perspective of the web news.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.29-38
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• 2022

The enormous sway of COVID-19 on the international financial market has been felt across the globe. The financial markets of Bangladesh have also been similarly affected by the global epidemic and experienced a significant increase in volatility. To scrutinise the connection between COVID-19 and the Dhaka Stock Exchange (DSE) indices' return and instability, this study uses data of the DSE from February 2014 to September 2021. A comparative examination of the return and instability of the stock indices of the DSE has also been done considering the outbreak of the current COVID-19 situation. After using the GJR-GARCH (1,1) model, this review uncovers that the outbreak of COVID-19 has a statistically positive noteworthy association with the DSE stock indices' instability, which increases the market's volatility. Traders' fear and the rising frequency of COVID-19 reported patients could cause this. Besides, according to this study, COVID-19 shows a substantial positive linkage with stock market returns that increases the market's return. An appealing valuation, lower interest rates in the banking channel, economic rebound following the closure to prevent coronavirus transmission, improved remittance inflows, and a return of export revenues could all have contributed to this outcome. In addition, the findings also reveal that all market indices are in a mean-reverting phase.