• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.199-226
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• 2009

We examine the intertemporal relation between risk and return in the Korean stock market and foreign exchange market based on the two factor ICAPM framework. The standard GARCH model and the GJR(1993) model are employed to estimate conditional variances of the stock returns and foreign exchange rates. The covariance between the rates of stock returns and changes in the exchange rates are estimated by the constant conditional correlation model of Bollerslev(1990) and the dynamic conditional correlation model of Engle(2002). The multivariate GARCH in mean model and quasi-maximum likelihood estimation method, consequently, are applied to investigate riskreturn relation jointly. We find that the estimated coefficient of relative risk aversion is negative and statistically significant in the post-financial crisis sample period in the Korean stock market. We also show that the expected stock returns are negatively related to the dynamic covariance with foreign exchange rates. Both estimated parameters of conditional variance and covariance in the foreign exchange market, however, are not statistically significant. The GJR model is better than the standard GARCH model to estimate the conditional variances. In addition, the dynamic conditional correlation model has higher explanatory power than the constant correlation model. The empirical results of this study suggest following two points to investors and risk managers in hedging and diversifying strategies for their portfolios in the Korean stock market: first, the variability of foreign exchange rates should be considered, and second, time-varying correlation between stock returns and changes in foreign exchange rates supposed to be considered.

• The Korean Journal of Financial Management
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• v.19 no.2
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• pp.1-25
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• 2002

본 논문은 한국의 대표적인 두 시장, 코스닥시장과 증권거래소간의 투자전략을 비대칭적 정보가정에서 모형화하고, 정보상의 비대칭요소를 추정하여 최적포트폴리오를 구하는데 목적이 있다. 정보상에 비대칭적 시장들에서는 그 시장에 독특한 위험프리미엄이 존재하게 되는데, 이는 시장간의 효율적 포트폴리오를 구성하는데 있어서 비체계적 위험과 체계적 위험간의 구분이 불분명해지는 상황을 의미하며, 따라서 최적포트폴리오도 보다 복잡한 구조를 갖게 된다. 본 연구는 분석적 차원에서 정보상에 비대칭적인 것으로 가정되는 두 자본시장, 즉 코스닥시장과 증권거래소에서의 최적포트폴리오 전략을 살펴보고, 두 시장간의 비대칭성이 주는 의미와 그것이 최적 투자정책에 미치는 영향의 정도를 분석하고, 비대칭적인 두 시장에서의 균형조건을 살펴본다. 먼저, 최적포트폴리오 전략을 살펴보고, 자산가격 움직임에 있어서 차익거래의 영향을 분석하며 이에 대한 정보상으로 비대칭적인 두 시장간의 포트폴리오 전략과의 관계도 분석하였다. 이어서 비대칭적 상황을 발생시키는 다양한 요소들을 살펴보며 이와 같은 정보비대칭요소가 기대수익수준에 미치는 영향을 분석하기 위해 확률과정의 최적화 방법을 이용하여 추정하였다. 따라서 관련정보를 보유하지 못한 일반투자자들의 경우, 이 추정치를 이용하여 새로운 포트폴리오를 구성하게 될 것이다. 이 또한 불편추정치로서 최적포트폴리오로서의 역할을 하고 있는 것으로 해석된다. 그러나 비대칭요소 추정치의 정확도를 높이기 위해서는 정보를 확보하여야 하며 이에 따라 정보비용도 증가하게 되며 이 또한 최적포트폴리오의 수익성에 영향을 미치게 된다. 따라서 정보비용수준을 포트폴리오의 수익성을 고려하여 결정하여야 하며 이 점에 있어서 본 연구는 어떤 조건하에서 정보비용을 감안한 코스닥시장에의 투자결정이 이루어질 수 있는지를 분석하고 그 기준을 확률적 방법을 이용하여 제시하였다.

• Management & Information Systems Review
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• v.35 no.3
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• pp.195-212
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• 2016

Utilizing intra-day volume weighted average price (VWAP) based on 1 minute return data of stocks traded on the Korean Stock Exchange, this paper examines and analyzes abnormal returns in reaction to patent listing disclosures as well as the cumulative abnormal returns, traded volumes, the interaction of VWAP spreads, the reaction of volumes, the reaction of VWAP spreads and the realized returns obtained from trading using an event driven arbitrage strategy. The results of the aforementioned research topics are follows. First, our analysis suggests that on average, 0.92% positive cumulative returns arise 1 minute after the patent listing disclosure announcement with high statistical significance, thereby reconfirming that the Korean stock market is a semi-strong form of the efficient market. Employing 3 separate panel tests differentiated by the size factor, we find that the abnormal returns of small sized stocks were less than the returns of medium sized stocks, which goes to support recent research findings suggesting that the size premium is no longer existent in the Korean stock market. Secondly, we show that among the event driven type strategies, the most outstanding realized returns are from the market making strategies. Furthermore, placing market order trades only at the bid or ask price resulted in negative returns. This implies that strategies utilizing a combination of market orders and limit orders, order cancelations ratios and order flows can enhance realized returns.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1118-1125
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• 2014

The past IMF foreign exchange crisis and subprime financial crisis had a big influence on variability of macroeconomics, even if the origin of its occurrence might be different. This not only had a significant infrequence on the overall industries, but also produced many insolvent companies by being closely linked with a management environment of an individual construction company leading the construction industry. The purpose of this research is to investigate characteristics of management condition of construction company according to the size of construction company using KMV model developed on the basis of the Black & Scholes option pricing theory. This research has set 28 construction companies listed to KOSPI/KOSDAQ for applying the KMV model and measuring the level of the default risk of construction companies. The data was retrieved from TS2000 established by Korea Listed Companies Association (KLCA), Statistics Korea. The analysis period is between first quarter of 2004 and fourth quarter of 2010. This research examine characteristics of the level and fluctuation process of the management condition of construction company according to the size of construction company.

• The Korean Journal of Financial Management
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• v.25 no.1
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• pp.23-49
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• 2008

We test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. And, the aim of this paper is related to forecast the stock market, business cycle index and industrial production by various indicators of economic activities in Korea. For this, our paper sets models and focuses on empirical test. The stock market on this month correlate with industries in Korea. The stock market doesn't lead to industries. The industries and macroeconomic variables have high correlation. We test that gradual diffusion of industrial information will predict stock market in Korea. For this, we analysis on possibility of Granger cause by VAR models between industries and stock market. As a result, 21 portfolios cause to Kospi statistically significance at 5%. Especially, the Beverage portfolio has bilateral Granger causality to Kospi. In case of Internet and Cosmetics portfolio, Kospi has unilateral Granger causality to it. The predictability of specific industries has a relation to Macroeconomic variables. What industrial portfolios predict to Business Coincidence Index? The only 6 industrial portfolios of 36 portfolios have a statistically significance at 10%. And, 9 portfolios have a statistically significance at 5%.

• The Korean Journal of Financial Management
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• v.10 no.2
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• pp.303-317
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• 1993

Banz(1981)와 Reinganum(1981)에 의한 실증연구에 의한 소기업과 대기업간의 수익률차이는 자본자산가격결정모형(CAPM)에 의하여 설명될 수가 없는 결과 즉, 과거의 체계적 위험과 기업규모를 기준으로 보면 도구변수(instrumental variable)인 기업규모는 추정된 CAPM의 베타에 대하여 통제한 연후에도 포트플리오간의 평균수익률에 대하여 통계적으로 유의적인 설명력을 가진다는 것이다 이것은 주식의 위험조정후수익률(risk-adjusted return)이 기업규모와 부(負)의 관계에 있다는 것으로서, CAPM으로서는 설명되지 않는 이상(異常)수익률 현상이다. Banz와 Reinganum 이후 미국학계에서 그동안 수많은 연구들이 규모효과에 대한 설명을 시도하였으나 아직도 완전한 설명은 존재하지 않고 있다. 본 연구는 우리나라 주식시장에서의 규모효과 존재여부에 관한 기존의 몇몇 연구들이 갖고 있는 방법론상의 문제점들을 제거함으로써 규모효과의 존재여부를 새로이 검증하였다. 특히, 동일가중지수수익률(equal-weighted index return)은 효율적 포트폴리오수익률이 나 종합주가지수수익률은 비효율적 포트폴리오수익률이라는 황선웅 이일균(1991)의 연구 결과를 고려하여, 시장수익률 대용치로서 종합주가지수수익률을 사용할 경우 규모효과의 검증결과가 어떠한 영향을 받는지도 아울러 분석하였다. 1980-90년의 기간을 대상으로 하여 실증분석한 결과, 먼저 동일가중지수수익률을 시장수익률로 사용할 경우 체계적위험 추정치와 기업규모간에는 부(負)의 관계가 존재하고 있음이 관측되고 있으며, 기업규모포트폴리오의 초과수익률 추정치도 대형주는 물론 소형주의 경우에도 통계적으로 유의하게 영(零)과 다르지 않다. 그 결과 최소한 1980-90년의 경우 우리나라 주식시장에는 규모효과가 존재하였다는 실증적 증거가 발견되지 않는다. 그러나 종합주가 지수수익률을 시장수익률로 사용하면 소형주에 대한 체계적위험이 대형주의 경우보다 오히려 작게 나타나고 있으며, 그에 따라 통제적으로 유의한 규모효과가 존재하는 것처럼 나타나고 있어 종합주가지수수익률은 시장수익률 대용치로 적절하지 않음을 제안하고 있다.

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.199-215
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• 2020

Purpose - This article tries to test if the conditional consumption capital asset pricing model (CCAPM) with bank credit for household as a conditional variable can explain the cross-sectional variation of stock returns in Korea. The performance of conditional CCAPM is compared to that of multifactor asset pricing models based on Arbitrage Pricing Theory. Design/methodology/approach - This paper extends the simple CCAPM to the conditional version of CCAPM by using bank credit for household as conditioning information. By employing KOSPI and KOSDAQ stocks as test assets from the second quarter of 2003 to the first quarter of 2018, this paper estimates risk premiums of conditional CCAPM and a variety of multifactor linear models such as Fama-French three and five-factor models. The significance of risk factors and the adjusted coefficient of determination are the basis for the comparison in models' performances. Findings - First, the paper finds that conditional CCAPM with bank credit performs as well as the multifactor linear models from Arbitrage Pricing theory on 25 test assets sorted by size and book-to-market. When using long-term consumption growth, the conditional CCAPM explains the cross-sectional variation of stock returns far better than multifactor models. Not only that, although the performances of multifactor models decrease on 75 test assets, conditional CCAPM's performance is well maintained. Research implications or Originality - This paper proposes bank credit for household as a conditional variable for CCAPM. This enables CCAPM, one of the most famous economic asset pricing models, to conform with the empirical data. In light of this, we can now explain the cross-sectional variation of stock returns from an economic perspective: Asset's riskiness is determined by its correlation with consumption growth conditional on bank credit for household.

• Management & Information Systems Review
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• v.37 no.4
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• pp.63-74
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• 2018

Recently, in the asset management industry, the smart beta strategy, which has an intermediate nature between passive and active strategies, is attracting attention. In this smart beta strategy, value, momentum, low volatility, and quality index are widely used. In this study, we analyzed the quality index which is not clear and complicated to calculate. According to the MSCI methodology, the quality index was calculated using three variables: return on equity, debt to equity, and earnings variability. In addition, we use the index using only return on equity variable, the index using only two variables of return on equity and debt to equity, and the KOSPI index as comparison targets for the quality index. In order to evaluate the performance of the indices used in the analysis, the arithmetic mean return, the coefficient of variation, and the geometric mean return were used. In addition, Fama and French (1993) model, which is widely used in related studies, was used as a pricing model to test whether abnormal returns in each index are occurring. The results of the empirical analysis are as follows. First, in all period analysis, quality index was the best in terms of holding period returns. Second, the quality index performed best in the currency crisis and the global financial crisis. Third, abnormal returns were not found in all indices before the global financial crisis. Fourth, in the period after the global financial crisis, the quality index has the highest abnormal return.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.55-80
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• 2009

Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.