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기업변동성과 주식수익률의 횡단면에 관한 연구

  • 윤상용 연세경영연구소 전문연구원
  • 구본일 연세대학교 경영대학 교수
  • 엄영호 연세대학교 경영대학 교수
본 연구에서는 국내 주식시장에서 기업변동성 즉, 기업고유의 변동성과 총변동성이 주식수익률에 횡단면적으로 어떠한 영향을 미치는지를 살펴보았다. 이를 위해 먼저 세 가지 주요 자산가격결정모형을 사용하여 추정한 기업고유위험이 주식수익률에 유 의한 영향력을 미치는지를 검증한 결과, 추정치들 모두 통계적으로 유의한 영향을 미 치지 못하는 것으로 나타났다. 그리고 이들 기업고유위험 추정치의 추정오류 포함 가 능성을 고려하여, 비모형 측정치인 총변동성과 주식수익률간의 관계를 살펴 본 결과 에서는, 총변동성이 가장 큰 포트폴리오가 가장 낮은 수익률을 가지는 것으로 나타나 주식수익률에 유의한 음(-)의 영향을 미치고 있는 것으로 나타났다. 사실, 국내 주식 시장에서 기업고유위험은 개별 주식수익률의 총변동성 중 상당부분을 차지하고 있음 을 감안할 때, 총변동성과 주식수익률간의 음(-)의 관계는 적어도 기업고유위험의 설명력이 유의하지 않다면 체계적 위험에 의한 것일 수 있음을 추측해 볼 수 있다. 과연 그러한지를 추가적으로 검증한 결과, 시장변동성의 변화에 민감하게 반응하는 주식들의 수익률들이 대체로 낮게 나타나고 있어, 이들 간의 음(-)의 횡단면적 관계 는 체계적 위험 부분에 의한 것임을 살펴볼 수 있었다.
기업변동성; 기업고유의 위험; 체계적 위험; 총변동성; 횡단면분석; Firm Volatility; Idiosyncratic Risk; Systematic Risk; Total Volatility; Cross-sectional Test

Empirical Investigation on the Relationship of Firm-Volatility and the Cross-section of Stock Returns

  • Sang Yong Yun
  • Bonil Ku
  • Young Ho Eom
The intertemporal relation between risk and return has long been an important topic in asset pricing literature. Most asset pricing models postulate a positive relationship between a stock portfolio’s expected returns and risk, which is often modeled by the variance or standard deviation of the portfolio’s returns, claiming that idiosyncratic risk has no significant effect on returns. However, no agreement has been met about the existence of such a trade-off for stock market indices. While return volatility is an intuitively appealing measure of risk, the difference approaches used by previous researchers suggest that no clear consensus has emerged regarding its relevance. Yet, whether investors require a larger risk premium on average for investing in a security during times when the security is more risky remains an open question. This paper, therefore, makes a contribution by exploring the relationship between return and risk as proxied by firm-volatility, comprised of both systematic and idiosyncratic volatility. As for the predictability of stock market returns, Goyal and Santa- Clara(2003) propose a new approach to test the presence and significance of a time-series relationship between risk and return for the aggregate stock market. They find a positive relation between the equal-weighted average stock volatility and the value-weighted portfolio returns. They also show that the lagged volatility of market returns has no predictive power for the expected return on the market. Bali, et al.(2005), on the other hand, find that Goyal and Santa-Clara’s empirical results based on the equal-weighted average stock risk are not robust across different stock portfolios and sample periods. That is, their conclusions do not hold when either the more natural value-weighted measure of average stock risk or the more robust median stock volatility is used in predictive regressions. Ang, Hodrick, Xing and Zhang(2006) further suggest that volatility of market return is a priced cross-sectional risk factor based on their observation that US stocks with high lagged idiosyncratic volatility earn very low future average returns, and these assets are indeed mispriced when applying the Fama-French model. Their results of the negative relationship between idiosyncratic volatility and expected returns are surprising for two reasons. First, the difference in average returns across stocks with low and high idiosyncratic volatility is rather large. Second, their findings cannot be explained by either exposure to aggregate volatility risk or other existing asset pricing models. On the other hand, in order to capture the time-varying property of idiosyncratic risk, Fu(2009) uses the exponentially generalized autoregressive conditional heteroskedasticity (EGARCH) models and out-of-sample data to estimate expected idiosyncratic volatilities, and find that idiosyncratic risk is positively related to expected returns. Views on the relationship remain divergent: asset pricing theory implies that expected returns should be positively related to model-implied systematic volatility; various theoretical studies suggest that idiosyncratic volatility should be positively related to expected returns; and several empirical studies suggest that idiosyncratic volatility has explanatory power for the cross-section of expected returns. As such, this paper examines the explanatory power of idiosyncratic volatility estimated by three asset pricing models(CAPM, Fama-French 3 factor model, and Alternative 3 factor model suggested in Yun, et al.(2009)), and total volatility, a model-free quantity, for the cross-section of stock returns. Total volatility is the sum of systematic volatility relative to some asset pricing model and idiosyncratic volatility relative to the same model. As a result, no significant link between expected returns and idiosyncratic volatility in Korea stock market (KOSPI) data is traced while some cross-sectional evidence for a negative relationship between total volatility and expected return is detected. In addition, the portfolios of lower volatility stocks achieved a higher expected return than those of higher volatility stocks. We could ascertain that the effect is driven mainly by systematic volatility by applying AHXZ(2006)’s method. We also investigate the implications of the findings above for asset pricing and construct a total volatility factor via the factor mimicking portfolio for total volatility. Following the Fama and French(1992, 1993) model, we construct the factor mimicking portfolio as the zero cost portfolio which is long for the quintile of stock with lowest total volatility and short for the quintile with highest total volatility. We estimate the factor price of total volatility risk using the Fama and MacBeth(1973) procedure for individual stocks in our data. During the research period, the factor price of total volatility risk is positive and significant, indicating that the variation in systematic risk has notable implications for asset pricing. We also conclude based on the multi-factor models of risk that aggregate volatility should be a cross-sectional risk factor. Our finding is that the total volatility has a negative cross-sectional relationship with expected returns of individual stocks. Moreover, when we decompose the total volatility factor into systematic and idiosyncratic components, we find that the factor price of total risk is positive while that for idiosyncratic is insignificant. This negative relationship corroborates the results from the past research in option pricing that has shown a negative price of risk for systematic volatility, by reassuring that stocks with high past exposure to innovations in aggregate market volatility earn low future average returns.