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김현석 ( Hyunseok Kim ),여효성 ( Hyosung Yeo ) 한국금융학회 2017 금융연구 Vol.31 No.1
본 연구는 한국주식시장을 대상으로 배당수익률, 이익주가 비율(E/P 비율), 이자율, Yield Gap과 같은 금융변수의 주가수익률 예측력을 검정하였다. 이를 위해 예측회귀분석 시 발생 가능한 계량적 문제들을 효과적으로 처리하기 위하여 Choi, Jacewitz, and Park(2016)의 변동성 시계 표본을 이용한 Cauchy 추정법을 적용하여 실증분석을 수행하였으며, 주요한 분석 결과는 다음과 같다. 첫째, 주가수익률의 QV(quadratic variation)을 기반으로 변동성 시계로 변환한 모형에서는 어떤 변수도 주가수익률 예측력을 가지지 못하는 것으로 나타났지만, 주가수익률의 BPV(bipower variation)을 기반으로 변동성 시계로 변환한 모형에서는 배당수익률이 주가수익률 예측력을 가지는 것으로 나타났다. 둘째, 장단기 Yield Gap의 주가수익률 예측력 검정 결과, 장단기 Yield Gap 중 어느 변수도 주가수익률 예측력을 가지지 못하는 것으로 나타났다. 본 연구는 금융변수의 주가수익률 예측력 검정 시 나타날 수 있는 다양한 문제들을 새로운 계량적 기법을 사용하여 효과적으로 처리하였다는 점에서 기존연구와 차별성을 가진다. 또한 주가수익률에 대한 예측 변수로서 선진금융시장에서 흔히 사용되는 Yield Gap을 소개하고, 이의 주가수익률 예측가능성을 추가적으로 검정하였다는 점에서 보다 진일보된 실증분석 연구로서의 의미를 갖는다. In this paper, we examine stock return predictability in the Korean stock market using the random time Cauchy estimator. Various financial variables have been tested for stock return predictability, but the results remain inconclusive. In the Korean stock market, Kim and Kim (2004), Kim and Park (2009) and Chung and Kim (2010) try to find evidence of return predictability using the dividend-price ratio, earnings-price ratio, and interest rates, but their conclusions are mixed. More recently, Choi et al. (2016) propose a robust test of stock return predictability which combines the time-change method with a Cauchy estimator. They show that the random time Cauchy test has an exact size of test and comparable power to Campbell and Yogo (2006) and Chen and Deo (2009)`s tests. We apply this new methodology to the KOSPI index and report the empirical results, providing comparison with other widely used tests for return predictability. Stock returns have nearly nonstationary stochastic volatility, and this causes substantial size distortions on standard tests relying on constant variance. To handle this nonstationary stochastic volatility in returns, we implement a simple time change. For the required time change, “we wait for volatility to reach a certain threshold before collecting each observation such that there is a constant level of volatility across all observations in our sample.”(Campbell and Yogo, 2006) Using the daily KOSPI index, we first test the presence of discrete jumps in our sample. We detect a total of 15 jumps between January 2001 and December 2014 with the windows size 16 and a 5% significance level, using Lee and Mykland (2008) test. Depending on the presence of discrete jumps, we measure the realized variance and realized bi-power variation. When we allow for only the continuous martingale in the regression error, we remove the detected daily jumps and compute the total realized variance by squaring and summing up daily excess returns. When we allow our continuous time regression model to have discrete jumps, we compute the realized total bi-power variation without removing detected jumps. We have a total of 168 months in our sample span. The computed total realized variance (TQV) and total realized bi-power variation (TBPV) are then divided by the number of months and we set this as our volatility threshold △<sub>QV</sub> and △<sub>BPV</sub> for a time change with QV and a time change with BPV, respectively. Using the given volatility threshold, we add up daily squared excess returns until it hits our set △ and resample our regression variables on that date and repeat the process until we reach the TQV and TBPV. After correcting for stochastic volatility in returns, we implement a Cauchy estimator to handle the persistent endogeneity of covariates. The Cauchy estimator uses the sign transform of a covariate as an instrumental variable. “The advantage of using a Cauchy estimator is that the asymptotic normality of the Cauchy t-ratio holds regardless of any statistical anomalies in covariates including nonstationarity, fat-tailed innovations, structural breaks, and jumps.” (Choi et al., 2016) Thus, it is well suited for testing return predictability in cases where many commonly used covariates are highly persistent and correlated with the regression residuals. Inferences using the Cauchy t-ratio are also valid even in the presence of structural breaks and jumps. We test for stock return predictability in the Korean market using the log of dividend-price ratio, the log of earnings-price ratio, the riskfree rate, and short-term, medium-term and long-term yield gaps. For the dividend-price ratio, conventional OLS t-test over-rejects the null hypothesis of no predictability. We could not find evidence for predictability using other tests except the random time Cauchy with BPV. For the earnings- price ratio, none of our tests reject the null. The coefficients of risk-free rates are insignificant except for Campbell and Yogo (2006)`s BQ test where its 90% confidence interval does not include zero value. Using the Amihud and Hurvich (2004) and Campbell and Yogo (2006)`s test, shortterm yield gap has predictive power. But using the Chen and Deo (2009)`s chi-square 1 and random time Cauchy t-test, we could not reject the null. After correcting for persistent endogeneity in the medium-term yield gap, evidence for return predictability disappears, and all our tests except for OLS t-test cannot reject the null. Finally, we could not find any evidence for predictability using the long-term yield gap.
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