고저변동성을 활용한 변동성 예측: 한국과 미국시장을 중심으로
Volatility Forecasting Using High-Low Volatility in Korea and the U.S.
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본 연구에서는 미래변동성에 대한 예측 수단으로 고저변동성을 제시한다. 고저변동성은 투자자가 형성한 고점과 저점을 사용하여 산출되기 때문에 실제 시장참여자의 의견을 잘 반영하게 된다. 투자자의 거래과정에서 형성되는 옵션가격에 내재되어 있는 내재변동성은 투자자의 의견이 직접적으로 반영된 변동성이다. 하지만 역사적변동성은 기초자산의 종가를 사용한 수익률의 평균과 표준편차를 활용하여 계산된 변동성이기 때문에 투자자의 장중 의견을 반영하지 못하는 한계가 있다. 이러한 관점에서 볼 때 고저변동성이나 내재변동성의 미래변동성에 대한 예측력은 역사적변동성보다 우위를 점할 수 있을 것으로 예상된다. 고저변동성을 활용한 변동성 예측을 살펴보기 위해 한국과 미국시장의 변동성 변수를 고려하여 GARCH 모형 등을 활용한 실증분석을 수행한 결과, t-1시점의 S&P500 고저변동성은 t시점의 KOSPI200 내재변동성에 대해 유의한 양(+)의 영향을 미친다는 결과를 발견하였다. 하지만 t-1시점 S&P500 역사적변동성이 KOSPI200 내재변동성에 대해 미치는 영향력은 분명하지 않았다. 이는 변동성 관리에 고저변동성을 함께 사용할 경우 시장의 변동성 위험을 보다 안정적으로 관리할 수 있다는 점을 제시한다. 본 연구에서 사용한 고저변동성의 산출방식은 직관적이고 실제 투자전략에 활용하기 용이한 장점이 있는 반면 후속연구에서 이론의 전개에 관한 보완이 필요할 수 있다. 본 연구결과에서 제시한 고저변동성의 효용성은 투자전략의 추가적인 변동성 예측지표로서의 시사점을 제공한다는 의의가 있다.
Abstract
In this study, we suggest the high-low volatility as a means of predicting future volatility. The high-low volatility well reflects the opinions of real market participants, as it is calculated based on the highs and the lows formed by investors in trading; the implied volatility inherent in the option price formed during investors’ trading process directly reflects investors’ opinions well. On the other hand, the historical volatility, which is calculated based on the average and the standard deviation of the rate of returns utilizing the underlying asset’s closing price, has limitations in well-reflecting investors’ intraday opinions. In this respect, it is expected that both the high-low volatility and the implied volatility can predominate historical volatility in predicting future volatility. As a result of an empirical analysis using the VAR and GARCH model in consideration of volatility variables in Korea and the United States to examine the variability using the high-low volatility, we found that the high-low volatility of the S&P500 at time t-1 had a significant positive (+) effect on the implied volatility of the KOSPI200 at time t. However, the effect of the S&P500’s historical volatility at time t-1 on KOSPI200’s implied volatility was unclear. This suggests that the use of high-low volatility can give more stability in managing volatility risks in the market.
참고문헌 (40)
- 안희준, "기업고유 변동성과 체계적 변동성이 유동성에 미치는 효과", 재무연구, 30(1), pp.1~32 (2017), 한국재무학회[DOI]
- 엄영호, "변동성지수의 미래예측력에 대한 연구", 금융연구, 22(3), pp.1~33 (2008), 한국금융학회
- 염명훈, "가격발견지수의 제안과 가격발견요인에 관한 연구", 金融工學硏究, 12(4), pp.1~25 (2013), 한국금융공학회[DOI]
- 염명훈, "한국과 미국 금융시장 간의 변동성 동조화현상에 관한 연구", 한국증권학회지, 43(1), pp.213~236 (2014), 한국증권학회
- 옥기율, "선물거래가 현물시장의 유동성에 미치는 영향에 관한 연구", 선물연구, 13(1), pp.25~43 (2005), 한국파생상품학회
- 정정현, "KOSPI 200 지수의 실현변동성 예측에서의 내재변동성 측정오차의 영향", 金融工學硏究, 7(3), pp.33~54 (2008), 한국금융공학회[DOI]
- Admati, A. R., "A theory of Intraday Patterns : Volume and Price Variability", Review of Financial Studies, 1(1), pp.3~40 (1988)
- Alizadeh, S., "Range-based Estimation of Stochastic Volatility Models", Journal of Finance, 57(3), pp.1047~1091 (2002)
- Balta, N., "Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations, Market Momentum: Theory and Practice Chapter 3" (2012), Wiley
- Bollerslev, T., "Generalized Autoregressive Conditional Heteroskedasticity", Journal of Econometrics, 31(3), pp.307~327 (1986)
- Brandt, M. W., "A No-arbitrage Approach to Range based Estimation of Return Covariances and Correlations", Journal of Business, 79, pp.61~74 (2006)
- Brandt, M. W., "Volatility Forecasting with Range-Based EGARCH Models", Journal of Business and Economic Statistics, 24(4), pp.470~486 (2006)
- Caporale, G. M., "Investor’s Trading Behavior and Stock Market Volatility during Crisis Periods : A Dual Long-Memory Model for the Korean Stock Exchange", International Journal of Finance and Economics, 26(3), pp.4441~4461 (2020)
- Chou, R. Y., "The Economic Value of Volatility Timing using a Range-based Volatility Model", Journal of Economic Dynamics and Control, 34(11), pp.2288~2301 (2010)
- Chow, G. C., "Tests of Equality Between Sets of Coefficients in Two Linear Regressions", Econometrica, 28(3), pp.591~605 (1960)
- Christensen, B. J., "The Relation between Implied and Realized Volatility", Journal of Financial Economics, 50(2), pp.125~150 (1998)
- Corrado, C. J., "The Forecast Quality of CBOE Implied Volatility Indexes", Journal of Futures Markets, 25(4), pp.339~373 (2005)
- Corrado, C., "Forecasting Stock Index Volatility : Comparing Implied Volatility and the Intraday High-Low Price Range", Journal of Financial Research, 30(2), pp.201~215 (2007)
- Day, T. E., "Stock Market Volatility and the Information Content of Stock Index Options", Journal of Econometrics, 52(1), pp.267~287 (1992)
- De Nard, G., "Large Dynamic Covariance Matrices: Enhancements Based on Intraday Data", Journal of Banking & Finance, 138, pp.106426~ (2022)
- Engle, R. F., "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation", Econometrica, 50(4), pp.987~1007 (1982)
- Engle, R. F., "A Multiple Indicators Model for Volatility Using Intra-daily Data", Journal of Econometrics, 131(1), pp.3~27 (2006)
- Fiszeder, P., "Modeling and Forecasting Dynamic Conditional Correlations with Opening, High, Low, and Closing Prices", Journal of Empirical Finance, 70, pp.308~321 (2023)
- Fleming, J., "The Quality of Market Volatility Forecasts Implied by S&P100 Index Option Prices", Journal of Empirical Finance, 5(4), pp.317~345 (1998)
- Foster, F. D., "A Theory of the Interday Variations in Volume, Variance, and Trading Costs in Securities Markets", Review of Financial Studies, 3(4), pp.593~624 (1990)
- Garman, M. B., "On the Estimation of Security Price Volatilities from Historical Data", Journal of Business, 53(1), pp.67~78 (1980)
- Granger, C. W., "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods", Econometrica, 37(3), pp.424~438 (1969)
- Granger, C. W., "Testing for Causality : A Personal Viewpoint", Journal of Economic Dynamics and Control, 2, pp.329~352 (1980)
- Hung, J. C., "The Information Contents of VIX Index and Range-based Volatility on Volatility Forecasting Performance of S&P 500", Economics Bulletin, 29(4), pp.2592~2604 (2009)
- Hung, J. C., "Evaluating and Improving GARCH-based Volatility Forecasts with Range-based Estimators", Applied Economics, 45(28), pp.4041~4049 (2012)
- Kang, J., "An Empirical Investigation of the Lead-Lag Relations of Returns and Volatilities among the KOSPI200 Spot, Futures and Options Markets and their Explanations", Journal of Emerging Market Finance, 5(3), pp.235~261 (2006)
- Killick. R., "Optimal Detection of Changepoints with a Linear Computational Cost", JASA, 107(500), pp.1590~1598 (2012)
- Lai, Y-S., "Improving Hedging Performance by Using High–Low Range", Finance Research Letters, 48, pp.102975~ (2022)
- Lamoureux, C. G., "Forecasting Stock-Return Variance : Toward an Understanding of Stochastic Implied Volatilities", Review of Financial Studies, 6(2), pp.293~326 (1993)
- Louzis, D. P., "The Role of High-Frequency Intra-daily Data, Daily Range and Implied Volatility in Multi-period Value-at-Risk Forecasting", Journal of Forecasting, 32(6), pp.561~576 (2013)
- Molnar, P., "High-Low Range in GARCH Models of Stock Return Volatility", Applied Economics, 48(51), pp.4977~4991 (2016)
- Parkinson, M., "The Extreme Value Method for Estimating the Variance of the Rate of Return", Journal of Business, 53(1), pp.61~65 (1980)
- Vipul and J. Jacob, "Forecasting Performance of Extreme-Value Volatility Estimators", Journal of Futures Markets, 27(11), pp.1085~1105 (2007)
- Xie, H., "Timing the Market : The Economic Value of Price Extremes", Financial Innovation, 4, pp.1~24 (2018)
- Yang, D., "Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices", Journal of Business, 73(3), pp.477~492 (2000)