은퇴 시점과 예측 변동성을 고려한 동적 Glide Path
Dynamic Glide Path using Retirement Target Date and Forecast Volatility
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초록
본 연구의 목적은 투자자의 은퇴 시점뿐만 아니라 시장의 예측 변동성을 동시에 고려하여 Target Date Fund의 위험자산 편입 비율을 동적으로 조정하는 새로운 Glide Path를 제안하고, 은퇴 시점만 고려하여 위험자산 편입 비율이 정해지는 전통적 Glide Path와 투자 성과를 비교 분석하는 것이다. 시장 변동성의 예측치로는 역사적 변동성, 시계열모형인 GARCH 변동성, 그리고 변동성지수인 VKOSPI를 활용하였으며, 2003년부터 2020년까지의 분석 기간에서 변동성을 고려하는 새로운 동적 Glide Path의 투자 성과가 우수함을 보여주었다. 3가지 변동성 예측모형 모두에서 은퇴 시점만을 고려하는 Glide Path보다 수익률은 더 높고 위험은 더 낮아지면서 투자 성과 지표인 Sharpe Ratio가 개선되었다. 실증 분석 결과는 은퇴예정자뿐만 아니라 Target Date Fund 운용업계에 새로운 Glide Path의 활용 가능성을 제시하고 있다.
Abstract
The objective of this study is to propose a new Glide Path that dynamically adjusts the risky asset inclusion ratio of the Target Date Fund by simultaneously considering the market’s forecast volatility as well as the time of investor retirement, and to compare the investment performance with the traditional Target Date Fund. Forecasts of market volatility utilize historical volatility, time series model GARCH volatility, and the volatility index VKOSPI. The investment performance of the new dynamic Glide Path, which considers stock market volatility has been shown to be excellent during the analysis period from 2003 to 2020. In all three volatility prediction models, Sharpe Ratio, an investment performance indicator, is improved with higher returns and lower risks than traditional static Glide Path, which considers only retirement date. The empirical results of this study present the potential for the utilization of the suggested Glide Path in the Target Date Fund management industry as well as retirees.
참고문헌 (22)
- L. H. Ederington, "asymmetric is U.S. stock market volatility?", Journal of Financial Markets, 13(2), pp.225~248 (2010)[DOI]
- 김선웅, "Negative Asymmetric Relationship between VKOSPI and KOSPI 200", Journal of The Korean Data Analysis Society, 12(4), pp.1761~1773 (2010), 한국자료분석학회
- I. Badshah, "Asymmetries of the intraday return-volatility relation", International Review of Financial Analysis, 48, pp.182~192 (2016)[DOI]
- S. Chen, "Forecasting volatility with SVM-based GARCH model", Journal of Forecasting, 29, pp.406~433 (2010)[DOI]
- 김선웅, "주가변동성의 예측성과 비교: 가격범위와 VKOSPI를 중심으로", Journal of The Korean Data Analysis Society, 13(2), pp.915~925 (2011), 한국자료분석학회
- 배경훈, "생애주기와 경기변동주기를 이용한 투자전략", 한국증권학회지, 48(6), pp.721~754 (2019), 한국증권학회[DOI]
- D. M. Blanchett, "Revisiting the optimal distribution glide path", Journal of Financial Planning, 28(2), pp.52~61 (2015)
- W. Pfau, "Reducing retirement risk with a rising equity glide path", Journal of Financial Planning, 27(1), pp.38~45 (2013)[DOI]
- L. Delorme, "Confirming the value of rising equity glide paths : Evidence from a utility model", Journal of Financial Planning, 28(5), pp.46~52 (2015)
- Y. Yoon, "Glide path and dynamic asset allocation of target date funds", Journal of Asset Management, 11, pp.346~360 (2010)[DOI]
- R. D. Arnott, "Practical applications of the Glidepath illusion and potential solutions", The Journal of Portfolio Management, Practical Applications, 1(4), pp.6~9 (2014)[DOI]
- "Are target date funds dinosaurs? Failure to adapt can lead to extinction" (2017)[URL]
- A. K., Basu, "Dynamic lifecycle strategies for target date retirement funds", Journal of Portfolio Management, 37(2), pp.83~96 (2011)[DOI]
- B. Boscaljon, "Determining the"glide path"for target-date funds", Financial Services Review, 20, pp.113~128 (2011)
- J. Paterra, "To or through? : Evaluating TDF Glide Paths", Benefits Magazine, 56(12), pp.44~50 (2019)
- H. Levy, "The cost of diversification over time, and a simple way to improve target-date funds", Journal of Banking and Finance, 122, pp.1~16 (2021)[DOI]
- R. Engl, "Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation", Econometrica, 50(4), pp.987~1007 (1982)[DOI]
- T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity", Journal of Econometrics, 31, pp.307~327 (1986)[DOI]
- R. E. Whaley, "Derivatives on market volatility : Hedging tools long overdue", The Journal of Derivatives, 1, pp.71~84 (1993)[DOI]
- Z. Pan, "Improving volatility prediction and option valuation using VIX information : A volatility spillover GARCH model", Journal of Futures Markets, 39, pp.744~776 (2019)[DOI]
- R. E. Whaley, "The investor fear gauge", Journal of Portfolio Management, 26(3), pp.12~17 (2000)[DOI]
- 김선웅, "기계학습알고리즘을 이용한 위험회복지수의 개발과 활용", Journal of Information Technology Applications & Management, 23(4), pp.25~39 (2016), 한국데이터전략학회[DOI]
