Earticle

초록

영어

This paper suggests stochastic volatility models incorporating both the leverage effect and information on the daily high/low prices of stocks. The leverage effect is measured using open-to-close returns and two distinct intraday data, ranges, defined by the differences between daily high and low log-prices, and extreme prices in order to detect asymmetric volatility behavior. The likelihood-based inferences of Markov Chain Monte Carlo (MCMC) are conducted to estimate parameters and volatility. The simulation study reveals that the proposed model is superior to a traditional stochastic volatility model using returns only but there is little difference between estimators using ranges or high/low prices. Performing an empirical analysis using the E-mini S&P 500 and the Nasdaq 100 Futures, we find strong evidence of the leverage effect even when information of high/low prices is incorporated.

목차

ABSTRACT
 1. Introduction
 2. Stochastic Volatility Models
  2.1. The basic model
  2.2. The model incorporating ranges and H/L prices
 3. Estimation Methodology
  3.1 The RHL estimator
  3.2 The RR estimator
  3.3 MCMC Method
 4. Simulation Analysis
 5. Empirical Analysis
  5.1. Data
  5.2. Estimates from the stochastic volatility models
 6. Conclusions and Future research
 REFERENCES
 Table
 Figure

저자

• Suk Joon Byun [ KAIST Business School, 85 Hoegiro, Dongdaemun-gu, Seoul, 130-722, Korea ]
• Jung-Soon Hyun [ KAIST Business School, 85 Hoegiro, Dongdaemun-gu, Seoul, 130-722, Korea ]
• Woon Jun Sung [ KAIST Business School, 85 Hoegiro, Dongdaemun-gu, Seoul, 130-722, Korea ]

참고문헌

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간행물

표지보기 관심저널 등록

• 간행물명

  한국재무학회 학술대회

• 간기

  부정기

• 수록기간

  2006~2024

• 십진분류

  KDC 325 DDC 330