Journal of Applied Econometrics · 2006 · 174 citations · 53 references
BusinessEconomicsFinanceEconometricsFinancial EconometricsFinancial EconomicsHigh-frequency Data AnalysisMarket RiskRisk-return AnalysisTime Series EconometricsVolatility AnalysisVolatility ModelingAsset VolatilityGeneralized Autoregressive Conditional Heteroskedasticity ModelsRange-based Volatility EstimationRealized VolatilityRisk-return Tradeoff AnalysisStock Market ReturnsVolatilityVolatility Modelling
The study investigates the intertemporal risk–return relationship in the aggregate stock market using high‑frequency data. The authors employ daily realized, GARCH, implied, and range‑based volatility estimators, leveraging high‑frequency intraday data and market‑expected future volatility, to test for a risk–return trade‑off across several indices. They find a positive, statistically significant, and persistent relationship between daily conditional mean returns and conditional volatility across indices, robust to alternative specifications and macro‑economic controls. © 2006 John Wiley & Sons, Ltd.
Abstract This paper examines the intertemporal relation between risk and return for the aggregate stock market using high‐frequency data. We use daily realized, GARCH, implied, and range‐based volatility estimators to determine the existence and significance of a risk–return trade‐off for several stock market indices. We find a positive and statistically significant relation between the conditional mean and conditional volatility of market returns at the daily level. This result is robust to alternative specifications of the volatility process, across different measures of market return and sample periods, and after controlling for macro‐economic variables associated with business cycle fluctuations. We also analyze the risk–return relationship over time using rolling regressions, and find that the strong positive relation persists throughout our sample period. The market risk measures adopted in the paper add power to the analysis by incorporating valuable information, either by taking advantage of high‐frequency intraday data (in the case of realized, GARCH, and range volatility) or by utilizing the market's expectation of future volatility (in the case of implied volatility index). Copyright © 2006 John Wiley & Sons, Ltd.
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Generalized autoregressive conditional heteroskedasticity
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