Measuring Downside Risk - Realised Semivariance

Barndorff–Nielsen, Ole E.; Kinnebrock, Silja; Shephard, Neil

doi:10.2139/ssrn.1262194

Cited by 128 publications

(168 citation statements)

References 53 publications

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“…Indeed, BarndorffNielsen, Kinnebrock, and Shephard (2010) show that, as H t → ∞, rv g t and rv b t converge to half of the Gaussian diffusion in the returns and positive and negative quadratic jump variation, respectively.…”

Section: Good and Bad Realized Variancesmentioning

confidence: 99%

“…Barndorff-Nielsen, Kinnebrock, and Shephard (2010) show that if the log price follows a right continuous jump-diffusion process with left limits, then the sum of squared positive (negative) returns converge to the sum of half of the diffusive variation and the sum of squared positive (negative) jumps as the sampling frequency increases. Therefore, following Barndorff-Nielsen, Kinnebrock, and Shephard (2010), we define good and bad instantaneous quadratic variation as…”

Section: Good and Bad Variance Premiamentioning

confidence: 99%

“…Good (bad) implied variance measures the conditional risk-neutral expectation of the 1-month squared positive (negative) log equity return, computed from option prices as in Bakshi and Madan (2000) and Bakshi, Kapadia, and Madan (2003). We also compute the good and bad realized variance measures using the high-frequency return data, following Barndorff-Nielsen, Kinnebrock, and Shephard (2010). The good variance premium is negative, on average, while the bad variance premium is positive most of the time.…”

Section: Introductionmentioning

confidence: 99%

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Good and Bad Variance Premia and Expected Returns

Kilic

Shaliastovich

2017

SSRN Journal

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We measure "good" and "bad" variance premia that capture risk compensations for the realized variation in positive and negative market returns, respectively. The two variance premium components jointly predict excess returns over the next 1 and 2 years with statistically significant negative (positive) coefficients on the good (bad) component. The R 2 s reach about 10% for aggregate equity and portfolio returns and 20% for corporate bond returns. To explain the new empirical evidence, we develop an economic model which underscores the difference in investors' risk attitudes towards upside and downside uncertainty risks AbstractWe measure "good" and "bad" variance premia that capture risk compensations for the realized variation in positive and negative market returns, respectively. The two variance premium components jointly predict excess returns over the next 1 and 2 years with statistically significant negative (positive) coefficients on the good (bad) component. The R 2 s reach about 10% for aggregate equity and portfolio returns and about 20% for corporate bond returns. We show that an asset pricing model that features distinct time variation in positive and negative shocks to fundamentals can explain the good and bad variance premium evidence in the data.

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Section: Good and Bad Realized Variancesmentioning

confidence: 99%

Section: Good and Bad Variance Premiamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Good and Bad Variance Premia and Expected Returns

Kilic

Shaliastovich

2017

SSRN Journal

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“…In other words, instead of deriving conditions for the asymptotic negligibility of the measurement error, we show that indirect inference, coupled with a contamination scheme of the simulated trajectories, avoids the need to neutralize a-priori the impact of the MN on the volatility estimates. Hence, the auxiliary model can be based on potentially distorted but efficient and simple estimators of IV , like RV , BP V or signed jump variation as in Barndorff-Nielsen et al (2010) and Patton and Sheppard (2015). This represents a very general approach to the treatment of the problem of measurement error in the estimation of the SV framework, as it is potentially valid for any SV model and contamination scheme.…”

Section: Estimation Of Sv Models With Rvmentioning

confidence: 99%

Indirect inference with time series observed with error

Rossi

Magistris

2018

J of Applied Econometrics

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We analyze the properties of the indirect inference estimator when the observed series are contaminated by measurement error. We show that the indirect inference estimates are asymptotically biased when the nuisance parameters of the measurement error distribution are neglected in the indirect estimation. We propose to solve this inconsistency by jointly estimating the nuisance and the structural parameters. The range of applicability of this methodology is supported by theoretical results based on several examples for both discrete and continuous-time models. Indirect inference is used to estimate the parameters of stochastic volatility models with auxiliary specifications based on realized volatility measures. Monte Carlo simulations show the bias reduction of the indirect estimates obtained when the microstructure noise is explicitly modeled. Finally, an empirical application illustrates the relevance of a realistic specification of the microstructure noise distribution to match the features of the observed log-returns at high frequencies.

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“…We have followed Barndorff-Nielsen et al (2010) closely in our treatment of sources of conditional skewness. This implies that in the proof of the above proposition, and following Barndorff-Nielsen et al…”

mentioning

confidence: 99%

Which Parametric Model for Conditional Skewness?

Feunou

Tedongap

2011

SSRN Journal

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Provided in Cooperation with:Bank of Canada, Ottawa Suggested Citation: Feunou, Bruno; Jahan-Parvar, Mohammad R.; Tédongap, Roméo (2013) AbstractThis paper addresses an existing gap in the developing literature on conditional skewness. We develop a simple procedure to evaluate parametric conditional skewness models. This procedure is based on regressing the realized skewness measures on model-implied conditional skewness values. We find that an asymmetric GARCH-type specification on shape parameters with a skewed generalized error distribution provides the best insample fit for the data, as well as reasonable predictions of the realized skewness measure. Our empirical findings imply significant asymmetry with respect to positive and negative news in both conditional asymmetry and kurtosis processes.

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Measuring Downside Risk - Realised Semivariance

Cited by 128 publications

References 53 publications

Good and Bad Variance Premia and Expected Returns

Good and Bad Variance Premia and Expected Returns

Indirect inference with time series observed with error

Which Parametric Model for Conditional Skewness?

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