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Kevin Sheppard

Biographic Data

ID4985003
NAMEKevin Sheppard
GIVEN NAMESKevin
FAMILY NAMESheppard
SIGNATURESHEPPARD K
AFFILIATIONSUniversity of Oxford
ORCID0000-0001-8700-2292
VERIFIEDYes
TOTAL WORKS6
TOTAL CITATIONS0
WORKS AS AUTHOR6
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2000
LATEST PUBLICATION YEAR2023
H-INDEX0
  • Bootstrapping Two-Stage Quasi-Maximum Likelihood Estimators of Time Series Models

    Sílvia Gonçalves, Śılvia Gonçalves et al.•ARTICLE•Journal of Business and Economic…•2023

    This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing th…

  • Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice

    Asger Lunde, Neil Shephard et al.•ARTICLE•Journal of Business and Economic…•2016

    We propose a composite realized kernel to estimate the ex-post covariation of asset prices. These measures can in turn be used to forecast the covariation of future asset returns. Composite realized kernels are a data-efficient method, where the covariance estimate is composed of univariate realized kernels to estimate variances and bivariate realized kernels to estimate correlations. We analyze the merits of our composite realized kernels in an …

  • Good Volatility, Bad Volatility

    Andrew J Patton, Kevin Sheppard•ARTICLE•The Review of Economics and…•2015

    Using estimators of the variation of positive and negative returns (realized semivariances) and high-frequency data for the S&P 500 Index and 105 individual stocks, this paper sheds new light on the predictability of equity price volatility.We showthat future volatility is more strongly related to the volatility of past negative returns than to that of positive returns and that the impact of a price jump on volatility depends on the sign of the j…

  • Ambiguity and the Historical Equity Premium

    Open Access•Fabrice Collard, Sujoy Mukerji et al.•ARTICLE•SSRN Electronic Journal•2011

  • Ambiguity and the Historical Equity Premium

    Open Access•Sujoy Mukerji, Fabrice Collard et al.•ARTICLE•SSRN Electronic Journal•2011

  • Late Wisconsinan stratigraphy and chronology at Highlands, southern St. George's Bay, southwest Newfoundland

    Open Access•Kevin Sheppard, Trevor Bell et al.•ARTICLE•Quaternary International•2000

No prominent works on this page.

  • Late Wisconsinan stratigraphy and chronology at Highlands, southern St. George's Bay, southwest Newfoundland

    Open Access•Kevin Sheppard, Trevor Bell et al.•ARTICLE•Quaternary International•2000

  • Ambiguity and the Historical Equity Premium

    Open Access•Fabrice Collard, Sujoy Mukerji et al.•ARTICLE•SSRN Electronic Journal•2011

  • Ambiguity and the Historical Equity Premium

    Open Access•Sujoy Mukerji, Fabrice Collard et al.•ARTICLE•SSRN Electronic Journal•2011

  • Good Volatility, Bad Volatility

    Andrew J Patton, Kevin Sheppard•ARTICLE•The Review of Economics and…•2015

    Using estimators of the variation of positive and negative returns (realized semivariances) and high-frequency data for the S&P 500 Index and 105 individual stocks, this paper sheds new light on the predictability of equity price volatility.We showthat future volatility is more strongly related to the volatility of past negative returns than to that of positive returns and that the impact of a price jump on volatility depends on the sign of the j…

  • Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice

    Asger Lunde, Neil Shephard et al.•ARTICLE•Journal of Business and Economic…•2016

    We propose a composite realized kernel to estimate the ex-post covariation of asset prices. These measures can in turn be used to forecast the covariation of future asset returns. Composite realized kernels are a data-efficient method, where the covariance estimate is composed of univariate realized kernels to estimate variances and bivariate realized kernels to estimate correlations. We analyze the merits of our composite realized kernels in an …

  • Bootstrapping Two-Stage Quasi-Maximum Likelihood Estimators of Time Series Models

    Sílvia Gonçalves, Śılvia Gonçalves et al.•ARTICLE•Journal of Business and Economic…•2023

    This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing th…

Econometrics (5 works) · Economics (4 works) · Financial economics (4 works) · Financial Markets and Investment Strategies (3 works) · Financial Risk and Volatility Modeling (3 works) · Mathematics (3 works) · Statistics (3 works) · Ambiguity (2 works) · Computer Science (2 works) · Estimator (2 works)

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