The Experts below are selected from a list of 78 Experts worldwide ranked by ideXlab platform
Markus Reiss - One of the best experts on this subject based on the ideXlab platform.
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
Journal of Business & Economic Statistics, 2019Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semimartingale log Asset Price Process, which is subject to noise and nonsynchronous observations. The es...
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
2014Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log Asset Price Process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We extend the LMM estimator to allow for autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. We prove the consistency and asymptotic normality of the proposed spot covariance estimator. Based on extensive simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and betas in normal but also extreme-event periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
2014Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log Asset Price Process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise para- metric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We prove consistency and a point-wise stable central limit theorem for the proposed spot covariance estimator in a very general setup with stochastic volatilities, leverage and for general noise distributions. Moreover, we extend the LMM estimator to be robust against autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. Based on simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and volatilities in normal but also unusual periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
Markus Bibinger - One of the best experts on this subject based on the ideXlab platform.
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
Journal of Business & Economic Statistics, 2019Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semimartingale log Asset Price Process, which is subject to noise and nonsynchronous observations. The es...
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
2014Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log Asset Price Process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We extend the LMM estimator to allow for autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. We prove the consistency and asymptotic normality of the proposed spot covariance estimator. Based on extensive simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and betas in normal but also extreme-event periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
2014Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log Asset Price Process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise para- metric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We prove consistency and a point-wise stable central limit theorem for the proposed spot covariance estimator in a very general setup with stochastic volatilities, leverage and for general noise distributions. Moreover, we extend the LMM estimator to be robust against autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. Based on simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and volatilities in normal but also unusual periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
Nikolaus Hautsch - One of the best experts on this subject based on the ideXlab platform.
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
Journal of Business & Economic Statistics, 2019Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semimartingale log Asset Price Process, which is subject to noise and nonsynchronous observations. The es...
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
2014Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log Asset Price Process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We extend the LMM estimator to allow for autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. We prove the consistency and asymptotic normality of the proposed spot covariance estimator. Based on extensive simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and betas in normal but also extreme-event periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
2014Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log Asset Price Process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise para- metric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We prove consistency and a point-wise stable central limit theorem for the proposed spot covariance estimator in a very general setup with stochastic volatilities, leverage and for general noise distributions. Moreover, we extend the LMM estimator to be robust against autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. Based on simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and volatilities in normal but also unusual periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
Peter Malec - One of the best experts on this subject based on the ideXlab platform.
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
Journal of Business & Economic Statistics, 2019Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semimartingale log Asset Price Process, which is subject to noise and nonsynchronous observations. The es...
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
2014Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log Asset Price Process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We extend the LMM estimator to allow for autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. We prove the consistency and asymptotic normality of the proposed spot covariance estimator. Based on extensive simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and betas in normal but also extreme-event periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
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estimating the spot covariation of Asset Prices statistical theory and empirical evidence
2014Co-Authors: Markus Bibinger, Nikolaus Hautsch, Peter Malec, Markus ReissAbstract:We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log Asset Price Process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise para- metric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We prove consistency and a point-wise stable central limit theorem for the proposed spot covariance estimator in a very general setup with stochastic volatilities, leverage and for general noise distributions. Moreover, we extend the LMM estimator to be robust against autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. Based on simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and volatilities in normal but also unusual periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
Vadim Linetsky - One of the best experts on this subject based on the ideXlab platform.
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pricing and hedging path dependent options under the cev Process
Management Science, 2001Co-Authors: Dmitry Davydov, Vadim LinetskyAbstract:Much of the work on path-dependent options assumes that the underlying Asset Price follows geometric Brownian motion with constant volatility. This paper uses a more general assumption for the Asset Price Process that provides a better fit to the empirical observations. We use the so-called constant elasticity of variance CEV diffusion model where the volatility is a function of the underlying Asset Price. We derive analytical formulae for the Prices of important types of path-dependent options under this assumption. We demonstrate that the Prices of options, which depend on extrema, such as barrier and lookback options, can be much more sensitive to the specification of the underlying Price Process than standard call and put options and show that a financial institution that uses the standard geometric Brownian motion assumption is exposed to significant pricing and hedging errors when dealing in path-dependent options.
