Department of Economics and Business Economics

A Jump-Diffusion Model with Stochastic Volatility and Durations

Research output: Working paperResearch

Standard

A Jump-Diffusion Model with Stochastic Volatility and Durations. / Wei, Wei; Pelletier, Denis.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2015.

Research output: Working paperResearch

Harvard

Wei, W & Pelletier, D 2015 'A Jump-Diffusion Model with Stochastic Volatility and Durations' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Wei, W., & Pelletier, D. (2015). A Jump-Diffusion Model with Stochastic Volatility and Durations. Institut for Økonomi, Aarhus Universitet. CREATES Research Papers, No. 2015-34

CBE

Wei W, Pelletier D. 2015. A Jump-Diffusion Model with Stochastic Volatility and Durations. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Wei, Wei and Denis Pelletier A Jump-Diffusion Model with Stochastic Volatility and Durations. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2015-34). 2015., 44 p.

Vancouver

Wei W, Pelletier D. A Jump-Diffusion Model with Stochastic Volatility and Durations. Aarhus: Institut for Økonomi, Aarhus Universitet. 2015 Aug 10.

Author

Wei, Wei ; Pelletier, Denis. / A Jump-Diffusion Model with Stochastic Volatility and Durations. Aarhus : Institut for Økonomi, Aarhus Universitet, 2015. (CREATES Research Papers; No. 2015-34).

Bibtex

@techreport{2a54c6e8ec13417092a62dd31e2e5013,
title = "A Jump-Diffusion Model with Stochastic Volatility and Durations",
abstract = "Market microstructure theories suggest that the durations between transactions carry information about volatility. This paper puts forward a model featuring stochastic volatility, stochastic conditional duration, and jumps to analyze high frequency returns and durations. Durations affect price jumps in two ways: as exogenous sampling intervals, and through the interaction with volatility. We adopt a bivariate Ornstein-Ulenbeck process to model intraday volatility and conditional duration. We develop a MCMC algorithm for the inference on irregularly spaced multivariate processes with jumps. The algorithm provides smoothed estimates of the latent variables such as spot volatility, conditional duration, jump times, and jump sizes. We apply this model to IBM data and find that volatility and conditional duration are interdependent. We also find that jumps play an important role in return variation, but joint modeling of volatility and conditional duration reduces significantly the need for jumps.",
keywords = "Durations, Stochastic Volatility, Price jumps, High-frequency data, Bayesian inference",
author = "Wei Wei and Denis Pelletier",
year = "2015",
month = aug,
day = "10",
language = "English",
series = "CREATES Research Papers",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
number = "2015-34",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - A Jump-Diffusion Model with Stochastic Volatility and Durations

AU - Wei, Wei

AU - Pelletier, Denis

PY - 2015/8/10

Y1 - 2015/8/10

N2 - Market microstructure theories suggest that the durations between transactions carry information about volatility. This paper puts forward a model featuring stochastic volatility, stochastic conditional duration, and jumps to analyze high frequency returns and durations. Durations affect price jumps in two ways: as exogenous sampling intervals, and through the interaction with volatility. We adopt a bivariate Ornstein-Ulenbeck process to model intraday volatility and conditional duration. We develop a MCMC algorithm for the inference on irregularly spaced multivariate processes with jumps. The algorithm provides smoothed estimates of the latent variables such as spot volatility, conditional duration, jump times, and jump sizes. We apply this model to IBM data and find that volatility and conditional duration are interdependent. We also find that jumps play an important role in return variation, but joint modeling of volatility and conditional duration reduces significantly the need for jumps.

AB - Market microstructure theories suggest that the durations between transactions carry information about volatility. This paper puts forward a model featuring stochastic volatility, stochastic conditional duration, and jumps to analyze high frequency returns and durations. Durations affect price jumps in two ways: as exogenous sampling intervals, and through the interaction with volatility. We adopt a bivariate Ornstein-Ulenbeck process to model intraday volatility and conditional duration. We develop a MCMC algorithm for the inference on irregularly spaced multivariate processes with jumps. The algorithm provides smoothed estimates of the latent variables such as spot volatility, conditional duration, jump times, and jump sizes. We apply this model to IBM data and find that volatility and conditional duration are interdependent. We also find that jumps play an important role in return variation, but joint modeling of volatility and conditional duration reduces significantly the need for jumps.

KW - Durations, Stochastic Volatility, Price jumps, High-frequency data, Bayesian inference

M3 - Working paper

T3 - CREATES Research Papers

BT - A Jump-Diffusion Model with Stochastic Volatility and Durations

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -