Department of Economics and Business Economics

A Jump Diffusion Model for Volatility and Duration

Research output: Working paperResearch

Standard

A Jump Diffusion Model for Volatility and Duration. / Wei, Wei; Pelletier, Denis.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2013.

Research output: Working paperResearch

Harvard

APA

CBE

Wei W, Pelletier D. 2013. A Jump Diffusion Model for Volatility and Duration. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Wei, Wei and Denis Pelletier A Jump Diffusion Model for Volatility and Duration. Aarhus: Institut for Økonomi, Aarhus Universitet. 2013., 41 p.

Vancouver

Wei W, Pelletier D. A Jump Diffusion Model for Volatility and Duration. Aarhus: Institut for Økonomi, Aarhus Universitet. 2013.

Author

Wei, Wei ; Pelletier, Denis. / A Jump Diffusion Model for Volatility and Duration. Aarhus : Institut for Økonomi, Aarhus Universitet, 2013.

Bibtex

@techreport{eed402a71f5e4708a57b2fad9fd30bc0,
title = "A Jump Diffusion Model for Volatility and Duration",
abstract = "This paper puts forward a stochastic volatility and stochastic conditional durationwith cojumps (SVSDCJ) model to analyze returns and durations. In highfrequency data, transactions are irregularly spaced, and the durations betweentransactions carry information about volatility as suggested by the market microstructure theory. Traditional measures of volatility do not utilize durations. Iadopt a jump diffusion process to model the persistence of intraday volatility andconditional duration, and their interdependence. The jump component is disentangled from the continuous part of the price, volatility and conditional duration process. I develop a MCMC algorithm for the inference of irregularly spaced multivariate process with jumps. The algorithm provides smoothed estimates of the latent variables such as spot volatility, jump times and jump sizes. I apply this model to IBM data and I find meaningful relationship between volatility and conditional duration. Also, jumps play an important role in the total variation, but the jump variation is smaller than traditional measures that use returns sampled at lower frequency.",
author = "Wei Wei and Denis Pelletier",
year = "2013",
language = "English",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - A Jump Diffusion Model for Volatility and Duration

AU - Wei, Wei

AU - Pelletier, Denis

PY - 2013

Y1 - 2013

N2 - This paper puts forward a stochastic volatility and stochastic conditional durationwith cojumps (SVSDCJ) model to analyze returns and durations. In highfrequency data, transactions are irregularly spaced, and the durations betweentransactions carry information about volatility as suggested by the market microstructure theory. Traditional measures of volatility do not utilize durations. Iadopt a jump diffusion process to model the persistence of intraday volatility andconditional duration, and their interdependence. The jump component is disentangled from the continuous part of the price, volatility and conditional duration process. I develop a MCMC algorithm for the inference of irregularly spaced multivariate process with jumps. The algorithm provides smoothed estimates of the latent variables such as spot volatility, jump times and jump sizes. I apply this model to IBM data and I find meaningful relationship between volatility and conditional duration. Also, jumps play an important role in the total variation, but the jump variation is smaller than traditional measures that use returns sampled at lower frequency.

AB - This paper puts forward a stochastic volatility and stochastic conditional durationwith cojumps (SVSDCJ) model to analyze returns and durations. In highfrequency data, transactions are irregularly spaced, and the durations betweentransactions carry information about volatility as suggested by the market microstructure theory. Traditional measures of volatility do not utilize durations. Iadopt a jump diffusion process to model the persistence of intraday volatility andconditional duration, and their interdependence. The jump component is disentangled from the continuous part of the price, volatility and conditional duration process. I develop a MCMC algorithm for the inference of irregularly spaced multivariate process with jumps. The algorithm provides smoothed estimates of the latent variables such as spot volatility, jump times and jump sizes. I apply this model to IBM data and I find meaningful relationship between volatility and conditional duration. Also, jumps play an important role in the total variation, but the jump variation is smaller than traditional measures that use returns sampled at lower frequency.

M3 - Working paper

BT - A Jump Diffusion Model for Volatility and Duration

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -