Non‐parametric Threshold Estimation for Models with Stochastic Diffusion Coefficient and Jumps

Mancini, Cecilia

doi:10.1111/j.1467-9469.2008.00622.x

Cited by 409 publications

(357 citation statements)

References 32 publications

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“…. , M ; I {·} is the indicator function and ϑ j is a threshold function (see the Web Appendix for its precise definition) which is designed to remove jumps from the returns time series (Mancini, 2009). We have V t = C t + J t provided TSRV t > TBPV t in days with jumps, which is always the case empirically.…”

Section: Construction Of the Variables Of Interestmentioning

confidence: 99%

Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling

Corsi

Renò

2012

Journal of Business & Economic Statistics

275

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Citation: Corsi, F. and Reno, R. (2012). Discrete-time volatility forecasting with persistent leverage effect and the link with continuous-time volatility modeling. Journal of Business and Economic Statistics, 30(3), pp. 368-380. doi: 10.1080/07350015.2012.663261 This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract We first propose a reduced form model in discrete time for S&P500 volatility showing that the forecasting performance can be significantly improved by introducing a persistent leverage effect with a long-range dependence similar to that of volatility itself. We also find a strongly significant positive impact of lagged jumps on volatility, which however is absorbed more quickly. We then estimate continuous-time stochastic volatility models which are able to reproduce the statistical features captured by the discrete-time model. We show that a single-factor model driven by a fractional Brownian motion is unable to reproduce the volatility dynamics observed in the data, while a multi-factor Markovian model fully replicates the persistence of both volatility and leverage effect. The impact of jumps can be associated with a common jump component in price and volatility. Permanent

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Section: Construction Of the Variables Of Interestmentioning

confidence: 99%

Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling

Corsi

Renò

2012

Journal of Business & Economic Statistics

275

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“…Both the continuous and the jump components are known to be present in financial time series. From an economic point of view, volatility and jump risks are very different and this has spurred the recent interest in separately identifying these risks from high-frequency data on X ; see, for example, Barndorff-Nielsen and Shephard (2006) and Mancini (2009). In this paper we focus attention on the diffusive volatility part of X while recognizing the presence of jumps in X .…”

Section: Introductionmentioning

confidence: 99%

Estimating the Volatility Occupation Time via Regularized Laplace Inversion

Jia

Todorov²,

Tauchen

2015

Econom. Theory

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We propose a consistent functional estimator for the occupation time of the spot variance of an asset price observed at discrete times on a finite interval with the mesh of the observation grid shrinking to zero. The asset price is modeled nonparametrically as a continuous-time Itô semimartingale with nonvanishing diffusion coefficient. The estimation procedure contains two steps. In the first step we estimate the Laplace transform of the volatility occupation time and, in the second step, we conduct a regularized Laplace inversion. Monte Carlo evidence suggests that the proposed estimator has good small-sample performance and in particular it is far better at estimating lower volatility quantiles and the volatility median than a direct estimator formed from the empirical cumulative distribution function of local spot volatility estimates. An empirical application shows the use of the developed techniques for nonparametric analysis of variation of volatility.

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“…To account for the possible presence of jumps, we use the threshold realised volatility estimator proposed by Mancini (2009) and defined as…”

Section: Data and Proxies For The Latent Processesmentioning

confidence: 99%

Long memory dynamics for multivariate dependence under heavy tails

Janus

Koopman

Lucas

2014

Journal of Empirical Finance

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Non‐parametric Threshold Estimation for Models with Stochastic Diffusion Coefficient and Jumps

Cited by 409 publications

References 32 publications

Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling

Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling

Estimating the Volatility Occupation Time via Regularized Laplace Inversion

Long memory dynamics for multivariate dependence under heavy tails

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