Conditional Jump Dynamics in Stock Market Returns

Chan, Wai-Yip; Maheu, John M.

doi:10.1198/073500102288618513

Cited by 281 publications

(237 citation statements)

References 26 publications

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“…Our fairly parsimonious models, however, will not provide for any asymmetry in this respect. A referee has pointed out that the existence of jump behaviour is another stylized fact (see Chan andMaheu, 2002, andEraker, 2004), which our models will not be able to account for, either. These aspects might be a route for further and more ambitious research.…”

Section: The Methods Of Simulated Momentsmentioning

confidence: 99%

Structural stochastic volatility in asset pricing dynamics: Estimation and model contest

Franke

Westerhoff

2012

Journal of Economic Dynamics and Control

197

184

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In the framework of small-scale agent-based financial market models, the paper starts out from the concept of structural stochastic volatility, which derives from different noise levels in the demand of fundamentalists and chartists and the time-varying market shares of the two groups. It advances several different specifications of the endogenous switching between the trading strategies and then estimates these models by the method of simulated moments (MSM), where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. In addition to the standard version of MSM with a quadratic loss function, we also take into account how often a great number of Monte Carlo simulation runs happen to yield moments that are all contained within their empirical confidence intervals. The model contest along these lines reveals a strong role for a (tamed) herding component. The quantitative performance of the winner model is so good that it may provide a standard for future research.JEL classification: D84; G12; G14; G15.

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Section: The Methods Of Simulated Momentsmentioning

confidence: 99%

Structural stochastic volatility in asset pricing dynamics: Estimation and model contest

Franke

Westerhoff

2012

Journal of Economic Dynamics and Control

197

184

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“…The restrictions φ 1 > 0 and φ 2 > φ 3 > 0 are sufficient to guarantee the positiveness of λ t as in Chan and Maheu (2002). Note that the innovation term depends on the conditional probabilities of observing m jumps given the information set at time t, and those are determined following the hypothesis of having a Poisson process governing the jumps number, see (8).…”

Section: Time-varying Jump Intensitymentioning

confidence: 99%

“…We first specify the dynamic evolution of the parameter λ t , i.e. the jump intensity, for which we suggest the Auto Regressive Jump Intensity (ARJI) specification of Chan and Maheu (2002). The innovation in the jump intensity dynamic are derived from the jump probability as follows:…”

Section: Time-varying Jump Intensitymentioning

confidence: 99%

Chasing Volatility: A Persistent Multiplicative Error Model with Jumps

Caporin

Rossi²,

Magistris

2014

SSRN Journal

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The realized volatility of financial returns is characterized by persistence and occurrence of unpredictable large increments. To capture those features, we introduce the Multiplicative Error Model with jumps (MEM-J). When a jump component is included in the multiplicative specification, the conditional density of the realized measure is shown to be a countably infinite mixture of Gamma and K distributions. Strict stationarity conditions are derived. A Monte Carlo simulation experiment shows that maximum likelihood estimates of the model parameters are reliable even when jumps are rare events. We estimate alternative specifications of the model using a set of daily bipower measures for 7 stock indexes and 16 individual NYSE stocks. The estimates of the jump component confirm that the probability of jumps dramatically increases during the financial crises. Compared to other realized volatility models, the introduction of the jump component provides a sensible improvement in the fit, as well as for in-sample and out-of-sample volatility tail forecasts.

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“…Furthermore, the information extracted from oil price is crucial for technology change decisions and oil price is a better indicator of the lack of resources than the amount of oil production. Using the Chan and Maheu (2002) model showed strong evidence on GARCH model behaviors and also conditional jump intensity in daily data of oil price; that is, conditional heteroscedasticity variance exists and practical distribution has fat tail. Furthermore, this model has high sensitivity to events and news and, therefore, it does not fluctuate around a long-term pattern; and despite that previous theories hold that prices fluctuate around an increasing pattern and oil price contains information, this model rejects the existence of such information.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we used the model of Chan and Maheu (2002) which was previously used to model stock return and exchange rate, to model OPEC oil price change. Two reasons supported this approach.…”

Section: Resultsmentioning

confidence: 99%

Modeling jumps in organization of petroleum exporting countries basket price using generalized autoregressive heteroscedasticity and conditional jump

Bahramgiri

Gharaati

Dolatabadi

2016

Investment Management and Financial Innovations

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This paper uses autoregressive jump intensity (ARJI) model to show that the oil price has both GARCH and conditional jump component. In fact, the distribution of oil prices is not normal, and oil price returns have conditional heteroskedasticity. Here the authors compare constant jump intensity with the dynamic jump intensity and evidences demonstrate that oil price returns have dynamic jump intensity. Therefore, there is strong evidence of time varying jump intensity Generalized Autoregressive Heteroscedasticity (GARCH) behavior in the oil price returns. The findings have several implications: first, it shows that oil price is highly sensitive to news, and it does settle around a trend in long-run. Second, the model separates variances of high volatilities from smooth volatilities. Third, the model rejects an optimal path for extracting oil and technology transmission. In fact, the lack of a long-term pattern can cause excessive oil extracting which can result in heavy climatic effects. Keywords: generalized autoregressive heteroscedasticity (GARCH), jumps, basket, oil price, Organization of Petroleum Exporting Countries (OPEC), Autoregre-ssive jump intensity (ARJI). JEL Classification: C32, C52, F31

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Conditional Jump Dynamics in Stock Market Returns

Cited by 281 publications

References 26 publications

Structural stochastic volatility in asset pricing dynamics: Estimation and model contest

Structural stochastic volatility in asset pricing dynamics: Estimation and model contest

Chasing Volatility: A Persistent Multiplicative Error Model with Jumps

Modeling jumps in organization of petroleum exporting countries basket price using generalized autoregressive heteroscedasticity and conditional jump

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