A Simplified Jump Process for Common Stock Returns

Ball, Clifford A.; Torous, Walter N.

doi:10.2307/2330804

Cited by 239 publications

(138 citation statements)

References 13 publications

(13 reference statements)

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“…For instance, Ball and Torous (1983) and Jorion (1988) use jump processes to study the impact of big shocks in common stock prices or foreign exchange rates while Vlaar and Palm (1993) propose Poisson or Bernoulli mixtures of normal distributions. Such a parametric approach requires one to further specify and estimate a model governing the time-varying jump intensity and also the jump size.…”

Section: Introductionmentioning

confidence: 99%

Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach

Laurent

Lecourt

Palm

2016

Computational Statistics & Data Analysis

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Financial series occasionally exhibit large changes. To deal with those events, we assume that the observed return series consists of a conditionally Gaussian ARMA-GARCH (or -GJR) model contaminated by an additive jump component. In this framework, we propose a new test for additive jumps. The test is based on standardised returns, where the first two conditional moments of the non-contaminated observations are estimated in a robust way. Simulation results indicate that the test has very good finite sample properties, i.e. correct size and high proportion of correct jump detection. We apply our test on daily returns and detect less than 1% of jumps for the three exchange rates and between 1 and 3% of jumps for about 50 large capitalization stocks from the NYSE. Once jumps have been filtered out, all series are found to be conditionally Gaussian. We also find that simple GARCH-type models estimated on filtered returns deliver better out-of-sample forecasts of the conditional variance than GARCH and GAS models estimated on raw data.

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Section: Introductionmentioning

confidence: 99%

Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach

Laurent

Lecourt

Palm

2016

Computational Statistics & Data Analysis

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“…The SNJD model is a further step in the tradition of JD models initiated by Merton (1976) and continued by Ball andTorous (1983, 1985) and Jorion (1988). The SNJD model reflects in a simple and intuitive way not just the jump, but also what happens after the occurrence of such jump.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is also assumed that the variables A k are independent of the Poisson process, N t . Then, H t is named Shot-Noise process and it has been intensively studied by Rice (1954) and Parzen (1962) 5 . Moreover, Bondesson (1988) provides a general treatment of these processes and includes extensive references.…”

Section: The Shot-noise Processmentioning

confidence: 99%

Statistical properties and economic implications of jump-diffusion processes with shot-noise effects

Moreno¹,

Serrano

Stute

2011

European Journal of Operational Research

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This paper analyzes the Shot-Noise Jump-Diffusion model of Altmann, Schmidt and Stute (2008), which introduces a new situation where the effects of the arrival of rare, shocking information to the financial markets may fade away in the long run. We analyze several economic implications of the model, providing an analytical expression for the process distribution. We also prove that certain specifications of this model can provide negative serial persistence. Additionally, we find that the degree of serial autocorrelation is related to the arrival and magnitude of abnormal information. Finally, a GMM framework is proposed to estimate the model parameters.

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“…In particular, there has been a fast growing literature on models of credit risk measurement. 1 One measurement issue that is both interesting and challenging is the credit risk of a portfolio of risky bonds. This is especially relevant for banks, pension funds, insurance companies, and bond mutual funds.…”

Section: Introductionmentioning

confidence: 99%

An Econometric Model of Credit Spreads with Rebalancing, ARCH and Jump Effects

2003

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An Econometric Model of Credit Spreads withRebalancing, ARCH, and Jump EffectsIn this paper, we examine the dynamic behavior of credit spreads on corporate bond portfolios. We propose an econometric model of credit spreads that incorporates portfolio rebalancing, the near unit root property of spreads, the autocorrelation in spread changes, the ARCH conditional heteroscedasticity, jumps, and lagged market factors. In particular, our model is the first that takes into account explicitly the impact of rebalancing and yields estimates of the absorbing bounds on credit spreads induced by such rebalancing. We apply our model to nine Merrill Lynch daily series of option-adjusted spreads with ratings from AAA to C for the period January, 1997 through August, 2002. We find no evidence of mean reversion in these credit spread series over our sample period. However, we find ample evidence of both the ARCH effect and jumps in the data especially in the investmentgrade credit spread indices. Incorporating jumps into the ARCH type conditional variance results in significant improvements in model diagnostic tests. We also find that while log spread variations depend on both the lagged Russell 2000 index return and lagged changes in the slope of the yield curve, the time-varying jump intensity of log credit spreads is correlated with the lagged stock market volatility. Finally, our results indicate the ARCHjump specification outperforms the ARCH specification in the out-of-sample, one-stepahead forecast of credit spreads.JEL Classification Codes: C22, C13, C53, G12.

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A Simplified Jump Process for Common Stock Returns

Cited by 239 publications

References 13 publications

Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach

Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach

Statistical properties and economic implications of jump-diffusion processes with shot-noise effects

An Econometric Model of Credit Spreads with Rebalancing, ARCH and Jump Effects

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