Stochastic Volatility Model with Jumps in Returns and Volatility: An R-Package Implementation

  • Adjoa Numatsi
  • Erick W. Rengifo
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 196)


In this chapter we estimate the stochastic volatility model with jumps in return and volatility introduced by [7]. In this model the conditional volatility of returns can not only increase rapidly but also persistently. Moreover, as shown by [8], this new model performs better than previous models presenting almost no misspecification in the volatility process. We implement the model coding the algorithm using R language. We estimate the model parameters and latent variables using FTSE 100 daily returns. The values of some of our estimated parameters are close to values found in previous studies. Also, as expected, our estimated state variable paths show high probabilities of jumps in the periods of financial crisis.


Markov Chain Monte Carlo Stock Return Markov Chain Monte Carlo Method Daily Return Markov Chain Monte Carlo Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag New York 2010

Authors and Affiliations

  1. 1.Department of EconomicsFordham UniversityBronxUSA

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