Simulation Methods for Lévy-Driven Continuous-Time Autoregressive Moving Average (Carma) Stochastic Volatility Models

Bibliographic Data

ID19420193
AuthorsViktor Todorov (0000-0002-2360-7812, Duke University), George Tauchen (0000-0003-0518-3611, Duke University)
Year2006
Volume24
Issue4
Pages455-469
Publication date2006-10-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1198/073500106000000260
OpenAlexW2052807037
LanguageEN
Citations received3
References cited22

We develop simulation schemes for the new classes of non-Gaussian pure jump Lévy processes for stochastic volatility. We write the price and volatility processes as integrals against a vector Lévy process, which makes series approximation methods directly applicable. These methods entail simulation of the Lévy increments and formation of weighted sums of the increments; they do not require a closed-form expression for a tail mass function or specification of a copula function. We also present a new, and apparently quite flexible, bivariate mixture-of-gammas model for the driving Lévy process. Within this setup, it is quite straightforward to generate simulations from a Lévy-driven continuous-time autoregressive moving average stochastic volatility model augmented by a pure-jump price component. Simulations reveal the wide range of different types of financial price processes that can be generated in this manner, including processes with persistent stochastic volatility, dynamic leverage, and jumps

Autoregressive model · Bivariate analysis · Econometrics · Jump · Lévy process · Physics · Realized variance · Statistics · Stochastic volatility · Computer Science · Financial Risk and Volatility Modeling · Mathematics · Statistical Distribution Estimation and Applications · Stochastic processes and financial applications · Applied Mathematics

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