Simulated likelihood estimators for discretely observed jump–diffusions
| Autor: | Kay Giesecke, Gustavo Schwenkler |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Economics and Econometrics
Applied Mathematics 05 social sciences Magnitude (mathematics) Estimator 01 natural sciences Monte carlo approximation 010104 statistics & probability Sample size determination 0502 economics and business Jump Transition density Applied mathematics 0101 mathematics Volatility (finance) Intensity (heat transfer) 050205 econometrics Mathematics |
| Zdroj: | Journal of Econometrics. 213:297-320 |
| ISSN: | 0304-4076 |
| Popis: | This paper develops an unbiased Monte Carlo approximation to the transition density of a jump–diffusion process with state-dependent drift, volatility, jump intensity, and jump magnitude. The approximation is used to construct a likelihood estimator of the parameters of a jump–diffusion observed at fixed time intervals that need not be short. The estimator is asymptotically unbiased for any sample size. It has the same large-sample asymptotic properties as the true but uncomputable likelihood estimator. Numerical results illustrate its properties. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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