Spectral GMM estimation of continuous-time processes. (English) Zbl 1026.62085
Summary: This paper derives a methodology for the estimation of continuous-time stochastic models based on characteristic functions. The estimation method does not require discretization of the stochastic process, and is simple to apply in practice. The method is essentially the generalized method of moments on the complex plane. Hence it shares the efficiency and distribution properties of GMM estimators. We illustrate the method with some applications to relevant estimation problems in continuous-time finance. We estimate a model of stochastic volatility, a jump-diffusion model with constant volatility and a model that nests both the stochastic volatility model and the jump-diffusion model.
We find that negative jumps are important to explain skewness and asymmetry in excess kurtosis of the stock return distribution, while stochastic volatility is important to capture the overall level of this kurtosis. Positive jumps are not statistically significant once we allow for stochastic volatility in the model. We also estimate a non-affine model of stochastic volatility, and find that the power of the diffusion coefficient appears to be between one and two, rather than the value of one-half that leads to the standard affine stochastic volatility model. However, we find that including jumps into this non-affine, stochastic volatility model reduces the power of the diffusion coefficient to one-half. Finally, we offer an explanation for the observation that the estimate of persistence in stochastic volatility increases dramatically as the frequency of the observed data falls based on a multiple factor stochastic volatility model.
We find that negative jumps are important to explain skewness and asymmetry in excess kurtosis of the stock return distribution, while stochastic volatility is important to capture the overall level of this kurtosis. Positive jumps are not statistically significant once we allow for stochastic volatility in the model. We also estimate a non-affine model of stochastic volatility, and find that the power of the diffusion coefficient appears to be between one and two, rather than the value of one-half that leads to the standard affine stochastic volatility model. However, we find that including jumps into this non-affine, stochastic volatility model reduces the power of the diffusion coefficient to one-half. Finally, we offer an explanation for the observation that the estimate of persistence in stochastic volatility increases dramatically as the frequency of the observed data falls based on a multiple factor stochastic volatility model.
MSC:
| 62M09 | Non-Markovian processes: estimation |
| 62P20 | Applications of statistics to economics |
| 62P05 | Applications of statistics to actuarial sciences and financial mathematics |
| 62M05 | Markov processes: estimation; hidden Markov models |
| 62M15 | Inference from stochastic processes and spectral analysis |
Keywords:
continuous-time estimation; characteristic function; GMM; spectral GMM; stochastic volatility; jump-diffusion process; affine modelsSoftware:
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