Speaker: Pentti Saikkonen (University of Helsinki)

Title: ”A mixture autoregressive model based on Student’s t-distribution”

Abstract: ”We propose a new mixture autoregressive model based on Student’s t-distribution. A key feature of our model is that the conditional t-distributions of the component models are based on autoregressions that have t-distributions as their stationary distributions. That autoregressions with such stationary distributions exist is not immediate. Our formulation implies that the conditional variance of each mixture component is not constant but of (nonlinear) ARCH type. Compared to previous mixture autoregressive models our model may therefore be useful in applications where the data exhibits rather strong conditional heteroskedasticity. Our formulation also has the theoretical advantage that conditions for stationarity and ergodicity are always met and these properties are much more straightforward to establish than is common in nonlinear autoregressive models. An empirical example employing a realized kernel series based on S&P 500 data shows that the proposed model performs well in volatility forecasting.”

Time: 12:15
Place: Lecture Hall XVI (Quantum 1st floor)

All interested are very welcome!

For more information on the seminar and future schedule is here.

Speaker: Pentti Saikkonen (University of Helsinki)

Title: ”A mixture autoregressive model based on Student’s t-distribution”

Abstract: ”We propose a new mixture autoregressive model based on Student’s t-distribution. A key feature of our model is that the conditional t-distributions of the component models are based on autoregressions that have t-distributions as their stationary distributions. That autoregressions with such stationary distributions exist is not immediate. Our formulation implies that the conditional variance of each mixture component is not constant but of (nonlinear) ARCH type. Compared to previous mixture autoregressive models our model may therefore be useful in applications where the data exhibits rather strong conditional heteroskedasticity. Our formulation also has the theoretical advantage that conditions for stationarity and ergodicity are always met and these properties are much more straightforward to establish than is common in nonlinear autoregressive models. An empirical example employing a realized kernel series based on S&P 500 data shows that the proposed model performs well in volatility forecasting.”

Time: 12:15
Place: Lecture Hall XVI (Quantum 1st floor)

All interested are very welcome!

For more information on the seminar and future schedule is here.