We detail and illustrate time series analysis and spectral inference in autoregressive models with a focus on the underlying latent structure and time series decompositions. A novel class of priors on parameters of latent components leads to a new class of smoothness priors on autoregressive coe cients, provides for formal inference on model order, including very high order models, and leads to...

The paper suggests a CUSUM-type test for time-varying parameters in a recently proposed spatial autoregressive model for stock returns and derives its asymptotic null distribution as well as local power properties. As can be seen from Euro Stoxx 50 returns, a combination of spatial modelling and change point tests allows for superior risk forecasts in portfolio management. JEL Classification: C...

This paper addresses optimal estimation for time varying autoregressive (TVAR) models. First, we propose a statistical model on the time evolution of the frequencies, moduli and real poles instead of a standard model on the AR coefficients as it makes more sense from a physical viewpoint. Second, optimal estimation involves solving a complex optimal filtering problem which does not admit any cl...

We consider an interstage dependent stochastic process whose components follow an autoregressive model with time varying order. At a given time, we give some recursive formulæ linking future values of the process with past values and noises. We then consider multistage stochastic linear programs with uncertain sets depending affinely on such processes. At each stage, dealing with uncertainty us...

Motivated by recent developments in light of the sub-prime and subsequent financial crisis we fit two different vector autoregressive generalized conditional heteroscedastic (VAR-GARCH) models to three financial indices with the aim of understanding the development of dependency structures between credit spreads and other macroeconomic variables. Our analysis includes daily quotes from June 200...

We propose a new model for dynamic volatilities and correlations of skewed and heavytailed data. Our model endows the Generalized Hyperbolic distribution with time-varying parameters driven by the score of the observation density function. The key novelty in our approach is the fact that the skewed and fat-tailed shape of the distribution directly affects the dynamic behavior of the time-varyin...

Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. Th...

This paper develops a method for pricing bivariate contingent claims under General Autoregressive Conditionally Heteroskedastic (GARCH) process. As the association between the underlying assets may vary over time, the dynamic copula with time-varying parameter offers a better alternative to any static model for dependence structure and even to the dynamic copula model determined by dynamic depe...

We address the issue of time varying persistence of shocks to macroeconomic time series variables by proposing a new and parsimonious time series model. Our model assumes that this time varying persistence depends on a linear combination of lagged explanatory variables, where this combination characterizes the business cycle regimes. The key feature of our model is that an autoregressive parame...