We provide a new framework for modeling trends and periodic patterns in high-frequency financial data. Seeking adaptivity to ever-changing market conditions, we enlarge the Fourier flexible form into a richer functional class: both our smooth trend and the seasonality are non-parametrically...

The episodes of stock market crises in Europe and the U.S.A.since the year 2000,and the fragility of the international stock markets,have sparked the interest of researchers in understanding and in modeling the markets’ rising volatilities in order to prevent against crises.Portfolio managers...

This paper analyzes vector autoregressive models (VAR) with multiple structural changes. One distinct feature of this paper is the explicit consideration of structural changes in the variance-covariance matrix, in addition to changes in the autoregressive coefficients. The model is estimated by...

This paper investigates the properties of Dickey-Fuller tests for seasonally unadjusted quarterly data when deterministic seasonality is present but it is neglected in the test regression. While for the random walk case the answer is straightforward, an extensive Monte Carlo study has to be...

The episodes of stock market crises in Europe and the U.S.A. since the year 2000,and the fragility of the New Technology sector after the explosion of the speculative bubble,have sparked the interest of researchers in understanding and in modeling this market’s high volatility to prevent...

In this article we investigate the theoretical behaviour of finite lag VAR(n) models fitted to time series that in truth come from an infinite order VAR(?) data generating mechanism. We show that overall error can be broken down into two basic components, an estimation error that stems from the...

We study the impact of the system dimension on commonly used model selection criteria (AIC,BIC, HQ) and LR based general to specific testing strategies for lag length estimation in VAR's. We show that AIC's well known overparameterization feature becomes quickly irrelevant as we move away from...

We consider the properties of nonlinear exponential smoothing state space models under various assumptions about the innovations, or error, process. Our interest is restricted to those models that are used to describe non-negative observations, because many series of practical interest are so...

This zip archive contains implementations of the trend-cycle-season filter in Eviews, Excel, and MatLab. The trend-cycle-season filter is another univariate method to decompose a time series into a trend, a cyclical and a seasonal component: the Trend-Cycle filter (TC filter) and its extension,...

This paper proposes a new univariate method to decompose a time series into a trend, a cyclical and a seasonal component: the Trend-Cycle filter (TC filter) and its extension, the Trend-Cycle-Season filter (TCS filter). They can be regarded as extensions of the Hodrick-Prescott filter (HP...