Asymptotic properties of jump tests rely on the property that any jump occurs within a single time interval no matter what the observation frequency is. Market microstructure effects in relation to news-induced revaluation of the underlying variable is likely to make this an unrealistic...

The literature on spurious regressions has found that the t-statistic for testing the null of no relationship between two independent variables diverges asymptotically under a wide variety of nonstationary data generating processes for the dependent and explanatory variables. This paper...

This paper proposes a sequential procedure to determine the common cointegration rank of panels of cointegrated VARs. It shows how a panel of cointegrated VARs can be transformed in a set of independent individual models. The likelihood function of the transformed panel is the sum of the...

It has been found that the t-statistic for testing the null of no relationship between two independent variables diverges asymptotically under a wide variety of nonstationary data generating processes. This paper introduces a simple method which guarantees convergence of this t-statistic to a...

We derive the parameter restrictions that a standard equity market model implies for a bivariate vector autoregression for stock prices and dividends, and we show how to test these restrictions using likelihood ratio tests. The restrictions, which imply that stock returns are unpredictable, are...

The present paper tests for the existence of multicointegration between real per capita private consumption expenditure and real per capita disposable personal income in the USA. In doing so, we exploit the fact that the flows of disposable income and consumption expenditure on the one hand, and...

We provide a new framework for estimating the systematic and idiosyncratic jump tail risks in financial asset prices. The theory underlying our estimates are based on in-fill asymptotic arguments for directly identifying the systematic and idiosyncratic jumps, together with conventional...

This paper studies the impact of jumps on volatility estimation and inference based on various realised variation measures such as realised variance, realised multipower variation and truncated realised multipower variation. We review the asymptotic theory of those realised variation measures...

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative...

We propose a new and flexible non-parametric framework for estimating the jump tails of Itô semimartingale processes. The approach is based on a relatively simple-to-implement set of estimating equations associated with the compensator for the jump measure, or its "intensity", that only...