The aim of this paper is to provide a new perspective on the nonparametric co-integration analysis for integrated processes of the second order. Our analysis focus on a pair of random matrices related to such integrated process. Such matrices are constructed by introducing some weight functions....

A new multivariate random walk model with slowly changing parameters is introduced and investigated in detail. Nonparametric estimation of local covariance matrix is proposed. The asymptotic distributions, including asymptotic biases, variances and covariances of the proposed estimators are...

In this paper we consider the dynamics of spot and futures prices in the presence of arbitrage. We propose a partially linear error correction model where the adjustment coefficient is allowed to depend non-linearly on the lagged price difference. We estimate our model using data on the DAX...

This paper considers the dynamics of spot and futures prices in the presence of arbitrage. A partially linear error correction model is proposed where the adjustment coefficient is allowed to depend non-linearly on the lagged price difference. The model is estimated using data on the DAX index...

This paper considers the dynamics of spot and futures prices in the presence of arbitrage. A partially linear error correction model is proposed where the adjustment coefficient is allowed to depend non-linearly on the lagged price difference. The model is estimated using data on the DAX index...

In this paper we consider the dynamics of spot and futures prices in the presence of arbitrage. We propose a partially linear error correction model where the adjustment coefficient is allowed to depend non-linearly on the lagged price difference. We estimate our model using data on the DAX...

This paper considers the dynamics of spot and futures prices in the presence of arbitrage. A partially linear error correction model is proposed where the adjustment coefficient is allowed to depend non-linearly on the lagged price difference. The model is estimated using data on the DAX index...

The paper considers various extended asymmetric multivariate conditional volatility models, and derives appropriate regularity conditions and associated asymptotic theory. This enables checking of internal consistency and allows valid statistical inferences to be drawn based on empirical...

We derive multivariate risk-neutral asset distributions for major US financial institutions (FIs) using option implied marginal risk-neutral asset distributions (RNDs) and probabilities of default (PoDs). The multivariate densities are estimated by combining the entropy approach, dynamic copulas...

In this paper, we extend copula-based univariate time series models studied in Chen & Fan (2006) to multivariate time series. Doing so, we tackle at the same time serial dependence as well as interdependence between several time series. The proposed methodology is totally different from the...