This paper proposes the use of a double correlation coefficient as a nonparametric measure of phase-dependence in time-varying correlations. An asymptotically Gaussian test statistic for the null hypothesis of no phase-dependence is derived from the proposed measure. Finite-sample distributions,...

Availability of high frequency data has improved the capability of computing volatility in an efficient way. Nevertheless, measuring volatility/covariance from the observation of the asset price is challenging for two main reasons: observed asset prices are generally affected by noise...

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We consider the problem of constructing global minimum variance portfolios based on the constituents of the S&P 500 over a four-year period covering the 2008 financial...

We propose a new estimator for the integrated covariance of two Ito semimartingales observed at a high-frequency. This new estimator, which we call the pre-averaged truncated Hayashi-Yoshida estimator, enables us to separate the sum of the co-jumps from the total quadratic covariation even in...

This paper revisits the fractional cointegrating relationship between ex-ante implied volatility and ex-post realized volatility. We argue that the concept of corridor implied volatility (CIV) should be used instead of the popular model-free option-implied volatility (MFIV) when assessing the...

Many statistical applications require the forecast of a random variable of interest over several periods into the future. The sequence of individual forecasts, one period at a time, is called a path forecast, where the term path refers to the sequence of individual future realizations of the...

The objective of this paper is to suggest a visual method for identifying departures from normality of the innovations in times series models. The method is based on replacing the variance by the Gini as the measure of variability. The Gini methodology is a rank-based methodology, which takes...

The asymptotic behaviour of the empirical copula constructed from residuals of stochastic volatility models is studied. It is shown that if the stochastic volatility matrix is diagonal, then the empirical copula process behaves like if the parameters were known, a remarkable property. However,...

We present a general method to detect and extract from a finite time sample statistically meaningful correlations between input and output variables of large dimensionality. Our central result is derived from the theory of free random matrices, and gives an explicit expression for the interval...