This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies...

This paper attempts to investigate if adopting accurate forecasts from Neural Network (NN) models can lead to statistical and economically significant benefits in portfolio management decisions. In order to achieve that, three NNs, namely the Multi-Layer Perceptron (MLP), Recurrent Neural...

We build a class of copula models that captures time-varying dependence across large panels of financial assets. Our models nest Gaussian, Student's t, grouped Student's t, and generalized hyperbolic copulas with time-varying correlations matrices, as special cases. We introduce time-variation...

The key to multiple testing is to respect the dependencies between the marginal hypotheses tests. Any dependency structure can be modeled by so-called copula functions. This makes copulas an interesting tool in multiple testing. In particular, it is possible to explicitly utilize the dependency...

Public debates about the rise in top income shares often focus on the growing dispersion in earnings and the soaring pay for top executives and financial-sector employees. But can the change in the marginal distribution of earnings on its own explain the rise in top income shares? Are top...

We study the dependence structure of share price returns among the Beijing Bank, Ningbo Bank, and Nanjing Bank using copula models. We use the normal, Student’s t, rotated Gumbel, and symmetrized Joe-Clayton (SJC) copula models to estimate the underlying dependence structure in two periods:...