We analyze the impact of time series dependence in market microstructure noise on the properties of estimators of the integrated volatility of an asset price based on data sampled at frequencies high enough for that noise to be a dominant consideration. We show that combining two time scales for...

I introduce a novel, hierarchical model of tail dependent asset returns which can be particularly useful for measuring portfolio credit risk within the structural framework. To allow for a stronger dependence within sub-portfolios than between them, I utilise the concept of nested Archimedean...

We put forward a Merton-type multi-factor portfolio model for assessing banks’ contributions to systemic risk. This model accounts for the major drivers of banks’ systemic relevance: size, default risk and correlation of banks’ assets as a proxy for interconnectedness. We measure systemic...

We show that including distribution costs into a general equilibrium model of international portfolio choice contributes to explaining the “home bias” in international equity investment. Our model is able to replicate observed investment positions for a wide range of parameter values, even...

We employ a life-cycle model with income risk to analyze how tax-deferred individual accounts affect households' savings for retirement. We consider voluntary accounts as opposed to mandatory accounts with minimum contribution rates. We contrast add-on accounts with carve-out accounts that...

This paper introduces a multivariate pure-jump Lévy process which allows for skewness and excess kurtosis of single asset returns and for asymptotic tail dependence in the multivariate setting. It is termed Variance Compound Gamma (VCG). The novelty of my approach is that, by applying a...

This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...

This paper gives a brief survey of forecasting with panel data. Starting with a simple error component regression and surveying best linear unbiased prediction under various assumptions of the disturbance term. This includes various ARMA models as well as spatial autoregressive models. The paper...

In this paper, we derive two shrinkage estimators for the global minimum variance portfolio that dominate the traditional estimator with respect to the out-of-sample variance of the portfolio return. The presented results hold for any number of observations d + 2 and number of assets d 4. The...