We propose a novel copula approach to producing density forecasts of economic aggregates combining models using disaggregate data. Our copula approach is more flexible compared to existing techniques, because it is applicable to any econometric model that produces density forecasts. We construct...

In this paper, we analyse Okun's law - a relation between the change in the unemployment rate and GDP growth - using data from Australia, the euro area, the United Kingdom and the United States. More specifically, we assess the relevance of non-Gaussianity when modelling the relation. This is...

This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which are based on a weighted threshold and...

In this paper, we introduce a flexible and widely applicable nonparametric entropy-based testing procedure that can be used to assess the validity of simple hypotheses about a specific parametric population distribution. The testing methodology relies on the characteristic function of the...

A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....

Assessing the state of the economy in real time is critical for policy-making, and understanding the risks to those assessments is equally important. Policy-makers are typically provided with point forecasts that contain insufficient information about risks. In contrast, predictive densities...

We introduce a structural vector autoregressive (SVAR) model with each of the mutually independent errors following a skewed generalized t-distribution that is more flexible than a Student's t-distribution typically considered. Hence, the effect of potential distributional misspecification is...

This paper focuses on developing and adapting statistical models of counts (non-negative integers) in the context of panel data and using them to analyze the relationship between patents and R&D expenditures. The model used is an application and generalization of the Poisson distribution to...

The paper develops a tail risk forecasting model that incorporates the wealth of economic and financial information available to risk managers. The approach can be viewed as a regularized extension of the two-stage GARCH-EVT model of McNeil and Frey (2000) where we permit a time-varying...

At the core of securities financing transaction modeling is computing the distribution of the borrower default contingent market losses. Typically, the borrower’s credit spread is modeled after the lognormal model and the asset price dynamics is governed by a correlated jump diffusion model....

We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters...

In this paper we propose a new pricing methodology for European multi-asset options based on the family of normal mean-variance mixture copulas. The goal is to develop a copula-based method with the flexibility to reproduce the correlation skew, and at the same time efficient enough to be used...

We develop a data-rich measure of expected macroeconomic skewness in the US economy. Expected macroeconomic skewness is strongly procyclical, mainly reflects the cyclicality in the skewness of real variables, is highly correlated with the cross-sectional skewness of firm-level employment growth,...

This paper investigates whether multivariate crash risk (MCRASH), defined as exposure to extreme realizations of multiple systematic factors, is priced in the cross-section of expected stock returns. We derive an extended linear model with a positive premium for MCRASH and we empirically confirm...

I propose a new model, conditional quantile regression (CQR), that generates density forecasts consistent with a specific view of the future evolution of some variables. This addresses a shortcoming of existing quantile regression-based models, for example the at-risk framework popularised by...

In this paper, we analysed the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigated the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects and/or non-Gaussianity. Using monthly data from...

We fit U.S. stock market volatilities on macroeconomic and financial market indicators and some industry level financial ratios. Stock market volatility is non-Gaussian distributed. It can be approximated by an inverse Gaussian (IG) distribution or it can be transformed by Box-Cox transformation...

This paper proposes a semiparametric realized stochastic volatility model by integrating the parametric stochastic volatility model utilizing realized volatility information and the Bayesian nonparametric framework. The flexible framework offered by Bayesian nonparametric mixtures not only...

The Kies probability model was proposed as an alternative to the extended Weibull models as it provides a more efficient fit to some real-life data sets in comparison to the aforementioned models. The paper proposes classical and Bayesian inferences for the Kies distribution based on records....

This paper suggests a new approach to evaluate realized covariance (RCOV) estimators via their predictive power on return density. By jointly modeling returns and RCOV measures under a Bayesian framework, the predictive density of returns and ex-post covariance measures are bridged. The forecast...

It is generally said that out-of-the-money call options are expensive and one can ask the question from which moneyness level this is the case. Expensive actually means that the price one pays for the option is more than the discounted average payoff one receives. If so, the option bears a...

Scaling behavior measured in cross-sectional studies through the tail index of a power law is prone to a bias. This hampers inference; in particular, time variation in estimated tail indices may be erroneous. In the case of a linear factor model, the factor biases the tail indices in the left and...

Based on intraday data for a large cross-section of individual stocks and Exchange traded funds, we show that short-term as well as long-term fluctuations of realized market and average idiosyncratic higher moments risks are priced in the crosssectionof asset returns. Specifically, we find that...

Top distributions of income and wealth are still incompletely measured in many national statistics, particularly when using survey data. This paper develops the technique of incorporating the joint distributional relationship to enhance the estimation of these two top distributions by using the...

We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters (even...

We develop novel multivariate time series models using Bayesian additive regression trees that posit nonlinear relationships among macroeconomic variables, their lags, and possibly the lags of the errors. The variance of the errors can be stable, driven by stochastic volatility (SV), or follow a...

If the elements of the choice set in a decision model involving randomness are not arbitrary, but restricted appropriately, an expected utility ordering of them can be represented by a mean standard deviation ranking function. These restrictions can apply to the form of, or can specify...

After outlining characteristics of Japan's distribution sector, a comprehensive international comparison of it to those of other nations is presented and analyzed for underlying differences. This leads to an explanation of Japan's retail store density, which is then related to the structure of...