In the paper we introduce an empirical approximation of the log-optimal investment strategy that guarantees an almost optimal growth rate of investments. The proposed strategy also considers the effects of portfolio rearrangement costs on growth optimality and advises a suboptimal portfolio for...

Regulators in the U.S. and Europe have called for quantitative risk retention requirements to address some of the shortcomings of securitized products, which contributed to the credit crisis but remain an integral component of financial markets. The paper explores the conflicts in the...

We develop a systematic approach for evaluating asset pricing models based on the second Hansen-Jagannathan distance (HJD), which requires a good asset pricing model to not only have small pricing errors but also be arbitrage free. Our approach includes a specification test and a sequence of...

Value-at-Risk (VaR) has been widely promoted by the Bank for International Settlement (BIS) as well as central banks of all countries as a way of monitoring and managing market risk and as a basis for setting regulatory minimum capital standards. The revised Basle Accord, implemented in January...

Modeling the joint term structure of interest rates in the United States and the European Union, the two largest economies in the world, is extremely important in international finance. In this paper, we provide both theoretical and empirical analysis of multi-factor joint affine term structure...

The Basel Committee regulations require the estimation of Value-at-Risk at 99% confidence level for a 10-trading-day-ahead forecasting horizon. The paper provides a multivariate modelling framework for multi-period VaR estimates for leptokurtic and asymmetrically distributed real-estate...

In this study, we aim to build better risk models for energy commodities by employing statistical procedures to identify outliers in the prices for all crude oil and natural gas futures contracts traded on the CME over the period of December 2003 through March 2017. Our results show that it is...

We examine machine learning and factor-based portfolio optimization. We find that factors based on autoencoder neural networks exhibit a weaker relationship with commonly used characteristic-sorted portfolios than popular dimensionality reduction techniques. Machine learning methods also lead to...

Using the Tsay (1988) outlier identification methodology on daily log-returns of 16 commodity spot price series and 25 commodity index series, this study assesses the impact significant and unexpected news announcements had on volatility between January 1, 1997 and December 31, 2007. Results...

The predictability of a high-dimensional time series model in forecasting with large information sets depends not only on the stability of parameters but also depends heavily on the active covariates in the model. Since the true empirical environment can change as time goes by, the variables...