After the financialization of commodity futures markets in 2004-05 oil volatility has become a strong predictor of returns and volatility of the overall stock market. Furthermore, stocks' exposure to oil volatility risk now drives the cross-section of expected returns. The difference in average...

We study the links between expectations, fundamentals, and asset returns using the rich empirical setup offered by commodity markets. We find that survey-based expectations predict future fundamentals, but are not significant predictors of future returns. Expectations of returns are correlated...

For a Lévy process corrupted with microstructure noise, I derive the sampling distributions for the information-related and information-unrelated pricing error parameters and for the variance of latent true price returns (a noise-robust and consistent estimator of realized variance). The test...

Based on the Partial Distribution (Feng Dai, 2001), a new model to price an asset (MPA) is given. Going a step further, this paper puts forward the Multivariate Partial Distribution (MPD) for the first time. By use of MPD, we could gain a new kind of model for pricing the group assets (MPGA), in...

We develop a new approach to identify model misspecifications based on Minimum Discrepancy (MD) projections that correct asset pricing models with the use of nonlinear functions of basis assets returns. These nonlinear corrections make our method more effective than the Hansen and Jagannathan...

The purpose of this paper is to propose a general econometric approach to no-arbitrage asset pricing modelling based on three main ingredients: (i) the historical discrete-time dynamics of the factor representing the information, (ii) the Stochastic Discount Factor (SDF), and (iii) the...

To facilitate crossing from the "black box" to "glass box" in the application of neural networks (NNs), we develop a variable significant test for the multi-layer perceptrons. To derive the test statistic and its asymptotic distribution, we provide the consistency of the multi-layer perceptrons...

In an exchange economy with recursive preferences (Epstein and Zin, 1989), we propose a novel nonparametric generalized method of moment (GMM) series approach to estimate unknown policy functions which are recursively specified in a system of nonlinear conditional expectation models...

To facilitate crossing from the "black box" to "glass box" in the application of neural net- works, we extend Horel and Giesecke (2020) and develop a variable/feature significant test for multi-layer perceptrons (MLP). The proposed test permits one to assess the statistical significance of the...

In this paper we approximate the risk factors of a polynomial arbitrage-free dynamic term structure model by running a sequential set of linear regressions independent across time. This approximation avoids the cost of a full optimization procedure allowing for a simple method to extract the...