In this paper, we consider a class of time-varying panel data models with individual-specific regression coefficients and common factors where both the serial correlation and cross-sectional dependence among error terms can be present. Based on an initial estimator of factors, we propose a...

This paper develops a statistical theory to estimate an unknown factor structure based on financial high-frequency data. We derive an estimator for the number of factors and consistent and asymptotically mixed-normal estimators of the loadings and factors under the assumption of a large number...

High-frequency data can provide us with a quantity of information for forecasting, help to calculate and prevent the future risk based on extremes. This tail behaviour is very often driven by exogenous components and may be modelled conditional on other variables. However, many of these...

We design a novel empirical framework to examine market efficiency through out-of-sample(OOS) predictability. We frame the classic empirical asset pricing problem as a machine learningclassification problem. We construct classification models to predict return states. The prediction- based...

Demonstration of nonlinear nonparametric regression technique using R-package "NNS" and comparison to kernel based regression methods in goodness of fit, partial derivative estimation, and out-of-sample extrapolation

This paper considers an alternative way of structuring stochastic variables in a dynamic programming framework where the model structure dictates that numerical methods of solution are necessary. Rather than estimating integrals within a Bellman equation using quadrature nodes, we use nodes...

In the paper we suggest a wavelet methodology of asset classification into defensive and cyclical groups. We demonstrate that such tools of bivariate wavelet analysis like wavelet gain, coherency, phase-locking value, and amplitude correlation together with term structures of risk defined via...

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage...

We develop an estimator for latent factors in a large-dimensional panel of financial data that can explain expected excess returns. Statistical factor analysis based on Principal Component Analysis (PCA) has problems identifying factors with a small variance that are important for asset pricing....

This paper proposes sparse and easy-to-interpret proximate factors to approximate statistical latent factors. Latent factors in a large-dimensional factor model can be estimated by principal component analysis (PCA), but are usually hard to interpret. We obtain proximate factors that are easier...