This paper examines the role of pricing errors in linear factor pricing models, allowing for observed strong and semi-strong factors, and latent weak factors. It focusses on the estimation of ∅k = λk − μk which plays a pivotal role, not only in the estimation of risk premia but also in...

In this paper we are concerned with the role of factor strength and pricing errors in asset pricing models, and their implications for identification and estimation of risk premia. We establish an explicit relationship between the pricing errors and the presence of weak factors that are...

The arbitrage pricing theory (APT) attributes differences in expected returns to exposure to systematic risk factors, which are typically assumed to be strong. In this paper we consider two aspects of the APT. Firstly we relate the factors in the statistical factor model to a theoretically...

This paper decomposes the risk premia of individual stocks into contributions from systematic and idiosyncratic risks. I introduce an affine jump-diffusion model, which accounts for both the factor structure of asset returns and that of the variance of idiosyncratic returns. The estimation is...

This paper proposes an estimator of factor strength and establishes its consistency and asymptotic distribution. The proposed estimator is based on the number of statistically significant factor loadings, taking account of the multiple testing problem. We focus on the case where the factors are...

While Federal Reserve continues to normalize its monetary policy on the back of a strengthening U.S. economy, the possibility of mimicking U.S. policy actions and so the debate of monetary autonomy has been particularly heated in the most of developing countries, even in advanced economies. We...

We estimate a structural dynamic factor model on large panel quarterly data to analyse the spillovers of U.S. monetary policy to the advanced economies and emerging and frontier market economies. The estimated model suggests that monetary contraction in U.S. leads to a significant decrease in...

The level of daily stock returns is generally regarded as unpredictable. Instead of the level, we focus on the signs of these returns and generate forecasts using various statistical classification techniques, such as logistic regression, generalized additive models, or neural networks. The...

This paper examines the behaviour of Bitcoin returns and those of several other cryptocurrencies in the pre and post period of the introduction of the Bitcoin futures market. We use the principal component-guided sparse regression (PC-LASSO) model to analyze several sample sizes for the pre and...

Purpose: The main aim of this research was to discuss the relationship between the patents and the performance of listed companies, more particularly, to find out whether China patent's claim impacted on China listed company's stock return rate or not. It was because the claim played the most...