We consider time series models in which the conditional mean of the response variable given the past depends on latent covariates. We assume that the covariates can be estimated consistently and use an iterative nonparametric kernel smoothing procedure for estimating the conditional mean...

We consider time series models in which the conditional mean of the response variable given the past depends on latent covariates. We assume that the covariates can be estimated consistently and use an iterative nonparametric kernel smoothing procedure for estimating the conditional mean...

We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted...

Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing.I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set...

A time-series basis decomposition and trend extraction technique known as Empirical Mode Decomposition (EMD), designed for multi-scale time-frequency decomposition in non-stationary time-series settings, will be combined with Regularised Covariance Regression (RCR) methods to produce a framework...

We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional bag-of-words approach, the contextualized representation captures both the syntax and semantics of text, thus...

Investors show different behaviour in falling markets and in rising markets. This paper demonstrates that the beta of individual stocks varies across the entire return distribution and that the variation depends on the frequency of the returns. While there is a symmetric u-shape increase for...

The transformed-data maximum likelihood estimation (MLE) method for struc- tural credit risk models developed by Duan (1994) is extended to account for the fact that observed equity prices may have been contaminated by trading noises. With the presence of trading noises, the likelihood function...

We derive a nonparametric test for constant (continuous) beta over a fixed interval of time. Continuous beta is defined as the ratio of the continuous covariation between an asset and observable risk factor (e.g., the market return) and the continuous variation of the latter. Our test is based...