The study determines if information extracted from a big data set that includes limit order book (LOB) and Dow Jones corporate news can help to improve realised volatility forecasting for 23 NASDAQ tickers over the sample from 28 June 2007 to 17 November 2016. The out-of-sample forecasting...

We propose a new class of conditional heteroskedasticity in the volatility (CH-V) models which allows for time-varying volatility of volatility in the volatility of asset returns. This class nests a variety of GARCH-type models and the SHARV model of Ding (2021b). CH-V models can be seen as a...

This Appendix contains details on several technical points and additional empirical results. Sections in this Appendix are indexed by letters and formulas/tables/figures by a letter followed by a number (e.g. A.1). Sections and formulas/tables/figures of the paper are referenced by numbers. In...

Several models have been developed to capture the dynamics of the conditional correlations between time series of financial returns and several studies have shown that the market volatility is a major determinant of the correlations. We extend some models to include explicitly the dependence of...

This paper proposes a Near Explosive Random-Coefficient autoregressive model for asset pricing which accommodates both the fundamental asset value and the recurrent presence of autonomous deviations or bubbles. Such a process can be stationary with or without fat tails, unit-root nonstationary...

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...

We survey the literature on stock return forecasting, highlighting the challenges faced by forecasters as well as strategies for improving return forecasts. We focus on U.S. equity premium forecastability and illustrate key issues via an empirical application based on updated data. Some studies...

This study develops a novel generalised seasonality test that utilises sequential dummy variable regressions for seasonality periodicity equal to prime numbers. It allows both to test for existence of any seasonal patterns against the broad null hypothesis of no seasonality and to isolate most...

The estimation of market impact is crucial for measuring the information content of trades and for transaction cost analysis. Hasbrouck's (1991) seminal paper proposed a Structural-VAR (S-VAR) to jointly model mid-quote changes and trade signs. Recent literature has highlighted some pitfalls of...

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...