This paper proposes a new model that captures the interaction between duration and magnitude of changes in asset prices, and thus provides a convenient framework to test statistically the existence of such relationship. The model is flexible and contains various well known models as special...

This paper finds positive evidence of return predictability and investment gains for individual corporate bonds for an extended period from 1973 to 2017. Our sample consists of both public and private company bond observations. We have implemented multiple machine learning methods and designed a...

The problems related to the application of multivariate GARCH models to a market with a large number of stocks are solved by restricting the form of the conditional covariance matrix. It contains one component describing the market and a second simple component to account for the remaining...

The problems related to the application of multivariate GARCH models to a market with a large number of stocks are solved by restricting the form of the conditional covariance matrix. It contains one component describing the market and a second simple component to account for the remaining...

We apply a multivariate multiplicative error model (MMEM) and investigate effects in the simultaneous processes of high-frequency return volatilities, trading volume, and trading intensities on the Italien Electronic Interbank Credit Market (e-MID). Analysing five minutes data from the Italian...

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...

Conventional measurements of risk premiums are biased if the estimation models are potentially misspecified and unstable. Say, factor interactions is one of the crucial omitted specifications that standard models cannot involve. Motivated by this argument, we propose an interpretable...

Several novel large volatility matrix estimation methods have been developed based on the high-frequency financial data. They often employ the approximate factor model that leads to a low-rank plus sparse structure for the integrated volatility matrix and facilitates estimation of large...

We propose a new modeling approach for the cross-section of returns. Our model, Factorization Asset Pricing Model (FAPM), allows for predictor interactions by introducing second-order observable characteristics interactions regarding the unobservable high-order loadings. If the characteristics...

Using a novel equity lending dataset, this paper is the first to show that expected returns strongly and negatively predict future equity lending fees. In comparing two expected return measures, I find that a rational expected return has stronger predictive power of future short selling activity...