We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions...

This paper is concerned with statistical inference and model evaluation in possibly misspecified and unidentified linear asset-pricing models estimated by maximum likelihood and one-step generalized method of moments. Strikingly, when spurious factors (that is, factors that are uncorrelated with...

Based on intraday data for a large cross-section of individual stocks and Exchange traded funds, we show that short-term as well as long-term fluctuations of realized market and average idiosyncratic higher moments risks are priced in the crosssectionof asset returns. Specifically, we find that...

The volatility specification of the Markov-switching Multifractal (MSM) model is proposed as an alternative mechanism for realized volatility (RV). We estimate the RV-MSM model via Generalized Method of Moments and perform forecasting by means of best linear forecasts derived via the...

We study the well-known multiplicative Lognormal cascade process in which the multiplication of Gaussian and Lognormally distributed random variables yields time series with intermittent bursts of activity. Due to the non-stationarity of this process and the combinatorial nature of such a...

We propose a new methodology for abnormal return detection and correction, and evaluate the economic impacts of outliers on asset allocations with higher-order moments (Cf. Jurczenko et al., 2008). Indeed, extreme returns and outliers greatly affect empirical higher-order moment estimations (Cf....

The presence of time series momentum effect has been widely documented in the financial markets across asset classes and countries. We find a predictable pattern of the realized semi-variance to the future individual asset return, especially during the stressed states of time series momentum...

To capture the well documented time series momentum and reversal in asset price, we develop a continuous-time asset price model, derive the optimal investment strategy theoretically, and test the strategy empirically. We show that, by combining market fundamentals and timing opportunity with...

We propose a continuous-time heterogeneous agent model consisting of fundamental, momentum, and contrarian traders to explain the significant time series momentum. We show that the market under-reacts in short-run and over-reacts in long-run when momentum traders dominate the market, which...

We develop a novel machine learning method to estimate large dimensional time-varying GMM models via our newly designed ridge fusion regularization scheme. Our method is a one-step procedure and allows for abrupt, smooth and dual type time variation with a fast rate of convergence. It...