This paper investigates whether multivariate crash risk is priced in the cross- section of expected stock returns. Motivated by a theoretical asset pricing model, we capture the multivariate crash risk of a stock by a combined measure based on its expected shortfall and its multivariate lower...

This paper investigates whether multivariate crash risk (MCRASH), defined as exposure to extreme realizations of multiple systematic factors, is priced in the cross-section of expected stock returns. We derive an extended linear model with a positive premium for MCRASH and we empirically confirm...

We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of...

This paper revisits the study of Cochrane (2005), to estimate the risk and returns of venture capital investments while correcting for the selection bias. We use an up-to-date dataset and enhance it to account for missing firm valuations using machine learning. The model is able to infer, with a...

The paper studies estimation of implied volatility and the impact of the choice of the corresponding risk-free rate proxy. We suggest to analyze the implied volatility and the risk-free rate proxy inferred in conjunction from the observed option prices. We formulate and solve an overdefined...

The paper studies the statistical properties of the evolution of the USD/HKD exchange rate during the period after the separation of strong and weak side convertibility undertakings, when the rate is confined to a specified corridor. We suggest a discrete time Markov model for the exchange rate...

This paper proposes new measures of the integrated variance, measures which use highfrequency bid-ask spreads and quoted depths. The traditional approach assumes that the mid-quote is a good measure of frictionless price. However, the recent high-frequency econometric literature takes the...

Observed high-frequency prices are contaminated with liquidity costs or market microstructure noise. Using such data, we derive a new asset return variance estimator inspired by the market microstructure literature to explicitly model the noise and remove it from observed returns before...

We introduce a high-dimensional structural time series model, where co-movement between the components is due to common factors. A two-step estimation strategy is presented, which is based on principal components in differences in a first step and state space methods in a second step. The...

Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a misspecified model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and...