Models based on factors such as size, value, or momentum are ubiquitous in asset pricing. Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid individual assets, this measure is...

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...

We provide the first comprehensive analysis of option information for pricing the cross-section of stock returns by jointly examining extensive sets of firm and option characteristics. Using portfolio sorts and high-dimensional methods, we show that certain option measures have significant...

Der Bestimmung risikoadäquater Diskontierungssätze kommt bei der Unternehmensbedeutung eine zentrale Bedeutung zu. Wird zu deren Bestimmung in der praktischen Anwendung das CAPM verwendet, gilt es dabei, risikolose Zinssätze und Risikoprämien zu bestimmen, für die erwartete Renditen des...

This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only...

A long tradition in macro finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop...

We use intraday data to compute weekly realized variance, skewness, and kurtosis for equity returns and study the realized moments' time-series and cross-sectional properties. We investigate if this week's realized moments are informative for the cross-section of next week's stock returns. We...

Bitcoin has been described as a decentralized, partially anonymous, virtual currency, not backed by any government or other legal entity. Bitcoins are highly liquid, have low transaction costs, and are very volatile. This paper will look at the behavior of the value of Bitcoins, forecast the...

This paper examines whether deep/machine learning can help find any statistical and/or economic evidence of out-of-sample bond return predictability when real-time, instead of fully-revised, macro variables are taken as predictors. First, when using pure real-time macro information alone, we...

We study market-timing strategies on a given portfolio to achieve a particular risk or return target. Targeting a constant risk level leads to increasing investment at better investment opportunities whereas targeting a constant expected return does the opposite. Theoretical and numerical...