Empirical evidence suggests that many macroeconomic and financial time series are subject to occasional structural breaks. In this paper we present analytical results quantifying the effects of such breaks on the correlation between the forecast and the realization and on the ability to forecast...

Density forecasts have become quite important in economics and finance. For example, such forecasts play a central role in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density forecast evaluation framework as a simple alternative to other...

In some recent papers, such as Elliott & van der Hoek, Hu & Öksendal, a fractional Black-Scholes model have been proposed as an improvement of the classical Black-Scholes model. Common to these fractional Black-Scholes models, is that the driving Brownian motion is replaced by a fractional...

In June 2022, the Federal Reserve started reducing the size of its balance sheet, which had expanded to just under $9 trillion in response to the COVID-19 pandemic. However, whereas banks' reserves at the Federal Reserve have decreased, the investment of money market funds (MMFs) at the Federal...

This paper examines the role of pricing errors in linear factor pricing models, allowing for observed strong and semi-strong factors, and latent weak factors. It focusses on the estimation of ∅k = λk − μk which plays a pivotal role, not only in the estimation of risk premia but also in...

The discount control mechanisms that closed-end funds often choose to adopt before IPO are supposedly implemented to narrow the difference between share price and net asset value, We find evidence that non-discretionary discount control mechanisms such as mandatory continuation votes serve as...

Næs, Skjeltorp, and Ødegaard (2011) provide empirical evidence that stock market liquidity contains leading information about future economic activity. Their result suggests a rebalancing of small, increasingly illiquid to large stocks in recession times, an expression of...

We let "Algorithmic Market-Makers" (AMMs), using Q-learning algorithms, choose prices for a risky asset when their clients are privately informed about the asset payoff. We find that AMMs learn to cope with adverse selection and to update their prices after observing trades, as predicted by...

We study the prices of a firm's debt and equity in a market where investors have private information and may exhibit differences of opinion. We show how debt and equity valuations, and the impact of public information and distress risk on these valuations, depend upon disagreement and the...