We use a standard consumption-based asset pricing model incorporating conditioning information to explain the risk-return profile of currency carry trade portfolios. We use a scaled stochastic discount factor instead of scaled or managed portfolio returns as in previous work. Our conditioning...

We use a standard consumption-based asset pricing model incorporating conditioning information to explain the risk-return profile of currency carry trade portfolios. We use a scaled stochastic discount factor instead of scaled or managed portfolio returns as in previous work. Our conditioning...

This paper proposes a risk measure, based on first-passage probability, which reflects intra-horizon risk in jump models with finite or infinite jump activity. Our empirical investigation shows, first, that the proposed risk measure consistently exceeds the benchmark Value-at-Risk (VaR). Second,...

Using a two-period model of a commodity market with a large number of atomistic consumers and two strategic sellers, we show that a speculator with access to storage can lower the market price while buying and raise the price while selling by clever use of limit, stop-loss, and market orders....

In over-the-counter (OTC) markets, customers search for counterparties. Little is known about this process, however, because existing data is comprised of transaction records, which are only informative about the end of a successful search. Leveraging data from the leading trading platform for...

Market participants often invest in order to acquire information that pertains to the market itself (e.g. order flow) rather than to fundamentals. This enables them to infer more information from past trades. I show that agents trading on such information, typically high-frequency traders,...

We examine the effects of political uncertainty surrounding the outcome of U.S. presidential elections on financial market quality. We postulate those effects to depend on a positive relation between political uncertainty and information asymmetry among investors, ambiguity about the quality of...

The emergence of algorithmic high-frequency trading in the market for credit risk affords accurate inference of new risk measures. When combined with machine learning predictive methods, these measures forecast substantial future changes in firms' credit and equity risk premiums in...

Many market participants invest resources to acquire information about liquidity rather than fundamentals. I show that agents using such information can reduce the magnitude of short-lived pricing errors by trading against liquidity shocks. However, the short-run stabilizing effect of this...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...