Journal of Risk

Farid AitSahlia    
Warrington College of Business, University of Florida

This issue of The Journal of Risk covers optimal trade execution, mortgage prepayment risk, the impact of climate transition on portfolio selection, and the relationship between stock price volatility and earnings announcements.

In “Optimal trade execution with unknown drift”, our first paper, Martin Forde accounts for the price impact of trades when the drift of the asset price is unknown by exploiting results from nonlinear filtering theory. Forde’s approach adapts known results for optimal trading strategies with linear temporary price impact, exponential resilience or proportional transaction costs. Through this method, he shows that an unknown drift can result in an optimal liquidation strategy that is highly stochastic, in contrast to the deterministic solution when the drift is known.

In the issue’s second paper, “The prediction of mortgage prepayment risks in the early stages of loan origination: a machine learning approach”, Zilong Liu and Hongyan Liang propose a machine learning model that identifies the drivers of early mortgage prepayments. In contrast to traditional studies, their paper focuses on the individual loan level and finds that the loan-to-value ratio, credit score and initial interest rate are critical in predicting early prepayment. In addition, Liu and Liang find that prepayment risk varies according to lender type, therefore suggesting that mortgage prepayment risk mitigation would vary accordingly.

The third paper in this issue, “The effects of climate transition risk on an investment portfolio” by Marco J. van der Burgt, incorporates scenarios from the Network for Greening the Financial System to predict climate transition vulnerability factors (TVFs). These, in turn, are used in stress tests to estimate the impact of climate transition on equity and bond prices. As these effects vary significantly from severe (eg, chemical, energy and transportation firms) to minimal (eg, services), this study highlights the importance of accounting for climate transition in portfolio selection.

Arjun K. M., Mike Lipkin and Leon Tatevossian close out this issue with “Earnings moves and pre-earnings implied volatility”, in which they conduct an empirical study that compares the behavior of the implied volatility of short- and intermediate-term options one (business) day prior to earnings announcements. They observe that, in most cases, options markets correctly anticipate the magnitude of price changes in relation to earnings announcements but with a significant number of outliers. As these findings appear stable across a moderately long timescale, they offer an additional perspective for portfolio management that may be useful to the trading community.