J.P. Morgan & Co.

About: J.P. Morgan & Co. is a based out in . It is known for research contribution in the topics: Portfolio & Implied volatility. The organization has 328 authors who have published 436 publications receiving 14291 citations.

Towards Self-Regulating AI: Challenges and Opportunities of AI Model Governance in Financial Services

Abstract: AI systems have found a wide range of application areas in financial services. Their involvement in broader and increasingly critical decisions has escalated the need for compliance and effective model governance. Current governance practices have evolved from more traditional financial applications and modeling frameworks. They often struggle with the fundamental differences in AI characteristics such as uncertainty in the assumptions, and the lack of explicit programming. AI model governance frequently involves complex review flows and relies heavily on manual steps. As a result, it faces serious challenges in effectiveness, cost, complexity, and speed. Furthermore, the unprecedented rate of growth in the AI model complexity raises questions on the sustainability of the current practices. This paper focuses on the challenges of AI model governance in the financial services industry. As a part of the outlook, we present a system-level framework towards increased self-regulation for robustness and compliance. This approach aims to enable potential solution opportunities through increased automation and the integration of monitoring, management, and mitigation capabilities. The proposed framework also provides model governance and risk management improved capabilities to manage model risk during deployment.

Do Asset Prices Reflect Fundamentals? Freshly Squeezed Evidence from the Fcoj Market

Abstract: The behavioral finance literature cites the frozen concentrated orange juice (FCOJ) futures market as a prominent example of the failure of prices to reflect fundamentals. This paper reexamines the relation between FCOJ futures returns and fundamentals, focusing primarily on temperature. We show that when theory clearly identifies the fundamental, i.e., at temperatures close to or below freezing, there is a close link between FCOJ prices and that fundamental. Using a simple, theoretically-motivated, nonlinear, state dependent model of the relation between FCOJ returns and temperature, we can explain approximately 50% of the return variation. This is important because while only 4.5% of the days in winter coincide with freezing temperatures, two-thirds of the entire winter return variability occurs on these days. Moreover, when theory suggests no such relation, i.e., at most temperature levels, we show empirically that none exists. The fact that there is no relation the majority of the time is good news for the theory and for market efficiency, not bad news. In terms of residual FCOJ return volatility, we also show that other fundamental information about supply, such as USDA production forecasts and news about Brazil production, generate significant return variation that is consistent with theoretical predictions. The fact that, even in the comparatively simple setting of the FCOJ market, it is easy to erroneously conclude that fundamentals have little explanatory power for returns serves as an important warning to researchers who attempt to interpret the evidence in markets where both fundamentals and their relation to prices are more complex.

Asymptotics for the Discrete-Time Average of the Geometric Brownian Motion and Asian Options

Abstract: The time average of geometric Brownian motion plays a crucial role in the pricing of Asian options in mathematical finance. In this paper we consider the asymptotics of the discrete-time average of a geometric Brownian motion sampled on uniformly spaced times in the limit of a very large number of averaging time steps. We derive almost sure limit, fluctuations, large deviations, and also the asymptotics of the moment generating function of the average. Based on these results, we derive the asymptotics for the price of Asian options with discrete-time averaging in the Black–Scholes model, with both fixed and floating strike.

Real Estate’s Evolution as an Asset Class

Abstract: In this introductory article, the editors of this issue examine the impact of the credit crisis on commercial and multifamily real estate and provide a post-crisis, evolutionary view of real estate as an asset class. This is done by putting the events of the last 18 months into a broad perspective and within a framework that encompasses these major themes: 1) capital market integration, financial leverage, and sentiment; 2) the “failure” of diversification; 3) innovations in real estate portfolio risk measurement and management; and 4) international real estate. The aim of the article is to help investors understand the evolving characteristics of the asset class and to help them improve the effectiveness of actions that anticipate, monitor, and manage risk. It also sets the stage for the collection of articles in this issue that offer new insights into how commercial and multifamily real estate investing lines up with assumptions about market efficiency, outlier risks, theories of diversification, and longer-term asset and risk allocation.