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.

Interbank interest rates: Funding liquidity risk and XIBOR basis spreads

Abstract: This article presents a theoretical model for interbank money market (XIBOR) rates that endogenously generates the basis spreads that characterize post-crisis fixed income markets: XIBOR-OIS spreads, tenor basis spreads, and the forward basis. Our approach is based on an explicit modeling of interbank cash transactions where interbank credit and liquidity risk are factored in. The framework of this article offers a consistent, arbitrage-free explanation for the emergence of basis spreads. We also demonstrate that funding liquidity is a key determinant of post-crisis XIBOR rates and, in particular, tenor basis spreads.

Revisiting the Impact of Large Assets on Real Estate Portfolio Returns

Abstract: In this article, Esrig, Hudgins, and Cerreta investigate if large properties have outperformed the institutional property market over time. This topic is relevant for real estate investors and portfolio managers considering property size as a way to differentiate portfolio performance. The body of academic literature on large asset performance is inconclusive due to issues in applied methodologies and definitions. This study uses a new methodology that corrects for property type, stale appraisals, and restricts “large” to the relatively selective and well-defined group that would strike a knowledgeable institutional investor as truly large. The authors also look at performance of large assets across major and non-major markets. Key findings are that large assets, as most reasonably defined, have historically outperformed other properties in the NCREIF database on an absolute and a risk-adjusted basis. This finding applies to all three sectors the authors tested: office, multifamily, and retail. Property size remains an important factor after correcting for large asset overrepresentation in six major markets.

Unconditional Communication-Efficient MPC via Hall’s Marriage Theorem

Abstract: The best known n party unconditional multiparty computation protocols with an optimal corruption threshold communicates O(n) field elements per gate. This has been the case even in the semi-honest setting despite over a decade of research on communication complexity in this setting. Going to the slightly sub-optimal corruption setting, the work of Damgard, Ishai, and Kroigaard (EUROCRYPT 2010) provided the first protocol for a single circuit achieving communication complexity of \(O(\log |C|)\) elements per gate. While a number of works have improved upon this result, obtaining a protocol with O(1) field elements per gate has been an open problem.

Get real: realism metrics for robust limit order book market simulations

Abstract: Market simulation is an increasingly important method for evaluating and training trading strategies and testing "what if" scenarios. The extent to which results from these simulations can be trusted depends on how realistic the environment is for the strategies being tested. As a step towards providing benchmarks for realistic simulated markets, we enumerate measurable stylized facts of limit order book (LOB) markets across multiple asset classes from the literature. We apply these metrics to data from real markets and compare the results to data originating from simulated markets. We illustrate their use in five different simulated market configurations: The first (market replay) is frequently used in practice to evaluate trading strategies; the other four are interactive agent based simulation (IABS) configurations which combine zero intelligence agents, and agents with limited strategic behavior. These simulated agents rely on an internal "oracle" that provides a fundamental value for the asset. In traditional IABS methods the fundamental originates from a mean reverting random walk. We show that markets exhibit more realistic behavior when the fundamental arises from historical market data. We further experimentally illustrate the effectiveness of IABS techniques as opposed to market replay.