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.

Eurodollar futures pricing in log-normal interest rate models in discrete time

Abstract: We demonstrate the appearance of explosions in three quantities in interest rate models with log-normally distributed rates in discrete time. (1) The expectation of the money market account in the Black, Derman, Toy model, (2) the prices of Eurodollar futures contracts in a model with log-normally distributed rates in the terminal measure and (3) the prices of Eurodollar futures contracts in the one-factor log-normal Libor market model (LMM). We derive exact upper and lower bounds on the prices and on the standard deviation of the Monte Carlo pricing of Eurodollar futures in the one factor log-normal Libor market model. These bounds explode at a non-zero value of volatility, and thus imply a limitation on the applicability of the LMM and on its Monte Carlo simulation to sufficiently low volatilities.

Who Makes the Decision

Abstract: All banks have rules as to who can take credit decisions. They should be clear and specify levels of authority; numbers needed to make the decision; whether authority is cumulative, pooled or individual; whether authority relates to the transaction or total exposure, and any special requirements for specific types of credit exposure. Some banks use a separate structure for counterparty — or Treasury or investment banking — credit; this chapter will deal mainly with plain lending authority, with specialised requirements for counterparty or products covered in Chapter 6.

Sensitivities of asian options in the black–scholes model

Abstract: We propose analytical approximations for the sensitivities (Greeks) of the Asian options in the Black–Scholes model, following from a small maturity/volatility approximation for the option prices w...

Joint Self-Attention Based Neural Networks for Semantic Relation Extraction

Abstract: Relation extraction is an important task in NLP community. However, some models often fail in capturing Long-distance dependence on semantics, and the interaction between semantics of two entities is ignored. In this paper, we propose a novel neural network model for semantic relation classification called joint self-attention biLSTM (SA-Bi-LSTM) to model the internal structure of the sentence to obtain the importance of each word of the sentence without relying on additional information, and capture Long-distance dependence on semantics. We conduct experiments using the SemEval-2010 Task 8 dataset. Extensive experiments and the results demonstrated that the proposed method is effective against relation classification, which can obtain state-ofthe-art classification accuracy just with minimal feature engineering.

Explainable clustering and application to wealth management compliance

Abstract: Many applications from the financial industry successfully leverage clustering algorithms to reveal meaningful patterns among a vast amount of unstructured financial data. However, these algorithms suffer from a lack of interpretability that is required both at a business and regulatory level. In order to overcome this issue, we propose a novel two-steps method to explain clusters. A classifier is first trained to predict the clusters labels, then the Single Feature Introduction Test (SFTT) method is run on the model to identify the statistically significant features that characterize each cluster. We describe a real wealth management compliance use-case that highlights the necessity of such an interpretable clustering method. We illustrate the performance of the method using simulated data and through an experiment on financial ratios of U.S. companies.