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.

CMBS Loan Defaults

Abstract: This examination of several empirical models that allow for loan seasoning and recession uses data from a recent Fitch CMBS loan default study Loan seasoning produces a permanent increase in default rates, while the recession bump in defaults is temporary Thus, different views of the relative importance of seasoning and the recession imply optimistic and pessimistic views of the future

SURF: Improving classifiers in production by learning from busy and noisy end users

Abstract: Supervised learning classifiers inevitably make mistakes in production, perhaps mis-labeling an email, or flagging an otherwise routine transaction as fraudulent. It is vital that the end users of such a system are provided with a means of relabeling data points that they deem to have been mislabeled. The classifier can then be retrained on the relabeled data points in the hope of performance improvement. To reduce noise in this feedback data, well known algorithms from the crowdsourcing literature can be employed. However, the feedback setting provides a new challenge: how do we know what to do in the case of user non-response? If a user provides us with no feedback on a label then it can be dangerous to assume they implicitly agree: a user can be busy, lazy, or no longer a user of the system! We show that conventional crowdsourcing algorithms struggle in this user feedback setting, and present a new algorithm, SURF, that can cope with this non-response ambiguity.

Risk Managing Smile Risk with SABR Model

Abstract: In this presentation, we discuss alternative parameterisations of SABR model, the meaning of SABR parameters, the computation of smile Value At Risk using SABR model, and at the risk management implications of the inaccuracy of SABR formula.

A New Heston Based Stochastic Volatility Model for Stock Price and Option Pricing

Abstract: In this paper, we propose a new Heston based stochastic volatility model for stock price and option pricing, which not only captures the volatility smile, but also naturally captures the stochastic volatility of volatility. It’s more empirically consistent to both historical stock price and equity option market than many existed models. It’s especially promising to price Equity cliquet products or credit-equity hybrid products and may shed some light on credit-equity relative value trading strategy.

Paying down metadata debt: learning the representation of concepts using topic models

Abstract: We introduce a data management problem called metadata debt, to identify the mapping between data concepts and their logical representations. We describe how this mapping can be learned using semisupervised topic models based on low-rank matrix factorizations that account for missing and noisy labels, coupled with sparsity penalties to improve localization and interpretability. We introduce a gauge transformation approach that allows us to construct explicit associations between topics and concept labels, and thus assign meaning to topics. We also show how to use this topic model for semisupervised learning tasks like extrapolating from known labels, evaluating possible errors in existing labels, and predicting missing features. We show results from this topic model in predicting subject tags on over 25,000 datasets from this http URL, demonstrating the ability to learn semantically meaningful features.