Senior Director, Product Management
Nihil Patel is a Senior Director within the enterprise risk services division at Moody's Analytics. He serves as the business lead driving our product strategy related to credit portfolio analytics. Nihil has broad experience in research, modeling, service delivery, and customer engagement.
Prior to his current role, Nihil spent nine years in the research organization leading the portfolio modeling services team as well as the correlation research team. Nihil holds a MSE in Operations Research and Financial Engineering from Princeton University and a BS in Industrial Engineering and Operations Research from UC Berkeley.
In this presentation, our experts discussed common CECL considerations for structured credit and answer key questions on how to provide CECL estimates for structured credit.
In this fifth webinar in our series, our experts discussed common CECL considerations for structured credit and answered key questions on how to provide CECL estimates for structured credit.
In this American Banker webinar, Moody's Analytics discusses potential approaches for firms to expand on their current sensitivity analysis and stress testing for CECL implementation.
To ease the transition to CECL, firms can leverage and align existing risk management practices. Institutions are in the process of trying to determine which methodologies can be expanded to meet the CECL impairment model requirements, while retaining a consistency between other regulatory and risk management activities.
In this webinar, expert Nihil Patel, outlines how institutions can leverage Basel and Stress Testing models to comply with FASB’s new impairment accounting standards.
Learn how Moody’s Analytics is helping institutions of all sizes address the challenges of implementing the IFRS 9 impairment model.
In this presentation, expert Nihil Patel, outlines how institutions can leverage Basel and Stress Testing models to comply with FASB's new impairment accounting standards.
In this webinar we will discuss different approaches in credit portfolio management, dangers of only using regulatory capital when optimizing your portfolio, how to appropriately incorporate regulatory capital considerations, and metrics to consider when optimizing your portfolio and setting appropriate limits.
This article provides an overview of the new standard and analyzes the major challenges financial institutions will face in ensuring IFRS 9 compliance.
In this webinar, we discuss how institutions can overcome challenges to ensure that risk appetite can be monitored as well as key analytic metrics which can be leveraged for strategic decision-making.
Nihil Patel, Senior Director, provides insight on how to link stress testing with portfolio credit risk for a comprehensive risk management solution.
This research develops an approach to expand the Moody's Analytics Global Correlation Model (GCorr) to include macroeconomic variables. Within the context of this document, macroeconomic variables can include financial market variables, economic activity variables, and other risk factors. The expanded correlation model, known as GCorr Macro, lends itself to several functions that facilitate a cohesive and holistic risk management practice.
The Moody's Analytics Global Correlation Model (GCorr™) is a multi-factor model for asset correlations. This document provides an overview of the GCorr framework, methodology, data used for estimation, and validation. In addition, this document describes the components of GCorr related to individual asset classes and their integration. The asset classes explicitly included in GCorr are: public firms, private firms, small and medium-sized enterprises, sovereigns, U.S. commercial real estate, and U.S. retail.
The recent sovereign debt crisis in Europe, along with the global increase in sovereign debt issuance, has motivated credit portfolio managers to renew their focus on managing sovereign risk. In response, Moody's Analytics Quantitative Research Group has developed new techniques for modeling sovereign asset correlations.
Understanding how the components of asset correlation change through time will allow us to investigate how asset correlation dynamics behave during periods of economic stress. Although the time-varying correlation of equity returns has been extensively researched, we have found few studies on the dynamics of asset correlation over time. In this paper, we explore how both R-squared values and systematic factor correlations change through time. We show that R-squared values are more volatile than the systematic factor correlations. We also study the relationship between changes in R-squared and changes in factor variance, as well as the relationship between changes in factor correlation and changes in factor variance.
As new regulations require increased visibility of risk management processes, financial institutions often struggle to find strategic value in new investments beyond regulatory compliance.