Financial institutions are seeking ways to gain a better understanding of their credit portfolios’ risk dynamics, allowing them to foresee and to prepare for potential increases in capital requirements resulting from economic shocks.
Aubrey Clayton, Associate Director, and Xuan Liang, Assistant Director of Portfolio Research at Moody’s Analytics, will discuss how to leverage a multi-period capital planning framework to determine the appropriate capital buffer level for a portfolio under various economic scenarios and how to fortify capital buffers through portfolio selection across periods of macroeconomic stress.
Webinar Highlights:Overview of Multi-Period Capital Analysis
Brute Force Approach
Proxy Function Approach
This paper studies how earnings volatility induced by credit risk can impact share price performance for financial institutions under CECL and IFRS 9, and quantifies the benefit of an active credit risk management practice.
An emerging business requirement for North American insurers is the ability to project forward stochastic reserve and capital requirements under various planning scenarios to a specific future date. In this paper we consider applying proxy functions to this task, using function fitting techniques described in our previous research paper Fitting Proxy Functions for Conditional Tail Expectation: Comparison of Methods.
A Composite Capital Measure Unifying Business Decision Rules in the Face of Regulatory Requirements Under New Accounting Standards
This paper introduces an approach that quantifies the additional capital buffer an institution requires, beyond the required regulatory minimum, to limit the likelihood of a capital breach.
This paper details alternative methods for fitting proxy functions to CTE, employing quantile regression in combination with OLS among other techniques. We compare methods according to quality of fit for an example portfolio of variable annuities.
IFRS 9 materially changes how institutions set aside loss allowance. With allowances flowing into earnings, the new rules can have dramatic effects on earnings volatility. In this paper, we propose general methodologies to measure and manage credit earnings volatility of a loan portfolio under IFRS 9. We walk through IFRS 9 rules and the different mechanisms that it interacts with which flow into earnings dynamics. We demonstrate that earnings will be impacted significantly by credit migration under IFRS 9. In addition, the increased sensitivity to migration will be further compounded by the impact of correlation and concentration. We propose a modeling framework that measures portfolio credit earnings volatility and discuss several metrics that can be used to better manage earnings risk.
In this paper, we show a practical application to forecasting capital requirements for real portfolios of participating whole life and annuity business, carried out in a joint research project between Moody's Analytics and New York Life Insurance Company.
The challenge of projecting dynamic hedge portfolios for blocks of Variable Annuities (VA) with complex guarantees has proven to be extremely computationally demanding but also essential for obtaining hedging credit in reserves or capital calculations. Our previous research has argued in favor of proxy function methods such as Least Squares Monte Carlo as alternatives to full nested stochastic calculations, and we have demonstrated the successful application of these methods for hedging in simple option examples including path-dependent options. This paper extends previous work by considering actual VA products with guarantees of the kind offered by insurers in North America and Europe.
This paper proposes and illustrates a multi-period capital planning framework that can be used to calculate a portfolio's capital requirement over time and to determine the appropriate capital buffer level under various economic scenarios. Such analysis can help financial institutions gain a better understanding of credit portfolios' risk dynamics, allowing them to foresee and to prepare for potential increases in capital requirements resulting from economic shocks.
In this paper we consider a framework for evaluating real-world probabilistic forecasts of economic variables, particularly nominal interest rates over quarterly time horizons.
In this paper we extend the analysis contained in a previous case study by considering a more complex example: a lookback option. We show that the methodology can produce a similar quality of fitting performance for the lookback option as in the vanilla option case. We also discuss the methodology adjustments necessary for the Greeks fitting strategy in order to accurately fit to forms of path-dependent, exotic options such as lookbacks.