Managing Director, Economic Research
Tony oversees the Moody’s Analytics credit analysis consulting projects for global lending institutions. An expert applied econometrician, he has helped develop approaches to stress testing and loss forecasting in retail, C&I, and CRE portfolios and recently introduced a methodology for stress testing a bank’s deposit book.
Tony was formerly the lead Asia-Pacific economist for Moody’s Analytics. Prior to that, he held academic positions at the University of Adelaide, the University of New South Wales, and Vanderbilt University. He received his PhD in Econometrics from Monash University in Melbourne, Australia.
In this article, we consider some possible long-term ramifications of ride-sharing for the broader auto indust
Increases in auto lease volumes are nothing new, yet the industry is rife with fear that used car prices are about to collapse. In this talk, we will explore the dynamics behind the trends and the speculation. The abundance of vehicles in the US that are older than 10 years will soon need to be replaced, and together with continuing demand from ex-lessees, this demand will ensure that prices remain supported under baseline macroeconomic conditions.
Increases in auto lease volumes are nothing new, yet the industry is rife with fear that used car prices are about to collapse. In this webinar, we explore the dynamics behind the trends and the speculation. The abundance of vehicles in the US that are older than 10 years will soon need to be replaced, and together with continuing demand from ex-lessees, this demand will ensure that prices remain supported under baseline macroeconomic conditions.
To effectively manage risk in your auto portfolios, you need to account for future economic conditions. Relying on models that do not fully account for cyclical economic factors and include subjective overlay, may produce inaccurate, inconsistent or biased estimates of residual values.
Granular risk rating models allow creditors to understand the credit risk of individual loans in a portfolio, facilitating underwriting and monitoring activities. In this webinar we will outline the value of granular risk rating models for CECL.
Improved Deposit Modeling: Using Moody's Analytics Forecasts of Bank Financial Statements to Augment Internal Data
We demonstrate how our service can be used to produce more realistic forecasts of income and balance sheet statements.
In this article, I take a theoretical look at negative interest rates as a means to stimulate the economy. I identify key factors that may influence the volume of deposits held in the economy. I then empirically describe the unique situation of negative interest rates.
With powerful computers and statistical packages, modelers can now run an enormous number of tests effortlessly. But should they? This article discusses how bank risk modelers should approach statistical testing when faced with tiny data sets.
This article discusses the role of third-party data and analytics in the stress testing process. Beyond the simple argument that more eyes are better, we outline why some stress testing activities should definitely be conducted by third parties.
In this article, we introduce a new risk management tool focused on network connectivity between financial institutions.
In this paper we discuss our approach to forecasting residual car values that accounts for cyclical economic factors affecting the automotive industry, under normal and stressed scenarios.
The market for new cars is growing strongly and lessors need forecasts and associated stress scenarios of future vehicle value to set the initial terms, to monitor the performance of their book and to stress-test cash flows. This presentation offers insight and tools to help lessors in this pursuit.
This article looks back at the Asian financial crisis of 1997-1998 and applies new methods of measuring systemic risk and pinpointing weaknesses, which can be used by today’s financial institutions and regulators.
Multicollinearity, the phenomenon in which the regressors of a model are correlated with each other, apparently causes a lot of confusion among practitioners and users of stress testing models. This article seeks to dispel this confusion.
This article addresses how banks should look to sources of high-quality, industry-level data to ensure that their PPNR modeling is not only reliable and effective, but also better informs their risk management decisions.
In this article, we consider the increasing prevalence of long term loans and use the AutoCycle™ wholesale price forecasts to uncover equity held by the borrower under different economic scenarios.
In this article, banks can significantly improve the effectiveness of their stress-testing exercises by incorporating systemic risk measures.
This article explores the interaction between a bank’s various models and how they may be built into a comprehensive stress testing framework, contributing to the overall performance of a bank.
The banking industry needs a regulatory framework that is carefully designed to maximize economic outcomes, both in terms of stability and growth, rather than one dictated by past banking sector excesses.
In this paper we describe the modeling methodology behind Moody's Analytics Stressed EDF measures for Western Europe. Stressed EDF measures are one-year, default probabilities conditioned on holistic economic scenarios developed in a large-scale,structural macroeconometric model framework.
In this paper we describe the modeling methodology behind Moody's Analytics Stressed EDF measures. Stressed EDF measures are one-year, default probabilities conditioned on holistic economic scenarios developed in a large-scale, structural macroeconometric model framework. This approach has several advantages over other methods, especially in the context of stress testing. Stress tests or scenario analyses based on macroeconomic drivers lend themselves to highly intuitive interpretation accessible to wide audiences – investors, economists, regulators, the general public, to name a few.
This whitepaper goes in-depth into the Moody's CreditCycle approach to loan loss modeling.
Banks face the difficult task of building hundreds of forecasting models that disentangle macroeconomic effects from bank-specific decisions. We propose an approach based on consistently reported industry data that simplifies the modeler’s task and at the same time increases forecast accuracy.