Garvit Arora | Searcharxiv

arXiv2024

Using CPI in Loss Given Default Forecasting Models for Commercial Real Estate Portfolio

Forecasting the loss given default (LGD) for defaulted Commercial Real Estate (CRE) loans poses a significant challenge due to the extended resolution and workout time associated with such defaults, particularly in CCAR and CECL framework where the utilization of post-default information, including macroeconomic variables (MEVs) such as unemployment (UER) and various rates, is restricted. The current environment of persistent inflation and resultant elevated rates further compounds the uncertainty surrounding predictive LGD models. In this paper, we leverage both internal and public data sources, including observations from the COVID-19 period, to present a list of evidence indicating that the growth rates of the Consumer Price, such as Year-over-Year (YoY) growth and logarithmic growth, are good leading indicators for various CRE related rates and indices. These include the Federal Funds Effective Rate and CRE market sales price indices in key locations such as Los Angeles, New York, and nationwide, encompassing both apartment and office segments. Furthermore, with CRE LGD data we demonstrate how incorporating CPI at the time of default can improve the accuracy of predicting CRE workout LGD. This is particularly helpful in addressing the common issue of early downturn underestimation encountered in CRE LGD models.

q-fin.RM↗

arXiv2024

An Exploration to the Correlation Structure and Clustering of Macroeconomic Variables

As a quantitative characterization of the complicated economy, Macroeconomic Variables (MEVs), including GDP, inflation, unemployment, income, spending, interest rate, etc., are playing a crucial role in banks' portfolio management and stress testing exercise. In recent years, especially during the COVID-19 period and the current high inflation environment, people are frequently talking about the changing "correlation structure" of MEVs. In this paper, we use a principal component based algorithm to perform unsupervised clustering on MEVs so we can quantify and better understand MEVs' correlation structure in any given period. We also demonstrate how this method can be used to visualize historical MEVs pattern changes between 2000 and 2022. Further, we use this method to compare different hypothetical and/or historical macroeconomic scenarios and present our key findings. One of these interesting observations is that, for a list of 132 transformations derived from 44 targeted MEVs that cover 5 different aspects of the U.S. economy (which takes as a subset the 10+ key MEVs published by FRB), compared to benign years where there are typically 20-25 clusters, during the great financial crisis (GFC), i.e., 2007-2010, they exhibited a more synchronized and less diversified pattern of movement, forming roughly 15 clusters. We also see this contrast in the hypothetical CCAR2023 FRB scenarios where the Severely Adverse scenario has 15 clusters and the Baseline scenario has 21 clusters. We provide our interpretation to this observation and hope this research can inspire and benefit researchers from different domains all over the world.

q-fin.RM↗