Abstract
Debt is a crucial source of corporate financing, and monitoring corporate liabilities is essential for evaluating a firm’s financial health. While it is common to rely on the book value of liabilities, there are several concerns with balance sheet data, including: (1) low reporting frequency (typically quarterly); (2) significant delays in disclosure; (3) backward-looking nature; and (4) potential for managerial discretion. These limitations are especially problematic when real-time updates on a firm’s financial health are needed, particularly in times of crisis. The divergence between book and market values of liabilities is a critical issue for all firms, both financial and non-financial. However, it is even more significant for financial institutions due to their unique debt structures and their systemic importance in developed economies. In this work we slightly modify the model of Nagel and Purnanandam (2020) [Banks’ risk dynamics and distance to default, Review of Financial Studies] and solve it semi-analytically. Using maximum likelihood in conjunction with a nonlinear Kalman filter allows us to provide real-time, high-frequency (daily) market-based estimates of debt and leverage for European and US financial institutions at the firm level These real-time estimates can be tested to determine whether they offer improvements over commonly used financial distress indicators, such as credit ratings and the KMV distance-to-default measure, which are updated far less frequently and may lag in responding to market shocks. Additionally, since the estimates are generated at the firm level, they can be aggregated to offer a broader market-wide measure of financial stability for the banking sectors in Europe and the US.
This is a joint work with Raffaele Corvino (NEOMA Business School, Paris) and Berardino Palazzo (Board of Governors of the Federal Reserve System, Washington DC).
Organized by: Umberto Cherubini