Key Terms Explained
A stable financial system is capable of efficiently allocating resources, assessing and managing financial risks, maintaining employment levels close to the economy’s natural rate, and eliminating relative price movements of real or financial assets that will affect monetary stability or employment levels. A financial system is in a range of stability when it dissipates financial imbalances that arise endogenously or as a result of significant adverse and unforeseen events. In stability, the system will absorb the shocks primarily via self-corrective mechanisms, preventing the adverse events from having a disruptive effect on the real economy or on other financial systems. Financial stability is paramount for economic growth, as most transactions in the real economy are made through the financial system.
The true value of financial stability is best illustrated in its absence, in periods of financial instability. During these periods, banks are more reluctant to finance profitable projects, asset prices may deviate excessively from their intrinsic values, and the payment settlement schedule can diverge from the norm. Financial instability can severely shake confidence in the entire system. Excessive instability can lead to bank runs, hyperinflation, or a stock market crash.
Firm-Level Stability Measures
One clear measure of firm-level stability is Altman’s z‐score, which has been used extensively in empirical research because it is highly correlated with the probability of default. It contrasts buffers (capitalization and returns) with risk (volatility of returns). The model has proven to be accurate at predicting bankruptcies within two years.1 Recently, alternative models have been advanced for predicting financial stability, but Altman’s model remains the most widely used. 2, 3
Another measure of individual stability has been advanced by
Another model used to measure institution-level stability is the Merton model. It is routinely used to ascertain a firm’s ability to meet its financial obligations and gauge the overall possibility of default. The Merton model (also called the asset value model) treats an institution’s equity as a call option on its held assets, taking into account the volatility of those assets. Put-call parity is used to price the value of the “put,” which is represented by the firm's credit risk. So, the model measures the value of the firm’s assets (weighting for volatility) at the time that the debtholders will “exercise their put option” by expecting repayment. The model defines default as when the value of a firm’s liabilities exceeds that of its assets (in different iterations of the model, the asset/liability level required to reach default is set at a different threshold). The Merton model can calculate the probability of credit default for the firm.
Merton’s model has been modified in subsequent research to capture a wider array of financial activity using credit default swap data. For example, it is part of the KMV model that Moody’s uses to both calculate the probability of credit default and as part of their credit risk management system.(vii) The Distance to Default (DD) is another market-based measure of corporate default risk based on Merton’s model. It measures both solvency risk and liquidity risk at the firm level.
Systemic Stability Measures
There is, as of yet, no singular, standard model for assessing financial system stability and for examining policies.
To measure systemic stability, a number of studies attempt to aggregate firm-level stability measures (z-score and distance to default) into a system-wide evaluation of stability, either by simply averaging or by weighting each measure by the institution’s relative size. However, these aggregate measures fail to take into account the interconnectedness of financial institutions; that is, that one institution’s failure can be contagious.
The First-to-Default probability, or the probability of observing one default among a number of institutions, has been proposed as a measure of systemic risk for large financial institutions. It uses risk-neutral default probabilities from credit default swap spreads. The probability, unlike distance-to-default measures, recognizes that defaults among a number of institutions can be connected. However, studies focusing on probabilities of default tend to overlook the fact that a large institution failing causes bigger ripples than a small one.
Another assessment of financial system stability is Systemic Expected Shortfall (SES), which measures each institution’s individual contribution to systemic risk. SES takes the individual taking leverage and risk-taking into account and measures the externalities from the banking sector to the real economy when these institutions fail. The model is especially good at identifying which institutions are systemically relevant and would have the largest effects, if they fail, on the wider economy. One drawback of the SES method is that it is difficult to determine when the systemically-important institutions are likely to fail. 7
In further research, the retrospective SES measure was extended to be somewhat predictive. The predictive measure is SRISK. SRISK evaluates the expected capital shortfall for a firm if there is another crisis. To calculate this predictive systemic risk measure, one must first find the Long-Run Marginal Expected Shortfal (LRMES), which measures the relation between a firm’s equity returns and the returns of the broader market (estimated using asymmetric volatility, correlation, and copula). The model estimates the drop in equity value of the firm if the aggregate market falls more than 40% in a six-month window to determine how much capital is needed during the simulated crisis in order to achieve an 8% capital to asset value ratio.. SRISK% measures the firm’s percentage of total financial sector capital shortfall. A high SRISK% simultaneously indicates the biggest losers and contributors to the hypothetical crisis. One of the assumptions of the SES indicator is that a firm is “systemically risky” if it is especially likely to face a capital shortage when the financial sector is weak overall. 8
Another gauge of financial stability is the distribution of systemic loss, which attempts to fill some of the gaps of the previously-discussed measures. This combines three key elements: each individual institution’s probability of default, the size of loss given default, and the “contagious” nature of defaults across the institutions due to their interconnectedness.9