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Systemic Expected Shortfall (SES)

API

function
systemic_expected_shortfall(mes_training_sample, lvg_training_sample, ses_training_sample, mes_firm, lvg_firm)

Systemic Expected Shortfall (SES)

A measure of a financial institution's contribution to a systemic crisis by Acharya, Pedersen, Philippon, and Richardson (2017), which equals to the expected amount a bank is undercapitalized in a future systemic event in which the overall financial system is undercapitalized.

SES increases in the bank’s expected losses during a crisis, and is related to the bank's marginal expected shortfall (MES), i.e., its losses in the tail of the aggregate sector’s loss distribution, and leverage.

SES is a theoretical construct and the authors use the following 3 measures to proxy it:

  1. The outcome of stress tests performed by regulators. The SES metric of a firm here is defined as the recommended capital that it was required to raise as a result of the stress test in February 2009.
  2. The decline in equity valuations of large financial firms during the crisis, as measured by their cumulative equity return from July 2007 to December 2008.
  3. The widening of the credit default swap spreads of large financial firms as measured by their cumulative CDS spread increases from July 2007 to December 2008.

Given these proxies, the authors seek to develop leading indicators which “predict” an institution’s SES, including marginal expected shortfall (MES) and leverage (LVG).

Note

Since SES is a theoretical construct, this function estimates the fitted SES following Bisias, Flood, Lo, and Valavanis (2012).

Specifically, the following model is estimated:

\textit{realized SES}_{i,\textit{crisis}} = a + b MES_{i,\textit{pre-crisis}} + c LVG_{i,\textit{pre-crisis}} + \varepsilon_{i}

where \textit{realized SES}_{i,\textit{crisis}} is the stock return during the crisis, and LVG_{i,\textit{pre-crisis}} is defined as (\text{book assets - book equity + market equity}) / \text{market equity}.

The fitted SES is computed as

\textit{fitted SES} = \frac{b}{b+c} MES + \frac{c}{b+c} LVG
Model in Acharya, Pedersen, Philippon, and Richardson (2017)

In Acharya, Pedersen, Philippon, and Richardson (2017), fitted SES is abtained via estimating the model:

\textit{realized SES}_{i,\textit{crisis}} = a + b MES_{i,\textit{pre-crisis}} + c LVG_{i,\textit{pre-crisis}} + \text{industriy dummies} + \varepsilon_{i}

and calculating the fitted value of \textit{realized SES}_{i} directly, where the industry dummies inlcude indicators for whether the bank is a broker-dealer, an insurance company and other.

See Model 6 in Table 4 (p.23) and Appendix C.

Parameters
  • mes_training_sample (np.ndarray) (n_firms,) array of firm ex ante MES.
  • lvg_training_sample (np.ndarray) (n_firms,) array of firm ex ante LVG (say, on the last day of the period of training data)
  • ses_training_sample (np.ndarray) (n_firms,) array of firm ex post cumulative return for date range after lvg_training_sample.
  • mes_firm (float) The current firm MES used to calculate the firm (fitted) SES value.
  • lvg_firm (float) The current firm leverage used to calculate the firm (fitted) SES value.
Returns (float)

The systemic risk that firm i poses to the system at a future time.

Examples
>>> from frds.measures import systemic_expected_shortfall
>>> import numpy as np
>>> mes_training_sample = np.array([-0.023, -0.07, 0.01])
>>> lvg_training_sample = np.array([1.8, 1.5, 2.2])
>>> ses_training_sample = np.array([0.3, 0.4, -0.2])
>>> mes_firm = 0.04
>>> lvg_firm = 1.7
>>> systemic_expected_shortfall(mes_training_sample, lvg_training_sample, ses_training_sample, mes_firm, lvg_firm)
-0.33340757238306845
References

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Last update: July 26, 2021
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