Intelligence (journal)

Decomposing systemic risk: the roles of contagion and common exposures

Retrieved on: 
Tuesday, April 23, 2024
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Abstract

Key Points: 
    • Abstract
      We evaluate the effects of contagion and common exposure on banks? capital through
      a regression design inspired by the structural VAR literature and derived from the balance
      sheet identity.
    • Contagion can occur through direct exposures, fire sales, and market-based
      sentiment, while common exposures result from portfolio overlaps.
    • First, we document that contagion varies in time, with the highest levels
      around the Great Financial Crisis and lowest levels during the pandemic.
    • Our new framework complements
      traditional stress-tests focused on single institutions by providing a holistic view of systemic risk.
    • While existing literature presents various contagion narratives, empirical findings on
      distress propagation - a precursor to defaults - remain scarce.
    • We decompose systemic risk into three elements: contagion, common exposures, and idiosyncratic risk, all derived from banks? balance sheet identities.
    • The contagion factor encompasses both sentiment- and contractual-based elements, common exposures consider systemic
      aspects, while idiosyncratic risk encapsulates unique bank-specific risk sources.
    • Our empirical analysis of the Canadian banking system reveals the dynamic nature of contagion, with elevated levels observed during the Global Financial Crisis.
    • In conclusion, our model offers a comprehensive lens for policy intervention analysis and
      scenario evaluations on contagion and systemic risk in banking.
    • This
      notion of systemic risk implies two key components: first, systematic risks (e.g., risks related
      to common exposures) and second, contagion (i.e., an initially idiosyncratic problem becoming
      more widespread throughout the financial system) (see Caruana, 2010).
    • In this paper, we decompose systemic risk into three components: contagion, common exposures, and idiosyncratic risk.
    • First, we include contagion in three forms: sentiment-based contagion, contractual-based
      contagion, and price-mediated contagion.
    • In this context,
      portfolio overlaps create common exposures, implying that bigger overlaps make systematic
      shocks more systemic.
    • With the COVID-19 pandemic starting
      in 2020, contagion drops to all time lows, potentially related to strong fiscal and monetary
      supports.
    • That is, our
      structural model provides a framework for analyzing the impact of policy interventions and
      scenarios on different levels of contagion and systemic risk in the banking system.
    • This provides a complementary approach to
      seminal papers that took a structural approach to contagion, such as DebtRank Battiston et al.
    • More generally, the literature on networks and systemic risk started with Allen and Gale
      (2001) and Eisenberg and Noe (2001).
    • The matrix is structured as follows:
      1

      In our model, we do not distinguish between interbank liabilities and other types of liabilities.

    • In other words, we can and aim to estimate different degrees
      of contagion per asset class, i.e., potentially distinct parameters ?Ga .
    • For that, we build three major
      metrics to check: average contagion, average common exposure, and average idiosyncratic risk.
    • N i j

      et ,
      Further, we define the (N ?K) common exposure matrix as Commt = [A

      (20)

      et ]diag (?C
      ?L

      such that average common exposure reads,
      average common exposure =

      1 XX
      Commik,t .

    • N i j

      (22)

      20

      ? c ),

      The three metrics?average contagion, average common exposure, and average idiosyncratic risk?provide a comprehensive framework for understanding banking dynamics.

    • Figure 4 depicts the average level of risks per systemic risk channel: contagion risk, common exposure, and idiosyncratic risk.
    • Figure 4: Average levels of contagion (Equation (20)), common exposure (Equation (21)), and idiosyncratic risk
      (Equation (22)).
    • The market-based contagion is the contagion due to
      investors? sentiment, and the network is an estimate FEVD on volatility data.
    • For most of
      the sample, we find that contagion had a bigger impact on the variance than common exposures.