Central bank

• 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.

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• Stock market development and familiarity (language and distance) are considered key determinants for home bias.
• The literature neglects however that investors often invest in foreign funds domiciled in financial centers.