Decomposing systemic risk: the roles of contagion and common exposures
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火曜日, 4月 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:
1In 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
?Lsuch 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.