2,530 research outputs found
Stability analysis of financial contagion due to overlapping portfolios
Common asset holdings are widely believed to have been the primary vector of
contagion in the recent financial crisis. We develop a network approach to the
amplification of financial contagion due to the combination of overlapping
portfolios and leverage, and we show how it can be understood in terms of a
generalized branching process. By studying a stylized model we estimate the
circumstances under which systemic instabilities are likely to occur as a
function of parameters such as leverage, market crowding, diversification, and
market impact. Although diversification may be good for individual
institutions, it can create dangerous systemic effects, and as a result
financial contagion gets worse with too much diversification. Under our model
there is a critical threshold for leverage; below it financial networks are
always stable, and above it the unstable region grows as leverage increases.
The financial system exhibits "robust yet fragile" behavior, with regions of
the parameter space where contagion is rare but catastrophic whenever it
occurs. Our model and methods of analysis can be calibrated to real data and
provide simple yet powerful tools for macroprudential stress testing.Comment: 25 pages, 8 figure
Statistically validated network of portfolio overlaps and systemic risk
Common asset holding by financial institutions, namely portfolio overlap, is
nowadays regarded as an important channel for financial contagion with the
potential to trigger fire sales and thus severe losses at the systemic level.
In this paper we propose a method to assess the statistical significance of the
overlap between pairs of heterogeneously diversified portfolios, which then
allows us to build a validated network of financial institutions where links
indicate potential contagion channels due to realized portfolio overlaps. The
method is implemented on a historical database of institutional holdings
ranging from 1999 to the end of 2013, but can be in general applied to any
bipartite network where the presence of similar sets of neighbors is of
interest. We find that the proportion of validated network links (i.e., of
statistically significant overlaps) increased steadily before the 2007-2008
global financial crisis and reached a maximum when the crisis occurred. We
argue that the nature of this measure implies that systemic risk from fire
sales liquidation was maximal at that time. After a sharp drop in 2008,
systemic risk resumed its growth in 2009, with a notable acceleration in 2013,
reaching levels not seen since 2007. We finally show that market trends tend to
be amplified in the portfolios identified by the algorithm, such that it is
possible to have an informative signal about financial institutions that are
about to suffer (enjoy) the most significant losses (gains)
DebtRank: A microscopic foundation for shock propagation
The DebtRank algorithm has been increasingly investigated as a method to
estimate the impact of shocks in financial networks, as it overcomes the
limitations of the traditional default-cascade approaches. Here we formulate a
dynamical "microscopic" theory of instability for financial networks by
iterating balance sheet identities of individual banks and by assuming a simple
rule for the transfer of shocks from borrowers to lenders. By doing so, we
generalise the DebtRank formulation, both providing an interpretation of the
effective dynamics in terms of basic accounting principles and preventing the
underestimation of losses on certain network topologies. Depending on the
structure of the interbank leverage matrix the dynamics is either stable, in
which case the asymptotic state can be computed analytically, or unstable,
meaning that at least one bank will default. We apply this framework to a
dataset of the top listed European banks in the period 2008 - 2013. We find
that network effects can generate an amplification of exogenous shocks of a
factor ranging between three (in normal periods) and six (during the crisis)
when we stress the system with a 0.5% shock on external (i.e. non-interbank)
assets for all banks.Comment: 10 pages, 2 figure
What is the Minimal Systemic Risk in Financial Exposure Networks?
Management of systemic risk in financial markets is traditionally associated
with setting (higher) capital requirements for market participants. There are
indications that while equity ratios have been increased massively since the
financial crisis, systemic risk levels might not have lowered, but even
increased. It has been shown that systemic risk is to a large extent related to
the underlying network topology of financial exposures. A natural question
arising is how much systemic risk can be eliminated by optimally rearranging
these networks and without increasing capital requirements. Overlapping
portfolios with minimized systemic risk which provide the same market
functionality as empirical ones have been studied by [pichler2018]. Here we
propose a similar method for direct exposure networks, and apply it to
cross-sectional interbank loan networks, consisting of 10 quarterly
observations of the Austrian interbank market. We show that the suggested
framework rearranges the network topology, such that systemic risk is reduced
by a factor of approximately 3.5, and leaves the relevant economic features of
the optimized network and its agents unchanged. The presented optimization
procedure is not intended to actually re-configure interbank markets, but to
demonstrate the huge potential for systemic risk management through rearranging
exposure networks, in contrast to increasing capital requirements that were
shown to have only marginal effects on systemic risk [poledna2017]. Ways to
actually incentivize a self-organized formation toward optimal network
configurations were introduced in [thurner2013] and [poledna2016]. For
regulatory policies concerning financial market stability the knowledge of
minimal systemic risk for a given economic environment can serve as a benchmark
for monitoring actual systemic risk in markets.Comment: 25 page
Quantification of systemic risk from overlapping portfolios in the financial system
Financial markets create endogenous systemic risk, the risk that a substantial fraction of the system ceases to function and collapses. Systemic risk can propagate through different mechanisms and channels of contagion. One important form of financial contagion arises from indirect interconnections between financial institutions mediated by financial markets. This indirect interconnection occurs when financial institutions invest in common assets and is referred to as overlapping portfolios. In this work we quantify systemic risk from indirect interconnections between financial institutions. Complete information of security holdings of major Mexican financial intermediaries and the ability to uniquely identify securities in their portfolios, allows us to represent the Mexican financial system as a bipartite network of securities and financial institutions. This makes it possible to quantify systemic risk arising from overlapping portfolios. We show that focusing only on direct interbank exposures underestimates total systemic risk levels by up to 50% under the assumptions of the model. By representing the financial system as a multi-layer network of direct interbank exposures (default contagion) and indirect external exposures (overlapping portfolios) we estimate the mutual influence of different channels of contagion. The method presented here is the first quantification of systemic risk on national scales that includes overlapping portfolios
Quantification of systemic risk from overlapping portfolios in the financial system
Financial markets create endogenous systemic risk, the risk that a substantial fraction of the system
ceases to function and collapses. Systemic risk can propagate through different mechanisms and channels
of contagion. One important form of financial contagion arises from indirect interconnections between
financial institutions mediated by financial markets. This indirect interconnection occurs when financial
institutions invest in common assets and is referred to as overlapping portfolios. In this work we quantify systemic risk from indirect interconnections between financial institutions. Complete information of
security holdings of major Mexican financial intermediaries and the ability to uniquely identify securities
in their portfolios, allows us to represent the Mexican financial system as a bipartite network of securities
and financial institutions. This makes it possible to quantify systemic risk arising from overlapping portfolios. We show that focusing only on direct interbank exposures underestimates total systemic risk levels
by up to 50% under the assumptions of the model. By representing the financial system as a multi-layer
network of direct interbank exposures (default contagion) and indirect external exposures (overlapping
portfolios) we estimate the mutual influence of different channels of contagion. The method presented
here is the first quantification of systemic risk on national scales that includes overlapping portfolio
Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations
We review recent progress in modeling credit risk for correlated assets. We
start from the Merton model which default events and losses are derived from
the asset values at maturity. To estimate the time development of the asset
values, the stock prices are used whose correlations have a strong impact on
the loss distribution, particularly on its tails. These correlations are
non-stationary which also influences the tails. We account for the asset
fluctuations by averaging over an ensemble of random matrices that models the
truly existing set of measured correlation matrices. As a most welcome side
effect, this approach drastically reduces the parameter dependence of the loss
distribution, allowing us to obtain very explicit results which show
quantitatively that the heavy tails prevail over diversification benefits even
for small correlations. We calibrate our random matrix model with market data
and show how it is capable of grasping different market situations.
Furthermore, we present numerical simulations for concurrent portfolio risks,
i.e., for the joint probability densities of losses for two portfolios. For the
convenience of the reader, we give an introduction to the Wishart random matrix
model.Comment: Review of a new random matrix approach to credit ris
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