A Bayesian methodology for systemic risk assessment in financial networks

We develop a Bayesian methodology for systemic risk assessment in financial networks such as the interbank market. Nodes represent participants in the network and weighted directed edges represent liabilities. Often, for every participant, only the total liabilities and total assets within this network are observable. However, systemic risk assessment needs the individual liabilities. We propose a model for the individual liabilities, which, following a Bayesian approach, we then condition on the observed total liabilities and assets and, potentially, on certain observed individual liabilities. We construct a Gibbs sampler to generate samples from this conditional distribution. These samples can be used in stress testing, giving probabilities for the outcomes of interest. As one application we derive default probabilities of individual banks and discuss their sensitivity with respect to prior information included to model the network. An R-package implementing the methodology is provided

Pricing q-forward contracts: an evaluation of estimation window and pricing method under different mortality models

The aim of this paper is to study the impact of various sources of uncertainty on the pricing of a special longevity–based instrument: a q-forward contract. At the expiry of a q-forward contract, the realized mortality rate for a given population is exchanged in return for a fixed (mortality) rate that is agreed at the initiation of the contract. Pricing a q-forward involves determining this fixed rate. In our study, we disentangle three main sources of uncertainty and consider their impact on pricing: model choice for the underlying mortality rate, time-window used for estimation and the pricing method itself

Compound poisson models for weighted networks with applications in finance

We develop a modelling framework for estimating and predicting weighted network data. The edge weights in weighted networks often arise from aggregating some individual relationships be- tween the nodes. Motivated by this, we introduce a modelling framework for weighted networks based on the compound Poisson distribution. To allow for heterogeneity between the nodes, we use a regression approach for the model parameters. We test the new modelling framework on two types of financial networks: a network of financial institutions in which the edge weights represent exposures from trading Credit Default Swaps and a network of countries in which the edge weights represent cross-border lending. The compound Poisson Gamma distributions with regression fit the data well in both situations. We illustrate how this modelling framework can be used for predicting unobserved edges and their weights in an only partially observed network. This is for example relevant for assessing systemic risk in financial networks

Optimal diversification in the presence of parameter uncertainty for a risk averse investor

We consider an investor who faces parameter uncertainty in a continuoustime financial market. We model the investor's preference by a power utility function leading to constant relative risk aversion. We show that the loss in expected utility is large when using a simple plug-in strategy for unknown parameters. We also provide theoretical results that show the trade-off between holding a well-diversified portfolio and a portfolio that is robust against estimation errors. To reduce the effect of estimation, we constrain the weights of the risky assets with an L1-norm leading to a sparse portfolio. We provide analytical results that show how the sparsity of the constrained portfolio depends on the coefficient of relative risk aversion. Based on a simulation study, we demonstrate the existence and the uniqueness of an optimal bound on the L1-norm for each level of relative risk aversion

Optimal diversification in the presence of parameter uncertainty for a risk averse investor

We consider an investor who faces parameter uncertainty in a continuoustime financial market. We model the investor's preference by a power utility function leading to constant relative risk aversion. We show that the loss in expected utility is large when using a simple plug-in strategy for unknown parameters. We also provide theoretical results that show the trade-off between holding a well-diversified portfolio and a portfolio that is robust against estimation errors. To reduce the effect of estimation, we constrain the weights of the risky assets with an L1-norm leading to a sparse portfolio. We provide analytical results that show how the sparsity of the constrained portfolio depends on the coefficient of relative risk aversion. Based on a simulation study, we demonstrate the existence and the uniqueness of an optimal bound on the L1-norm for each level of relative risk aversion

Interbank clearing in financial networks with multiple maturities

We consider the problem of systemic risk assessment in interbank networks in which interbank liabilities can have multiple maturities. In particular, we allow for both short-term and long-term interbank liabilities. We develop a clearing mechanism for the interbank liabilities to deal with the default of one or more market participants. Our approach generalizes the clearing approach for the single maturity setting proposed by Eisenberg and Noe [Management Sci., 47 (2001), pp. 236-249]. Our clearing mechanism focuses on the vector of each bank's liquid assets at each maturity date and develops a fixed-point formulation of this vector for a given set of defaulted banks. Our formulation is consistent with the main stylized principles of insolvency law. We show that in the context of multiple maturities, specifying a set of defaulted banks is challenging. We propose two approaches to overcome this challenge: First, we propose an algorithmic approach for defining the default set and show that this approach leads to a well-defined liquid asset vector for all financial networks with multiple maturities. Second, we propose a simpler functional approach which leads to a functional liquid asset vector which need not exist but under a regularity condition does exist and coincides with the algorithmic liquid asset vector. Our analysis permits construction of simple dynamic models and furthermore demonstrates that systemic risk can be underestimated by single maturity models

Adjustable network reconstruction with applications to CDS exposures

This paper is concerned with reconstructing weighted directed networks from the total in- and out-weight of each node. This problem arises for example in the analysis of systemic risk of partially observed financial networks. Typically a wide range of networks is consistent with this partial information. We develop an empirical Bayesian methodology that can be adjusted such that the resulting networks are consistent with the observations and satisfy certain desired global topological properties such as a given mean density, extending the approach by Gandy and Veraart (2017). Furthermore we propose a new fitness-based model within this framework. We provide a case study based on a data set consisting of 89 fully observed financial networks of credit default swap exposures. We reconstruct those networks based on only partial information using the newly proposed as well as existing methods. To assess the quality of the reconstruction, we use a wide range of criteria, including measures on how well the degree distribution can be captured and higher order measures of systemic risk. We find that the empirical Bayesian approach performs best

When does portfolio compression reduce systemic risk?

We analyze the consequences of portfolio compression for systemic risk. Portfolio compression is a post-trade netting mechanism that reduces gross positions while keeping net positions unchanged and it is part of the financial legislation in the United States (Dodd–Frank Act) and in Europe (European Market Infrastructure Regulation). We derive necessary structural conditions for portfolio compression to be harmful and discuss policy implications. We show that any potential harmfulness of portfolio compression arises from contagion effects. We show how portfolio compression affects systemic risk depends on the resilience of nodes taking part in compression, on the proportion of debt that they can repay, and on the recovery rates in case of default. In particular, the potential danger of portfolio compression comes from defaults of firms that conduct portfolio compression. If no defaults occur among the firms that engage in compression, then portfolio compression always reduces systemic risk

Optimal market making in the foreign exchange market

This paper is concerned with optimal market making in the foreign exchange market. The market maker's holdings in the different currencies are modelled as stochastic processes that are influenced by both the stochastic exchange rates and the stochastic customer buy and sell orders. The market maker can control their own bid and ask price quotes and, additionally, can buy and sell at other market participants' quotes. The resulting stochastic control problem consists of a controlled diffusion problem for the optimal quotes and a singular control problem for optimal trades at other market participants' quotes. A Markov chain approximation is used to derive optimal strategies