Network formation and systemic risk

Decomposing the Smile: Systematic Credit Risk in Mortgage Portfolios

Systematic risk has been a focus for stress testing and risk capital assessment. Under the Vasicek asymptotic single risk factor model framework, entity default risk for a risk homogeneous portfolio divides into two parts: systematic and entity specific. While entity specific risk can be modelled by a probit or logistic model using a relatively short period of portfolio historical data, modeling of systematic risk is more challenging. In practice, most default risk models do not fully or dynamically capture systematic risk. In this paper, we propose an approach to modeling systematic and entity specific risks by parts and then aggregating together analytically. Systematic risk is quantified and modelled by a multifactor Vasicek model with a latent residual, a factor accounting for default contagion and feedback effects. The asymptotic maximum likelihood approach for parameter estimation for this model is equivalent to least squares linear regression. Conditional entity PDs for scenario tests and through-the-cycle entity PD all have analytical solutions. For validation, we model the point-in-time entity PD for a commercial portfolio, and stress the portfolio default risk by shocking the systematic risk factors. Rating migration and portfolio loss are assessed.

We present a model of strategic network formation with absolute friction and heterogeneous agents. The individual payoffs from a given network are determined by the difference of an agent specific utility function that depends on the number of her direct links and the sum of her link-costs. These link-costs decompose in a symmetric function that represents the social distance between the two agents and an agent specific function representing the partner’s quality. From a theoretical point of view, we analyze strong pairwise stable networks (Belleflamme and Bloch, Int Econ Rev 45(2):387–411, 2004) and show that our model always has a unique strong pairwise stable network which is also strongly stable (Dutta and Mutuswami, J Econ Theory, 76:322–344, 1997). From a practical point of view, we provide an algorithm that reproduces this stable network from information on the individual payoff structure. We illustrate the use of this algorithm by applying it to an informal risk sharing network data set from the village of Nyakatoke in rural Tanzania.

Finance is becoming more important in the national economy. Maintaining financial stability is critical not only for the financial industry’s prosperity and development, but also for a country’s political, economic, and social development. This paper will look at the mechanisms that cause systemic risk to develop and evolve, as well as how to measure systemic financial risk in multiple dimensions. To begin, create a system for evaluating financial risk in a systematic manner. Second, using the AHP and CRITIC methods to determine various indicators and market weights, create a systemic financial risk evaluation model based on the system dynamics model, and calculate the system from 2010 to 2019 comprehensive financial risk index. Finally, simulation research is conducted using the system dynamics model of systemic financial risk, and the simulation results are analyzed. The findings show that China’s financial risk has been gradually increasing since 2016, with relatively small fluctuations in risk state.

R&D Collaboration Networks in Mixed Oligopoly

This paper proposes an empirical model of network formation, combining strategic and random networks features. Payoffs depend on direct links, but also link externalities. Players meet sequentially at random, myopically updating their links. Under mild assumptions, the network formation process is a potential game and converges to an exponential random graph model (ERGM), generating directed dense networks. I provide new identification results for ERGMs in large networks: if link externalities are nonnegative, the ERGM is asymptotically indistinguishable from an Erdős–Renyi model with independent links. We can identify the parameters only when at least one of the externalities is negative and sufficiently large. However, the standard estimation methods for ERGMs can have exponentially slow convergence, even when the model has asymptotically independent links. I thus estimate parameters using a Bayesian MCMC method. When the parameters are identifiable, I show evidence that the estimation algorithm converges in almost quadratic time.

Systemic Risk in the Insurance Sector- General Issues and a First Assessment of Large Commercial (Re-)Insurers in Bermuda

Abstract. The objective of this article is theoretical and methodological justifying of determining algorithm of the cost of debt capital for enterprises functioning in emerging markets (EM). The methods of research: analysis and synthesis, system analysis, comparative analysis, empirical and statistical methods, factor analysis. Results. In this article key determinants of interest rates on debt capital for enterprises and their impact on the procedure of discount rate calculation are determined. The issue of the cost of debt calculation of enterprises in condition of absence of complete information concerning systematic and non-systematic crediting risks is studied. Differences between interest rate on the loan fixed in credit agreement and expected by creditors the cost of debt are identified. It is determined that the key factor impacting the deviation level of market value of debt capital from the nominal, and respectively, deviation of the cost of debt from the cost of capital is probability of default (PD). At the minimum values of PD, the contract interest rate corresponds to the rate of cost of debt and it is advisable to use it for discount rate calculation. Critical analysis of alternative methodological approaches of the cost of debt calculation is made. Ways of integrating of market information concerning credit default swaps into the process of expected cost of debt calculation are justified. Factors of shadowing of rates of the cost of debt and ways of reducing of shadow transactions’ level in the credit market are identified. Conclusions. At high PD values, expected by market premium for default risk may exceed the contract interest rate, which necessitates constant monitoring of credit risks and appropriate adaptation of interest rates. In the paper the algorithm of such adaptation are proposed. It is shown that in the case of non-use of interest rates adjustment taking into account changes in PD, CDS and LGD, premium for creditors’ systematic risk can differ significantly from market values of similar enterprises (peer-group), and estimated value of the cost of debt can acquire negative values. Contract (promised) interest rate should be set in such way that the premium for systematic risk of providing debt capital will be at the level of similar companies and does not change significantly as a result of probability of default changes. If in practice the opposite situation occurs, it is the evidence of contract interest rate shadowing, absence of effective system of assessment and management of credit risks. For solving the problem of interest rate transparency and filling of information gaps concerning PD borrowers in EM countries, should intensify CDS market. Keywords: debt capital, default probability, non-performing loans, credit default swap, credit spread, debt capital premium, shadow economy. JEL Classification E47 Formulas: 16; fig.: 0; tabl.: 3; bibl.: 15.

We make four main contributions to the theory of network formation. (1) The problem of network formation with farsighted agents can be formulated as an abstract network formation game. (2) In any farsighted network formation game the feasible set of networks contains a unique, finite, disjoint collection of nonempty subsets having the property that each subset forms a strategic basin of attraction. These basins of attraction contain all the networks that are likely to emerge and persist if individuals behave farsightedly in playing the network formation game. (3) A von Neumann Morgenstern stable set of the farsighted network formation game is constructed by selecting one network from each basin of attraction. We refer to any such von Neumann-Morgebstern stable set as farsighted basis. (4) The core of the farsighted network formation games is constructed by selecting one network from each basin of attraction containing a single network. We call this notion of the core, the farsighted core. We conclude that the farsighted core is nonempty if and only if there exists one farsighted basin of attraction containing a single network. To relate our three equilibrium and stability notions (basins of attraction, farsighted basis and farsighted core) to recent work by Jackson and Wolinsky (1996), we define a notion of pairwise stability similar to the Jackson-Wolinsky notion and we show that a farsighted core is contained in the set of pairwise stable networks. Finally, we introduce, via an example, competitative contracting networks and highlight how the analysis of these networks requires the new features of our network formation model.

Chapter 6 - An empirical model for strategic network formation

We develop and analyze a tractable empirical model for strategic network formation that can be estimated with data from a single network at a single point in time. We model the network formation as a sequential process where in each period a single randomly selected pair of agents has the opportunity to form a link. Conditional on such an opportunity, a link will be formed if both agents view the link as beneficial to them. They base their decision on their own characateristics, the characteristics of the potential partner, and on features of the current state of the network, such as whether the two potential partners already have friends in common. A key assumption is that agents do not take into account possible future changes to the network. This assumption avoids complications with the presence of multiple equilibria, and also greatly simplifies the computational burden of anlyzing these models. We use Bayesian markov-chainmonte-carlo methods to obtain draws from the posterior distribution of interest. We apply our methods to a social network of 669 high school students, with, on average, 4.6 friends. We then use the model to evaluate the effect of an alternative assignment to classes on the topology of the network.

This dissertation consists of four essays, focusing on the relationship between financial risks, financial market uncertainty and macroeconomic conditions. The first essay estimates the effects of anticipated and unanticipated monetary policy changes on jump variation by employing high frequency non-parametric jump detection methods. We use an event study approach and a structural VAR framework in examining stock price jump variation for the aggregate economy, the financial, health, energy and telecommunication-information technology sectors (Tel-Info). We find that anticipated changes in the Fed funds have no significant effect on jumps. In contrast, jump variation in the price of financial market data increases with monetary policy surprises. We document evidence of asymmetries in the response of jumps to monetary policy changes. Monetary policy surprises and positive changes in the Fed target rate induce increment in jumps. Similar results exist in the sector analysis. In addition, this study uncovers no evidence of endogenous response between jumps and monetary policy surprises. The second essay estimates the response of uncertainty/risk aversion to monetary policy actions in both the financial sector and the aggregate economy using a Structural Vector Autoregressive (SVAR) model. When compared with other sectors, our constructs reveal that financial risk aversion/uncertainty has greater correlation with the aggregate risk aversion and uncertainty. Our analysis reveals that financial risk aversion and uncertainty exhibit stronger interdependence with monetary policy actions than aggregate uncertainty and risk aversion. The third essay provides the dynamic characterization of the link between ex-ante financial distress risk and the real economy. Using 219, 990 firm-year observations, an ex-ante measure of financial distress is generated at sector level. By employing simultaneous equation model, we provide a comprehensive set-up for predicting ex-ante financial distress risk and examining its effect on GDP growth. Over the period of 1970-2012, the results from the US firms reveal that ex-ante financial distress strongly relate to GDP growth. Ex-ante distress risk contracts GDP growth by up to 10%. Similar contractions in the growth of GDP are uncovered when a single equation approach is used in establishing the relationship between financial distress and growth in GDP. In addition, the results remain consistent when a confirmatory analysis is generated by using a weighted sector index of financial distress. The fourth essay examines liquidity risk and financial integration using bank level flows for 95 countries. The results suggest that global banking network influences liquidity risk. The more the banks are connected to each other the more they are prone to liquidity risk and the result is the same for intermediaries in the network formation. Borrowers that connect to important lenders are not at an advantage, but banks that have independent access to finance in the financial network are at an advantage. On a regional basis, banks in Europe, Africa and Asia and Pacific that have strong connections are prone to liquidity risk. American banks that function as intermediary in the network are prone to liquidity risk. Banks in the American region that have independent access to financing in the financial network are able to reduce liquidity creation, but their degree of connectivity worsens net stable funding.

The interconnectedness of financial institutions affects instability and credit crises. To quantify systemic risk we introduce here the PD model, a dynamic model that combines credit risk techniques with a contagion mechanism on the network of exposures among banks. A potential loss distribution is obtained through a multi-period Monte Carlo simulation that considers the probability of default (PD) of the banks and their tendency of defaulting in the same time interval. A contagion process increases the PD of banks exposed toward distressed counterparties. The systemic risk is measured by statistics of the loss distribution, while the contribution of each node is quantified by the new measures PDRank and PDImpact. We illustrate how the model works on the network of the European Global Systemically Important Banks. For a certain range of the banks’ capital and of their assets volatility, our results reveal the emergence of a strong contagion regime where lower default correlation between banks corresponds to higher losses. This is the opposite of the diversification benefits postulated by standard credit risk models used by banks and regulators who could therefore underestimate the capital needed to overcome a period of crisis, thereby contributing to the financial system instability.

Systematic credit risk in securitised mortgage portfolios

Identification and inference of network formation games with misclassified links