Narrow Results

The main focus of this research is the contagion of a financial crisis on an interbank debt network. In order to simulate the crisis propagation a weighted community complex network based on growth strategy has been created. The contagion is described by a new way of disease propagation perspective based on the concept of a financial virus. The model reproduces the existence of TBTF banks and shows the impact that an initial TBTF bank crash produces in the interbank network depending on the magnitude of the initial crash and on the resistance that the network offers against the contagion propagation.

In this paper, we study and simulate the effect of individual social responses, as a collective factor, on the epidemic spreading processes. We formally define the problem based on the traditional $SI$ and $SIR$ compartmental models considering the time-varying infection probability dependent on the social responses. In this study, models of generic and special case scenarios are developed. While in the generic case the effective parameter of behavioral response is demonstrated as one collective factor, in the special case the behavioral response is assumed as the combination of two collective factors: social cost and transfer rate of social awareness. With social cost, we refer to the costs incurred by a certain population to prevent or mitigate an epidemic. With transfer rate of social awareness, we describe the averaged rate of received information and knowledge regarding a disease that individuals hold and make use to avoid negative consequences. We show that, while in both $SI$ and $SIR$ models the density of infected agents grows exponentially during the initial time steps, the inclusion of our models of social responses, either generic or special one, leads to mitigation of the spreading. As a result of both generic and special cases, the density of infected agents in the stationary state and the maximum number of infected agents decrease according to power-law functions for different values of collective factors. In the special case results, we also witnessed significant changes in the slope of decreasing trends of stationary density of states happening for a critical value of transfer rate of social awareness, approximately at about the inverse of the time interval of transmission rate update. With this result, we point out that increasing the transfer rate of social awareness to about this critical point outperforms any slight increase in social cost in reducing the number of infected agents.

This paper investigates the effects of the East Asian crisis on the Indian economy and exchange rate movements. Despite the contagion effects that profoundly affected the other crisis-hit countries, the Indian economy and the rupee were found less affected. Reforms after the 1990–1991 crisis, control of capital flows, weak economic linkages with crisis-affected countries and stabilization policies that include intervention in the foreign exchange market and tightening of monetary policy are reasons for the insulation of the Indian economy from the crisis.

This paper considers the valuation problem of basket CDSs. Based on the construction of total hazard rates, the paper develops the work of Zheng and Jiang Zheng and Jiang (2009) from the homogenous case to the primary-subsidiary heterogenous case in the interacting intensity framework, and obtains the corresponding joint density of the default time. Moreover, the paper derives the valuation formulae for the basket CDSs with and without counterparty risk. Numerical results robustly show that, under certain conditions, using the analytical pricing formulae derived in this paper is more efficient than the Monte Carlo method for the basket CDS valuation.

We find evidence of "pure" contagion effects in international banking arising from the collapse of BCCI. A Markov regime-switching approach is employed to allow for the uncertainty surrounding the date of BCCI's collapse. The results indicate that there are shortcomings in the supervision of internationally spread banking groups like BCCI, and carry implications for the EU single market programme in financial services.

We consider a reduced-form credit risk model where default intensities and interest rate are functions of a not fully observable Markovian factor process, thereby introducing an information-driven default contagion effect among defaults of different issuers. We determine arbitrage-free prices of OTC products coherently with information from the financial market, in particular yields and credit spreads and this can be accomplished via a filtering approach coupled with an EM-algorithm for parameter estimation.

The paper is concerned with counterparty credit risk for credit default swaps in the presence of default contagion. In particular, we study the impact of default contagion on credit value adjustments such as the Bilateral Collateralized Credit Value Adjustment (BCCVA) of Brigo et al. (2014) and on the performance of various collateralization strategies. We use the incomplete-information model of Frey & Schmidt (2012) for our analysis. We find that contagion effects have a substantial impact on the effectiveness of popular collateralization strategies. We go on and derive improved collateralization strategies that account for contagion. Theoretical results are complemented by a simulation study.

A heat kernel approach is proposed for the development of a novel method for asset pricing over a finite time horizon. We work in an incomplete market setting and assume the existence of a pricing kernel that determines the prices of financial instruments. The pricing kernel is modeled by a weighted heat kernel driven by a multivariate Markov process. The heat kernel is chosen so as to provide enough freedom to ensure that the resulting model can be calibrated to appropriate data, e.g. to the initial term structure of bond prices. A class of models is presented for which the prices of bonds, caplets, and swaptions can be computed in closed form. The dynamical equations for the price processes are derived, and explicit formulae are obtained for the short rate of interest, the risk premium, and for the stochastic volatility of prices. Several of the closed-form models presented are driven by combinations of Markovian jump processes with different probability laws. Such models provide a basis for consistent applications in various market sectors, including equity markets, fixed-income markets, commodity markets, and insurance. The flexible multidimensional and multivariate structure on which the resulting price models are based lends itself well to the modeling of dependence across asset classes. As an illustration, the impact of spiraling debt, a typical feature of a financial crisis, is modeled explicitly, and the contagion effects can be readily observed in the dynamics of the associated asset returns.

The scope of financial systemic risk research encompasses a wide range of interbank channels and effects, including asset correlation shocks, default contagion, illiquidity contagion, and asset fire sales. This paper introduces a financial network model that combines the default and liquidity stress mechanisms into a “double cascade mapping”. The progress and eventual result of the crisis is obtained by iterating this mapping to its fixed point. Unlike simpler models, this model can therefore quantify how illiquidity or default of one bank influences the overall level of liquidity stress and default in the system. Large-network asymptotic cascade mapping formulas are derived that can be used for efficient network computations of the double cascade. Numerical experiments then demonstrate that these asymptotic formulas agree qualitatively with Monte Carlo results for large finite networks, and quantitatively except when the initial system is placed in an exceptional “knife-edge” configuration. The experiments clearly support the main conclusion that when banks respond to liquidity stress by hoarding liquidity, then in the absence of asset fire sales, the level of defaults in a financial network is negatively related to the strength of bank liquidity hoarding and the eventual level of stress in the network.

We propose a tool for monitoring fire sales and fund liquidations in financial markets. This liquidation index detects fire sales episodes in a contemporaneous manner and estimates their magnitude, using only publicly available data (asset prices and volumes). At every date $t$, it takes as input the movement of asset prices and realized covariances between dates $t-\tau$ and $t$ and the market depth of each asset and estimates a theoretical magnitude for fire sales over the period $[t-\tau ,t]$ that generated such joint movement of prices and covariances. As such, the liquidation index spikes during fire sales episodes and can hence be used in a systemic risk management perspective, as it enables to detect fire sales episodes — even complex liquidation events such as the hedge fund crash of August 2007 which was undetected by commonly-used monitoring tools. It can also be useful in a trading and portfolio allocation perspective as it allows to distinguish between periods of “fundamental” asset behavior from fire sales periods, characterized by crowding and contagion effects and during which diversification effects are reduced.

We use the case of the 2007 United States subprime mortgage crisis to investigate the impact of borrowing capacity limitations on financial instability and contagion. We divide an economy into agents that interact via flow of funds and express the financial instability level of each agent as a function of time derivatives of its wealth, cash inflows, and borrowing capacity. We show that among these factors, the borrowing capacity, which is determined by other economic constraints, has the largest impact on financial instability. It is suggested that borrowing capacity limitations could even cause contagion through feedback loop formed by flow of funds. We use historical time series of the integrated macroeconomic accounts of the United Stated from 1960 to 2017 to verify our conjecture by quantifying the financial instability levels of the agents under different levels of borrowing capacity and how they affect one another during the period of the subprime mortgage crisis. Finally, the constraints of data collecting practice outside the United States in assessing borrowing capacity is addressed, accompanied by partial, yet compatible, results of selected Eurozone countries.

This paper documents the economic and financial recovery of East Asia based on its real GDP, export, currency value and stock performance since the 1997 financial crisis. A macroeconomic model is used to estimate the chain effect of international trade on Asian recovery. It is found that the U.S. economy had a significant impact on the recovery of this region through close international trade relationship. Two major factors appear to explain the recent rapid recovery: (1) strong U.S. economic growth and currency value, and (2) the current account surplus and net inflow in foreign direct investment of crisis-hit countries.

Following the 1997 financial crisis in East Asia, the issue of contagion has resurfaced. Contagion has most often been associated with high frequency events; hence, it has been measured on stock market returns, interest rates, the exchange rate, or linear combinations of them. This paper tests for evidence of contagion between selected East Asian stock markets, thereby exploring the importance of the linkages between stock markets as a transmission channel during the crisis.

In this paper, we examine the effects of subprime crisis on the largest African stock markets (South Africa, Nigeria, Egypt, and Morocco) by testing the fractal market hypothesis. We use a rolling window Multifractal Detrended Fluctuation Analysis, and find decline in local Hurst exponent and an increase in short-term trading activity for all considered stock markets during the global financial crisis. We furthermore investigate the interrelationships of African and the American stock markets using multi-scale contagion test. Findings suggest that the cross-correlation of African stock markets increases with American markets becoming higher during the crisis sub-period. However, the presence of contagion or interdependence effects are country and time horizon-dependent. Implications of the results are discussed.

We model a simple dynamic process in which myopic agents are matched amongst each other to play a coordination game. The network of player interaction is varied between a regular lattice and a random network allowing us to model contagion in small world networks. Weighting times for an equilibrium shift from the risk dominated to risk dominant equilibrium are shown to be smallest in small world networks.

This case study is part of a research project based in Spain between 2011 and 2014 on the social institutions and affective processes involved in what is normally referred to as social movement. Our purpose is to study how information circulates in small-world networks in which the dynamics are modeled with a stochastic version of Greenberg–Hasting’s excitable model. This is a three-state model in which a node can be in an excited, passive, or susceptible state. Only in the susceptible state does a node interact with its neighbors in the small-world network, and its interaction depends on the probability of contagion. We introduce an infection probability, which is the only parameter in our implementation of Greenberg–Hasting’s model. The small-world network is characterized by a mean connectivity parameter and by a disorder parameter.

The resulting dynamics are characterized by the average activity in the network. We have found transitions from inactive to active collective regimes, and we can induce this transition by varying. We search for different dynamics within small-world networks of citizens’ organizations by going through the following steps: identifying alliance patterns; looking for robust small-world attributes and how they are constructed; and interpreting the three modes of our model.

We consider a model of contagion in financial networks recently introduced in Gai, P. and Kapadia, S. [Contagion in financial networks, Proc. R. Soc. A466(2120) (2010) 2401–2423], and we characterize the effect of a few features empirically observed in real networks on the stability of the system. Notably, we consider the effect of heterogeneous degree distributions, heterogeneous balance sheet size and degree correlations between banks. We study the probability of contagion conditional on the failure of a random bank, the most connected bank and the biggest bank, and we consider the effect of targeted policies aimed at increasing the capital requirements of a few banks with high connectivity or big balance sheets. Networks with heterogeneous degree distributions are shown to be more resilient to contagion triggered by the failure of a random bank, but more fragile with respect to contagion triggered by the failure of highly connected nodes. A power law distribution of balance sheet size is shown to induce an inefficient diversification that makes the system more prone to contagion events. A targeted policy aimed at reinforcing the stability of the biggest banks is shown to improve the stability of the system in the regime of high average degree. Finally, disassortative mixing, such as that observed in real banking networks, is shown to enhance the stability of the system.

We discuss a special Pólya lattice model to study cascading failures of firms in a simple industrial economy. In particular, every firm is represented by a Pólya-like urn, whose reinforcement is function of time, of the neighboring urns and their compositions, and of a random variable representing systemic risk or fate. The simple idea is to build the dependence among firms by assuming simple balance sheet rules on debts and credits. In detail we assume that the debts of every company are credits for some of its neighbors. Debts and credits are represented by different balls in the urns/firms. At the same time we assume that the riskiness of every firm also depends on the economic wealth of its neighbors and of the economy in general. These simple rules are sufficient to create business cycles, in which the accumulation of debts pushes the economy towards frequent crises. The model can be easily simulated and the results we obtain encourage the development of brand new finitary probabilistic approaches to study firms' behavior and dynamics.

In this paper, we use the financial network contagion model of Gai P. and Kapadia S. [Contagion in financial networks, Proc. R. Soc. A466 (2010) 2401–2423] to investigatethe interaction of several types of heterogeneity found in real world banking systems. The first source of heterogeneity originates in the distribution of assets across banks in the financial system. The second source is in how individual banks then distribute these assets among their neighbors. We characterize how these two sources of heterogeneity interact to affect the probability and extent of financial contagions in three network structures. We find that greater heterogeneity has a stabilizing effect for networks that are sparsely connected and a destabilizing effect for networks that are highly interconnected. Finally, we consider multiple sequential shocks and find that when banks redistribute assets following an initial mild contagion it increases the stability, on average, of the system to subsequent shocks originating at weakened banks.

We consider a model of financial contagion in a bipartite network of assets and banks recently introduced in the literature, and we study the effect of power law distributions of degree and balance-sheet size on the stability of the system. Relative to the benchmark case of banks with homogeneous degrees and balance-sheet sizes, we find that if banks have a power law degree distribution the system becomes less robust with respect to the initial failure of a random bank, and that targeted shocks to the most specialized banks (i.e., banks with low degrees) or biggest banks increases the probability of observing a cascade of defaults. In contrast, we find that a power law degree distribution for assets increases stability with respect to random shocks, but not with respect to targeted shocks. We also study how allocations of capital buffers between banks affects the system’s stability, and we find that assigning capital to banks in relation to their level of diversification reduces the probability of observing cascades of defaults relative to size-based allocations. Finally, we propose a non-capital-based policy that improves the resilience of the system by introducing disassortative mixing between banks and assets.