The recent crisis has brought to the fore a crucial question that remains still open: what would be the optimal architecture of financial systems? We investigate the stability of several benchmark topologies in a simple default cascading dynamics in bank networks. We analyze the interplay of several crucial drivers, i.e., network topology, banks' capital ratios, market illiquidity, and random vs targeted shocks. We find that, in general, topology matters only--but substantially--when the market is illiquid. No single topology is always superior to others. In particular, scale-free networks can be both more robust and more fragile than homogeneous architectures. This finding has important policy implications. We also apply our methodology to a comprehensive dataset of an interbank market from 1999 to 2011.
We implement a novel method to detect systemically important financial institutions in a network. The method consists in a simple model of distress and losses redistribution derived from the interaction of banks' balance-sheets through bilateral exposures. The algorithm goes beyond the traditional default-cascade mechanism, according to which contagion propagates only through banks that actually default. We argue that even in the absence of other defaults, distressed-but-non-defaulting institutions transmit the contagion through channels other than solvency: weakness in their balance sheet reduces the value of their liabilities, thereby negatively affecting their interbank lenders even before a credit event occurs. In this paper, we apply the methodology to a unique dataset covering bilateral exposures among all Italian banks in the period 2008-2012. We find that the systemic impact of individual banks has decreased over time since 2008. The result can be traced back to decreasing volumes in the interbank market and to an intense recapitalization process. We show that the marginal effect of a bank's capital on its contribution to systemic risk in the network is considerably larger when interconnectedness is high (good times): this finding supports the regulatory work on counter-cyclical (macroprudential) capital buffers.
In this article, we generalize the classical Edgeworth series expansion used in the option pricing literature. We obtain a closed-form pricing formula for European options by employing a generalized Hermite expansion for the risk neutral density. The main advantage of the generalized expansion is that it can be applied to heavy-tailed return distributions, a case for which the standard Edgeworth expansions are not suitable. We also show how the expansion coefficients can be inferred directly from market option prices.
The problem of optimal investment with CRRA (constant, relative risk aversion) preferences, subject to dynamic risk constraints on trading strategies, is the main focus of this paper. Several works in the literature, which deal either with optimal trading under static risk constraints or with VaR{based dynamic risk constraints, are extended. The market model considered is continuous in time and incomplete, and the prices of financial assets are modeled by It^o processes. The dynamic risk constraints, which are time and state dependent, are generated by a general class of risk measures. Optimal trading strategies are characterized by a quadratic BSDE. Within the class of time consistent distortion risk measures, a three{fund separation result is established. Numerical results emphasize the effects of imposing risk constraints on trading.
A fast method is developed for value-at-risk and expected shortfall prediction for univariate asset return time series exhibiting leptokurtosis, asymmetry and conditional heteroskedasticity. It is based on a GARCH-type process driven by noncentral t innovations. While the method involves the use of several shortcuts for speed, it performs admirably in terms of accuracy and actually outperforms highly competitive models. Most remarkably, this is the case also for sample sizes as small as 250.
