Abstract: Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR). We provide a comprehensive look at the problem by considering the impact that different distributional assumptions have on the accuracy of both univariate and multivariate GARCH models in out-of-sample VaR prediction. The set of analyzed distributions comprises the normal, Student, Multivariate Exponential Power and their corresponding skewed counterparts. The accuracy of the VaR forecasts is assessed by implementing standard statistical backtesting procedures used to rank the different specifications. The results show the importance of allowing for heavy-tails and skewness in the distributional assumption with the skew-Student outperforming the others across all tests and confidence levels.
Abstract: This paper provides a behavioural motivation for the interbank market downturn that occurred during the global financial crisis. Two types of interdependency, represented in a counterfactually simulated bilayer network model are considered namely, direct exposures arising due to borrowing/lending behaviour of banks on the interbank market and indirect exposures in which banks are linked due to commonly-held securities in overlapping portfolios. Embedded in both layers of the simulated network, we develop an Agent-Based Model whereby banks use adaptive expectations in the form of simple heuristics to fix interbank borrowing/lending volumes, set interbank rates and sell off their securities to meet obligations to creditors. The heuristics are constructed to reflect banks' funding liquidity, counterparty and market liquidity risk in a tractable manner.
Under review at Journal of Money, Credit and Banking
Abstract: In this paper, we study the extent to which financial interconnectedness drives dynamics within the banking sector as well as interactions between banks and the wider macroeconomy. To this end, we develop a novel methodology combining a DSGE model featuring an active banking sector with a stylised network structure representing their interbank exposures. The micro-founded framework allows inter alia for endogenous bank defaults and bank capital requirements. In addition, we introduce a central bank who intervenes directly in the interbank market through liquidity injections. Model dynamics are driven by standard productivity as well as banking sector shocks. In our simulations, we incorporate four different interbank network structures: Complete, cyclical and two variations of the (realistic) core-periphery topology. Comparison of interbank market dynamics under the different topologies reveals a strong stabilising role played by the complete network while the remaining structures show a non-negligible shock propagation mechanism. Finally, we show that central bank interventions can counteract negative banking shocks with the effect depending again on the network structure.
Abstract: This paper examines the relationship between the topology of interbank networks and their ability to propagate localised, idiosyncratic shocks across the banking sector via banks' interbank claims on one another. We begin by creating a wide variety of networks and heterogenous balance sheet structures using a generative algorithm capable of replicating key characteristics of real-world interbank networks. On each network, we run a standard financial contagion model with cascading defaults. Our modelling framework differentiates between random and targeted shocks of varying magnitude. Interbank contagion comprises a direct channel via banks' cross-exposures and an indirect channel due to liquidity effects and external asset firesales. Lastly, we develop an empirical model to test which global features of the network, aggregate banking sector balance sheet and shock properties drive contagion dynamics.