Adam Zawadowski

Entangled Financial Systems

I model an entangled financial system in which banks hedge their portfolio risks using over-the-counter (OTC) contracts. However, banks choose not to hedge counterparty risk, and thus the idiosyncratic failure of a bank can lead to a systemic run of lenders. An inefficiency arises because banks engage in a version of risk shifting through the network externalities created by OTC contracts. Banks do not take into account that the costly hedging of low-probability counterparty risk also benefits other banks. In the model, it is welfare improving to tax OTC contracts to finance a bailout fund. The Author 2013. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Large price changes on small scales

In this study we examine the evolution of price, volume, and the bid–ask spread after extreme 15min intraday price changes on the NYSE and the NASDAQ. We find that due to strong behavioral trading there is an overreaction. Furthermore, we find that volatility which increases sharply at the event decays according to a power law with an exponent of ≈0.4, i.e., much faster than the autocorrelation function of volatility.

Price drops, fluctuations, and correlation in a multi-agent model of stock markets

In this paper, we compare market price fluctuations with the response to fundamental price drops within the Lux–Marchesi model which is able to reproduce the most important stylized facts of real market data. Major differences can be observed between the decay of spontaneous fluctuations and changes due to external perturbations reflecting the absence of detailed balance, i.e., of the validity of the fluctuation–dissipation theorem. We found that fundamental price drops are followed by an overshoot with a rather robust characteristic time.

Interwoven Lending, Uncertainty, and Liquidity Hoarding

This paper shows how uncertainties about funding in an interwoven system of intermediation can lead to excessive liquidity hoarding. In the model, funds are channeled through several financial intermediaries (banks) until they are finally invested in real assets. In case of a funding shock, banks that are uncertain about their own loans being rolled over, fear bankruptcy and cannot commit to rolling over loans they made to others either. This fear can lead local funding uncertainties to prompt banks to liquidate inefficiently large amounts of real assets without any defaults in equilibrium. The results hold even though the only source of risk aversion in the model is due to bankruptcy cost, banks are otherwise risk-neutral. The model suggests a novel explanation for the drop in lending during the Financial Crisis of 2007-2008.

Correlations and response: absence of detailed balance on the stock market

In and near thermal equilibrium microscopic reversibility resulting in detailed balance has specific consequences like the symmetry of time dependent cross correlation (TDCC) functions and fluctuation dissipation theorem. In this paper we study some consequences of the absence of microscopic reversibility on financial processes. By analyzing high resolution data we find asymmetric TDCC functions indicating dominance of some companies in the price formation procedure. We show that in the Lux–Marchesi multi agent market model spontaneous fluctuations decay differently from perturbations caused by external effects.

Time dependent correlations and response in stock market data and models

We study some consequences of the absence of microscopic reversibility on financial processes. We analyze high resolution data and find asymmetric time dependent cross correlation functions indicating dominance of some companies in the price formation procedure. These effects can be summarised in a directed netowrk of influence. Furthermore, we show that in the Lux-Marchesi multi agent market model spontaneous fluctuations decay differently from perturbations caused by external effects. The latter are easily controlled in the model, however, in real data the separation of the internal and the external effects is a highly nontrivial task.