I am an Associate Professor at the University of Cape Town and hold the South African Reserve Bank Research Chair in Financial Stability Studies. I also hold a part-time position as Research Economist at Deutsche Bundesbank. Please note that this is my private website and the views presented here do not necessarily reflect the views of the SARB, Deutsche Bundesbank or the ESCB.
New Positions I am looking for Postdoctoral Research Fellows and PhD Students in two areas: the data economy (ad here) and the future of finance (ad here).
Teaching We have launched a new fantastic online short course on Blockchain and Digital Currency: The Future of Money in collaboration with GetSmarter. The course is great if you are interested in central bank digital currencies. You can enroll here. You can also find our previous course on Fintech: Disruption in Financehere. You can also find all four of our Coursera MOOCS online which are part of our specialization Fintech Startups in Emerging Markets Specialization.
Most of the code I am developing is on github. In particular, check out my teaching repo and the included wiki, which contains a lot of additional material.
The slides for my Fintech and Cryptocurrencies course are quite large, so they are online here.
Research
Publications
February 2021. "What 5,000 Acknowledgements Tell Us About Informal Collaboration in Financial Economics" (with Michael Rose) [abstract] [paper] [data repo]
[voxeu] - Accepted, Research Policy
Formal collaboration between researchers via co-authorship has been shown to have a positive impact on academic productivity. But most academic collaboration is informal, e.g. in the form of commentary on research papers. We present and discuss a novel dataset on informal collaboration in Financial Economics obtained from the acknowledgement sections of over 5,000 published research papers. We construct the social network of informal collaboration connecting authors and commenters and show that a researcher’s position in this network is predictive of her future productivity and the scholarly impact of the papers she comments on. We study the characteristics of the network using various measures from network theory and characterize what determines a researcher’s position in it.
Financial economists collaborate by commenting on each other's papers. We use the information captured in acknowledgements, construct the social network of informal collaboration, and highlight several interesting stylized facts.
November 2020. "Systemic Risk-Shifting in Financial Networks" (with Matthew Elliott and Jonathon Hazell), Journal of Economic Theory 191 (2021). [abstract] [paper]
Banks face different but potentially correlated risks from outside the financial system. Financial connections can help hedge these risks, but also create the means by which shocks can propagate. We examine this tradeoff in the context of a new stylised fact we present: German banks are more likely to have financial connections when they face more similar risks—potentially undermining the hedging role of financial connections and contributing to systemic risk. We find that such patterns are socially suboptimal, but can be explained by risk-shifting. Risk-shifting motivates banks to correlate their failures with their counterparties even though it creates systemic risk.
We provide a novel mechanism why banks would lend to other banks that have similar real exposures and show that predictions of our model are in line with empirical evidence from Germany.
May 2017. "Information Contagion and Systemic Risk" (with Toni Ahnert), Journal of Financial Stability 35 (2017). [abstract] [paper]
We examine the effect of ex-post information contagion on the ex-ante level of systemic risk defined as the probability of joint default of banks. Because of counterparty risk or common exposures, bad news about one bank reveals valuable information about another bank and trigger information contagion. When banks are subject to common exposures, information contagion induces small adjustments to bank portfolios and therefore increases systemic risk overall. When banks are subject to counterparty risk, by contrast, information contagion induces a large shift toward more prudential portfolios and therefore reduces systemic risk.
Information contagion can reduce systemic risk if banks lend to each other because ex-post counterparty risk leads to more prudent ex-ante portfolio choice.
January 2015.
"Contagious Synchronization and Endogenous Network Formation in Financial Networks" (with Christoph Aymanns), Journal of Banking and Finance 50(1) (2015). [abstract] [paper] [code available upon request]
When banks choose similar investment strategies the financial system becomes vulnerable to common shocks. We model a simple financial system in which banks decide about their investment strategy based on a private belief about the state of the world and a social belief formed from observing the actions of peers. Observing a larger group of peers conveys more information and thus leads to a stronger social belief. Extending the standard model of Bayesian updating in social networks, we show that the probability that banks synchronize their investment strategy on a state non-matching action critically depends on the weighting between private and social belief. This effect is alleviated when banks choose their peers endogenously in a network formation process, internalizing the externalities arising from social learning.
Banks act based on a private and a social signal in a simple extension of Bayesian learning. The social signal is stronger if banks observe a larger group of peers which leads to correlated investment strategies of highly interconnected banks.
February 2013.
"The Effect of the Interbank Network Structure on Contagion and Common Shocks", Journal of Banking and Finance 37(7) (2013). [abstract] [paper] [code]
This paper proposes a dynamic multi-agent model of a banking system with central bank. Banks optimize a portfolio of risky investments and riskless excess reserves according to their risk, return, and liquidity preferences. They are linked via interbank loans and face stochastic deposit supply. Evidence is provided that the central bank stabilizes interbank markets in the short-run only. Comparing different interbank network structures, it is shown that money-center networks are more stable than random networks. Systemic risk via contagion is compared to common shocks and it is shown that both forms of systemic risk require different optimal policy responses.
A dynamic multi-agent model of the financial system. The interbank network structure does not always affect financial stability and central bank liquidity provision can stabilize the interbank market in the short-run only.
Papers Under Revision
November 2020. "Illiquidity Spirals in Over-the-Counter Repo Markets" (with Christoph Aymanns and Ben Golub) [abstract] [paper]- Revision requested, Operations Research
We model intermediaries trading economically coupled assets, each asset in its own over-the-counter market--e.g., secured debt and the underlying collateral. Incentives to provide liquidity in one market are increasing in counterparties' activity in both markets. The intermediaries' activity is thus the outcome of a game of strategic complements on two coupled trading networks. We model a crisis as an exogenous change to network structure, as well as the exogenous exit of some intermediaries. This causes an illiquidity spiral across the two networks. We find that in coupled networks, in contrast to uncoupled ones, illiquidity spirals can be so severe that liquidity vanishes discontinuously as we vary the shock. Liquidity can be improved if one of the two OTC markets is replaced by an exchange, or if the two OTC markets have more links in common.
The extent of illiquidity spirals in over-the-counter repo markets depends on the network structure of the repo and collateral market.
August 2017. "A Network View on Interbank Liquidity" (with Silvia Gabrieli) [abstract] [paper] - Revision requested, Journal of Banking and Finance
The euro area overnight interbank market is best described as a network of over-the-counter lending relationships. We study liquidity reallocation in this interbank network using a novel dataset of all interbank loans settled between European banks. We show the existence of a centrality premium when banks act as intermediaries of liquidity: banks with a one standard deviation higher betweenness centrality capture a 30% larger intermediation spread. Our results are in line with predictions from models of intermediation and bargaining in networks, but are difficult to reconcile with search based models of over-the-counter markets.
The euro area interbank market is an over-the-counter market with a non-trivial network structure. Banks in this market enjoy a centrality premium which can easily be understood in models of bargaining in networks.
We study the role of informal collaboration in academic knowledge production. Our focus is on published papers presented at similar workshops at the NBER Summer Institutes. Though not random, our setting is conducive to studying the role of informal collaboration in academic knowledge production. Papers are of comparable quality and the workshops are otherwise similar. Even among a set of papers that is highly selected on expected quality, discussants matter for top journal publication. Conditional on having a discussant, a paper’s citation count increases in the discussant’s prolificness. Our findings support the existence of quality-improving channels through which discussants improve the inherent quality of a paper. Conversely, using social network analysis we rule out a diffusion channel through which citations accumulate because discussants diffuse information about the paper within their social network.
Getting feedback from discussants increases the impact of research through a quality-improvement channel and not through diffusion.
July 2020. "SABCoM: A Spatial Agent-Based Covid-19 Model
" (with Allan Davids, Gideon du Rand, Tina Koziol, and Joeri Schasfoort) [abstract] [paper] [github]
How effective are 'lockdown' measures and other policy interventions to curb the spread of Covid-19 in emerging market cities that are characterized by large heterogeneity and high levels of informality? The most commonly used models to predict the spread of Covid-19 are SEIR models which lack the spatial resolution necessary to answer this question. We develop an agent-based model of social interactions in which the distribution of agents across wards, as well as their travel and interactions are calibrated to real data for Cape Town, South Africa. We characterize the elasticity of various policy interventions including increased likelihood to self-isolate, travel restrictions, assembly bans, and behavioural interventions like washing hands or wearing masks. Even in an informal setting, where agents' ability to self-isolate is compromised, a lockdown remains an effective intervention. In our model, the lockdown enacted in South Africa reduced expected fatalities in Cape Town by 26% and the expected demand for intensive care beds by 46%. However, our best calibration predicts a substantially higher case load, demand for ICU beds, and expected number of deaths than the current best estimate published for Cape Town.
SABCoM is a multi-agent epidemiological model to understand the spread of Covid-19 in Cape Town and to evaluate policy responses.
May 2020. "The Cape of Good Homes: Exchange Rate Depreciations, Foreign Demand and House Prices" (with Allan Davids) [abstract] [paper]
Emerging markets are characterized by frequent periods of large and unexpected exchange rate depreciations. These events create opportunities for foreign investors to purchase domestic assets at a discount, especially if these assets have sticky prices. We show this to be the case in the housing market in Cape Town, South Africa. Using property transaction data, we find that foreign non-residents buy more properties following large exchange rate depreciations—in the lower quartile of month-on-month changes. We find no evidence of a similar effect for other buyers, suggestive of strong exchange rate-related motives. Using these depreciations as demand shocks to foreign non-resident buyers, we find that this increased demand leads to an increase in house prices of 3.39%. We find that foreign non-resident buyers pay 10.42% more than other buyers for otherwise identical properties and that this tendency to pay a premium accounts for around 27% of the observed causal impact of foreign demand on house prices.
Following historically large exchange rate depreciations, foreign demand for property in Cape Town increases. This does not drive up prices for locals, though. Instead, foreigners realize lower capital gains upon resale.
Consistent with theoretical predictions, we show that investors incorporate expected joint liquidation costs in their portfolio decisions. Using detailed security-level holdings of U.S. Money Market Mutual Funds (MMFs), we construct a new measure of portfolio similarity among investors and show that investors actively manage asset holdings as a function of how similar their portfolios are with those of other investors. They are less likely to roll over investments and they decrease funding when similarity increases. At the issuer level, average similarity also predicts her total funding in the next period. Importantly, issuers are unable to fully replace the loss in funding when similar investors withdraw.
Mutual funds reduce their exposure to a security issuer more if they are more similar to other funds investing in the same security issuer. This increases issuers' liquidity risk.
Despite a heated debate on the perceived increasing complexity of financial regulation, there is no available measure of regulatory complexity other than the mere length of regulatory documents. To fill this gap, we propose to apply simple measures from the computer science literature by treating regulation like an algorithm - a fixed set of rules that determine how an input (e.g., a bank balance sheet) leads to an output (a regulatory decision). We apply our measures to the regulation of a bank in a theoretical model, to an algorithm computing capital requirements based on Basel I, and to actual regulatory texts. Our measures capture dimensions of complexity beyond the mere length of a regulation. In particular, shorter regulations are not necessarily less complex, as they can also use more "high-level" language and concepts. Finally, we propose an experimental protocol to validate measures of regulatory complexity.
The amount of (financial) regulation has increased significantly over the past decades. But has regulation really become more complex? We propose to use measures of software complexity to provide a more nuanced answer to this question and apply our measures to Basel I and the Dodd-Frank Act.
We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors' past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in accordance with the veracity of claims. Our framework yields strategies with agent utility close to a theoretical, Bayes optimal benchmark, while remaining flexible to model re-specification. Optimized strategies allow agents to correctly identify most false claims, when all agents receive unbiased private signals. However, an adversary's attempt to spread fake news by targeting a subset of agents with a biased private signal can be successful. Even more so when the adversary has information about agents' network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary's attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users' private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected.
We model the spread of fake news as a learning game on a social network and use deep learning methods to solve the model. When an attacker has access to information about the strength of users' private beliefs, fake news spread faster.
Work in progress:
"Contagious Zombies" (with Christian Bittner and Falko Fecht).
"Anticipated Financial Contagion" (with Toni Ahnert and Gideon du Rand).
Interdisciplinary, Policy, and Other Publications
"Interbank Intermediation" (with Marcel Bluhm and Jan-Pieter Krahnen) [abstract] [paper]
This paper explores the economics of interbank lending and borrowing using bank-balance sheet data for Germany, the largest European economy. Our 2002-2014 panel data set allows us to analyze the cross section and the dynamics of the observed interbank exposures. Our findings suggest a genuine intermediation process within the banking system, with implications for allocative efficiency and financial stability. A typical bank in our sample holds a significant amount of term and overnight interbank positions on both sides of the balance sheet simultaneously, and at any point in time. The average contract length in the German interbank market is well above one year, which stands in contrast to the widely held view that interbank exposures are largely overnight. Based on panel regressions, we find the build-up of the interbank book to be driven by innovations in the client book (i.e. non-bank deposit taking and lending). The resulting interbank book affects the bank’s duration gap, the maturity disparity between bank assets and bank liabilities. A bank’s duration gap is often seen as its major macroeconomic risk factor. Overall our findings lend support to a theory of banking that involves leverage stacks, i.e intermediation among banks.
"Revealing patterns of local species richness along environmental gradients with a novel network tool" (with Mara Baudena, Utrecht, Angel Sanchez, UC3M, Paloma Ruiz-Benito, Alcala, Miguel A. Rodriguez, Alcala, Miguel A. Zavala, Alcala, and Max Rietkerk, Utrecht), Nature Scientific Reports 5 (2015). [paper]
"Seven Questions on Financial Interconnectedness" (with Camelia Minoiu, IMF), IMF Research Bulletin, (2014), March. [.pdf]
"Complex Derivatives" (with Stefano Battiston, Guido Caldarelli, Robert M. May, and Joseph E. Stiglitz), Nature Physics Vol.9, No.3, (2013). [abstract] [focus]
The intrinsic complexity of the financial derivatives market has emerged as both an incentive to engage in it, and a key source of its inherent instability. Regulators now faced with the challenge of taming this beast may find inspiration within the budding science of complex systems..
"Systemic Risk in the Financial Sector" (with Ian Goldin, Mike Mariathasan, and Tiffany Vogel), in: Ian Goldin and Mike Mariathasan: "The Butterfly Defect - Globalisation and Systemic Risk", Princeton University Press, (2014)
"Note on interlinkages in the South African interbank system" (with Nicola Brink), Special Note in the Financial Stability Review, South African Reserve Bank (March 2011). [abstract] [fsr]
This paper analyses the network structure of the South African overnight interbank market by employing measures from network theory. A unique data set of interbank transactions from the South African Multiple Options Settlement (SAMOS) system is used. It is shown that the South African interbank system has been largely stable and resilient over the period from March 2005 to June 2010, even in times of great distress on the international financial markets. The number of banks participating in the interbank market was approximately constant over the analysed period, as well as the high level of interconnectedness. A low average path length and high clustering coefficient indicate a high level of liquidity allocation and risk sharing in the system. Furthermore a Network Systemic Importance Index (NSII) is developed to assess the systemic importance of individual banks in South Africa. This index measures each banks size, interconnectedness and substitutability by employing network theory. It is a relative index in the sense that the systemic importance of any given bank does not only depend on the properties of the bank itself, but rather on the properties of the whole network. This approach is therefore less prone to moral hazard and can be used as a tool for macroprudential oversight in addition to microprudential supervision. The NSII addresses the cross-sectional dimension of systemic risk. It has to be stressed, however, that it gives no indication of the default probability of individual banks and has therefore be accompanied by other macroprudential tools for a full picture of systemic risk.
"Basel III and Systemic Risk Regulation - What Way Forward?", Global Financial Markets Working Paper Series 17-2011, (2011). [abstract] [paper] .
One of the most pressing questions in the aftermath of the financial crisis is how to deal with systemically important financial institutions
(SIFIs). The purpose of this paper is to review the recent literature on systemic risk and evaluate the regulation proposals in the Basel III framework with respect to this literature. A number of shortcomings in the current framework are analyzed and three measures for future reform are proposed: counter-cyclical risk-weights, dynamic asset value correlation multipliers, and enhanced transparency requirements for SIFIs.
Software and Web Projects:
We have built Registree, a decentralized student data platform that enables students to take ownership of their personal data. You can find our white paper here.
We are developing Black Rhino, an open source financial network multi-agent model framework. You can find the latest release (including a short tutorial) in our github repository.
Team
I am fortunate to work with a group of outstanding students and postdoctoral researchers. If your contacts are missing or outdated, it is time to get in touch again!
University of Cape Town
Postdoctoral Researchers (#: First placement)
Joeri Schasfoort (PhD Groningen, 10/2019-)
Suraj Shekhar (PhD Penn State, 08/2016-06/2019, #: Ashoka University, New Delhi)
Gideon du Rand (MSc Stellenbosch, 04/2015-02/2020, PhD Student at Stellenbosch, #: Lecturer, Stellenbosch)
Bundesbank
Alexander Valentin (2019, intern, PhD Student Goethe University Frankfurt)
Xiangling (Flora) Meng (2018, intern, Master Student MIT CSAIL)
Philippa Sigl-Gloeckner (2017, intern, Master Student Imperial College London. Her thesis can be found here.)
Sabine Bertram (2016, intern, Master Student HU Berlin)
Ali Josue Limon (2016, intern, Master Student NYU)
Dieter Wang (2015, intern, Master Student Tinbergen Institute, Amsterdam)
Andrea Deghi (2015, intern, PhD Student Siena)
Dr. Raphael Flore (2014, intern, PhD Student Cologne)
Christoph Aymanns (2013, intern, PhD Student Oxford)
Niccolo Stamboglis (2013, intern, PhD Student City University London)
Florian Urbschat (2013, intern, Master Student at University of Hamburg)
Tarik Roukny (2013, intern, PhD Student ULB)
Teaching
Important. I created a github repository for all my teaching material. In particular, I have uploaded the slides for my course on Financial Regulation and Fintech and Cryptocurrencies. Check out the wiki which contains tons of material. Comments welcome.
We have launched a fantastic online short course on Fintech: Disruption in Finance aimed at finance professionals in collaboration with GetSmarter. You can enroll here.
E-mail: co-pierre.georg@uct.ac.za | Public key PGP: 9C5F 4812 9A6A 6717 F285 3825 ED2B 8B83 BC96 CCAB Postal Address: University of Cape Town, School of Economics, Private Bag X1, Cape Town 8000, South Africa
We are developing Black Rhino, an open source financial network multi-agent model framework. You can find the latest release (including a short tutorial) in our github repository.