"Counterparty Credit Limits: The Impact of a Risk-Mitigation Measure on Everyday Trading"

A paper of mine (from an extremely old project) final came out in final form today. Here are some details.

Title: Counterparty Credit Limits: The Impact of a Risk-Mitigation Measure on Everyday Trading

Authors: Martin D. Gould, Nikolaus Hautsch, Sam D. Howison, and Mason A. Porter

Abstract: A counterparty credit limit (CCL) is a limit that is imposed by a financial institution to cap its maximum possible exposure to a specified counterparty. CCLs help institutions to mitigate counterparty credit risk via selective diversification of their exposures. In this paper, we analyse how CCLs impact the prices that institutions pay for their trades during everyday trading. We study a high-quality data set from a large electronic trading platform in the foreign exchange spot market that allows institutions to apply CCLs. We find empirically that CCLs had little impact on the vast majority of trades in this data set. We also study the impact of CCLs using a new model of trading. By simulating our model with different underlying CCL networks, we highlight that CCLs can have a major impact in some situations.

'Working Paper' in Collaboration with International Monetary Fund: "Evolution of the Global Financial Network and Contagion: A New Approach"

A 'working paper' from a collaboration with folks from the International Monetary Fund (IMF) came out today. You can download it from this website. We also hope to submit a version of this work to a journal for publication. Here are some details.

Title: Evolution of the Global Financial Network and Contagion: A New Approach

Authors: Yevgeniya Korniyenko, Manasa Patnam, Rita Maria del Rio-Chanon, and Mason A. Porter

Abstract: This paper studies the interconnectedness of the global financial system and its susceptibility to shocks. A novel multilayer network framework is applied to link debt and equity exposures across countries. Use of this approach—that examines simultaneously multiple channels of transmission and their important higher order effects—shows that ignoring the heterogeneity of financial exposures, and simply aggregating all claims, as often done in other studies, can underestimate the extent and effects of financial contagion.The structure of the global financial network has changed since the global financial crisis, impacted by European bank’s deleveraging and higher corporate debt issuance. Still, we find that the structure of the system and contagion remain similar in that network is highly susceptible to shocks from central countries and those with large financial systems (e.g., the USA and the UK). While, individual European countries (excluding the UK) have relatively low impact on shock propagation, the network is highly susceptible to the shocks from the entire euro area. Another important development is the rising role of the Asian countries and the noticeable increase in network susceptibility to shocks from China and Hong Kong SAR economies.

"Quasi-Centralized Limit Order Books"

One of my papers got assigned its final journal coordinates today. (It came out a few months ago in advanced access.) Here are the details.

Title: Quasi-Centralized Limit Order Books

Authors: Martin D. Gould, Mason A. Porter, and Sam D. Howison

Abstract: A quasi-centralized limit order book (QCLOB) is a limit order book (LOB) in which financial institutions can only access the trading opportunities offered by counterpartieswithwhomthey possess sufficient bilateral credit. In this paper, we perform an empirical analysis of a recent, high-quality data set from a large electronic trading platform that utilizes QCLOBs to facilitate trade. We argue that the quote-relative framework often used to study other LOBs is not a sensible reference frame for QCLOBs, so we instead introduce an alternative, trade-relative framework, which we use to study the statistical properties of order flow and LOB state in our data. We also uncover an empirical universality: although the distributions that describe order flow and LOB state vary considerably across days, a simple, linear rescaling causes them to collapse onto a single curve. Motivated by this finding, we propose a semi-parametric model of order flow and LOB state for a single trading day. Our model provides similar performance to that of parametric curve-fitting techniques but is simpler to compute and faster to implement.

"The Long Memory of Order Flow in the Foreign Exchange Spot Market"

One of my papers just came out in final form. Here are the details.

Title: The Long Memory of Order Flow in the Foreign Exchange Spot Market

Authors: Martin D. Gould, Mason A. Porter, and Sam D. Howison

Abstract: We study the long memory of order flow for each of three liquid currency pairs on a large electronic trading platform in the foreign exchange (FX) spot market. Due to the extremely high levels of market activity on the platform, and in contrast to existing empirical studies of other markets, our data enables us to perform statistically stable estimation without needing to aggregate data from different trading days. We find strong evidence of long memory, with a Hurst exponent $H \approx 0.7$, for each of the three currency pairs and on each trading day in our sample. We repeat our calculations using data that spans different trading days, and we find no significant differences in our results. We test and reject the hypothesis that the apparent long memory of order flow is an artifact caused by structural breaks, in favor of the alternative hypothesis of true long memory. We therefore conclude that the long memory of order flow in the FX spot market is a robust empirical property that persists across daily boundaries.

"Community Detection in Temporal Multilayer Networks, with an Application to Correlation Networks"

One of my papers was just published in final form a few days ago. As the title indicates, this paper concerns community detection in temporal multilayer networks. Here are the title, authors, and abstract.

Title: Community Detection in Temporal Multilayer Networks, with an Application to Correlation Networks

Authors: Marya Bazzi, Mason A. Porter, Stacy Williams , Mark McDonald, Daniel J. Fenn, and Sam D. Howison

Abstract: Abstract. Networks are a convenient way to represent complex systems of interacting entities. Many networks contain “communities” of nodes that are more densely connected to each other than to nodes in the rest of the network. In this paper, we investigate the detection of communities in temporal networks represented as multilayer networks. As a focal example, we study time-dependent financial-asset correlation networks. We first argue that the use of the "modularity" quality function –– which is defined by comparing edge weights in an observed network to expected edge weights in a "null network" –– is application-dependent. We differentiate between "null networks" and "null models" in our discussion of modularity maximization, and we highlight that the same null network can correspond to different null models. We then investigate a multilayer modularity-maximization problem to identify communities in temporal networks. Our multilayer analysis depends only on the form of the maximization problem and not on the specific quality function that one chooses. We introduce a diagnostic to measure persistence of community structure in a multilayer network partition. We prove several results that describe how the multilayer maximization problem measures a trade-off between static community structure within layers and larger values of persistence across layers. We also discuss some computational issues that the popular "Louvain" heuristic faces with temporal multilayer networks and suggest ways to mitigate them.

Tales from the ArXiv: Systemic Risk in a Galaxy Far, Far Away

Yes, really.

The abstract begins: In this paper we study the financial repercussions of the destruction of two fully armed andoperational moon-sized battle stations ("Death Stars") in a 4-year period and the dissolution ofthe galactic government in Star Wars.

(Tip of the cap to Guido Caldarelli.)

Congratulations to Dr. Martin Gould!

My Ph.D. student Martin Gould passed his dissertation defense (aka "viva") today, and it looks like he might not have to make any corrections at all. Thus far, Martin and I have coauthored a review article on limit order books, and several additional papers are in preparation. Martin's thesis was about limit order books. The Examiners were Doyne Farmer (internal) and Rama Cont (external).

CONGRATULATIONS, Dr. Gould!

"Limit Order Books"

Our review article on limit order books finally has official page numbers and all that, so I am finally going to blog about it. Here are the details of the article.

Title: Limit Order Books

Authors: Martin D. Gould, Mason A. Porter, Stacy Williams, Mark McDonald, Daniel J. Fenn, and Sam D. Howison

Abstract: Limit order books (LOBs) match buyers and sellers in more than half of the world's financial markets. This survey highlights the insights that have emerged from the wealth of empirical and theoretical studies of LOBs. We examine the findings reported by statistical analyses of historical LOB data and discuss how several LOB models provide insight into certain aspects of the mechanism. We also illustrate that many such models poorly resemble real LOBs and that several well-established empirical facts have yet to be reproduced satisfactorily. Finally, we identify several key unresolved questions about LOBs.

"Dynamical Clustering of Exchange Rates"

This paper is another one of those papers that my collaborators and I submitted to a journal a long time ago (in this case, in spring 2009) that has only now appeared in final form. Hence, although it's a new paper in some sense of the word, it certainly doesn't feel new. Here are some details:

Title: Dynamical Clustering of Exchange Rates

Authors: Daniel J. Fenn, Mason A. Porter, Peter J. Mucha, Mark McDonald, Stacy Williams, Neil F. Johnson, and Nick S. Jones

Abstract: We use techniques from network science to study correlations in the foreign exchange (FX) market during the period 1991–2008. We consider an FX market network in which each node represents an exchange rate and each weighted edge represents a time-dependent correlation between the rates. To provide insights into the clustering of the exchange-rate time series, we investigate dynamic communities in the network. We show that there is a relationship between an exchange rate’s functional role within the market and its position within its community and use a node-centric community analysis to track the temporal dynamics of such roles. This reveals which exchange rates dominate the market at particular times and also identifies exchange rates that experienced significant changes in market role. We also use the community dynamics to uncover major structural changes that occurred in the FX market. Our techniques are general and will be similarly useful for investigating correlations in other markets.



Note: I know I have brought this one up before in my blog and it did appear online after dealing with page proofs a couple of months ago, but it now has page numbers, etc.

Dysfunctional Markets: Featuring Principal Component Analysis

Here is a video describing some insights based on research by my former Ph.D. student Dan Fenn. There is some discussion of this work in his Ph.D. thesis and also in this paper.

Somehow, when the media gets ahold of it, a "PCA" turns into a kind of "Risk On, Risk Off index". (Actually, I think HSBC chose to use that name.)

(Tip of the cap to Stacy Williams of HSBC.)

Temporal Evolution of Financial-Market Correlations

Here's another new paper of mine that just came out.

Title: Temporal Evolution of Financial-Market Correlations

Authors: Daniel J. Fenn, Mason A. Porter, Stacy Williams, Mark McDonald, Neil F. Johnson, Nick S. Jones

Abstract: We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes.We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider.We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007–2008 credit and liquidity crisis.