Publications

Counterparty choice in the UK credit default swap market: An empirical matching approach with Jun Sung Kim, Bonsoo Koo and Zijun Liu (2020) - Economic Modelling

Central counterparty auction design with Xin Li and Daniel Marszalec (2020) - Journal of Financial Market Infrastructures

Media/Citation: Risk.net

Multiplex network analysis of the UK OTC derivatives market with Marco Bardoscia and Ginestra Bianconi (2019) - International Journal of Finance & Economics

Media/Citation: Centralbanking.com

Systemic Illiquidity in the Interbank Network with Sam Langfield, Zijun Liu and Tomohiro Ota (2019) - Quantitative Finance

The impact of de-tiering in the UK's large value payment system with Evangelos Benos and Pedro Gurrola-Perez (2017) - Journal of Financial Market Infrastructures

The small bank failures of the early 1990s: another story of boom and bust with Kushal Balluck, Artus Galiay, and Glenn Hoggarth (2016) - Bank of England Quarterly Bulletin

Working Papers

The COVID-19 Auction Premium with Maria Flora and Roberto Reno' (2021)

Abstract: We uncover an additional channel by which a pandemic is costly for taxpayers, namely the surge of the bond auction premium. Using futures and cash data on Italian bonds, we show that the auction premium is correlated with bond price volatility which, in turn, was associated with news about COVID-19 infections and the ECB monetary policy response to the first wave of the pandemic. We quantify the issuance cost for the Italian Treasury at the volatility peak to be 136 bps of the auction size. Our results indicate that subsequent monetary policy measures, implemented since 18 March 2020, effectively reduced volatility, and consequently the size of the premium, during the second wave of the pandemic.

Modelling fire sale contagion across banks and non-banks with Fabio Caccioli and Amanah Ramadiah (2020)

Media/Citation: Centralbanking.com

Abstract: We study the impact of common asset holdings across different financial sectors on financial stability. In particular, we model indirect contagion via fire sales across UK banks and non-banks. Fire sales are triggered by different responses to a financial shock: banks and non unit-linked insurers are subject to regulatory constraints, while funds and unit-linked insurers are obliged to meet investor redemptions. We use our model to conduct a systemic stress simulation under different initial shock scenarios and institutions' selling strategies. We find that performing a stress simulation that does not account for common asset holdings across multiple sectors can severely underestimate the fire sale losses in the financial system.

Simulating liquidity stress in the derivatives market with Marco Bardoscia, Nicholas Vause and Michael Yoganayagam (2019)

Applications: The FPC's assessment of the risks from leverage in the non-bank financial system - Financial Stability Report November 2018 and Building the resilience of market-based finance - Financial Stability Report August 2020

Abstract: We investigate whether margin calls on derivative counterparties could exceed their available liquid assets and, by preventing immediate payment of the calls, spread such liquidity shortfalls through the market. Using trade repository data on derivative portfolios, we simulate variation margin calls in a stress scenario and compare these with the liquid-asset buffers of the institutions facing the calls. Where buffers are insufficient we assume institutions borrow additional liquidity to cover the shortfalls, but only at the last moment when payment is due. Such delays can force recipients to borrow more than otherwise, and so liquidity shortfalls can grow in aggregate as they spread through the network. However, we find an aggregate liquidity shortfall equivalent to only a small fraction of average daily cash borrowing in international repo markets. Moreover, we find that only a small part of this aggregate shortfall could be avoided if payments were co‑ordinated centrally.

Full Payment Algorithm with Marco Bardoscia, Nicholas Vause and Michael Yoganayagam (2019)

Abstract: Clearing payments between firms are usually computed under the assumption that firms that cannot fully meet their obligations exhaust their cash resources to make partial payments. In practice, however, firms might wait and see whether they receive payments that would help them to meet their obligations in full before making any payments themselves, thereby avoiding partial payments. In this paper, we model this situation by introducing the Full Payment Algorithm (FPA). We fully characterize the steady state of the FPA, and we prove necessary and sufficient conditions under which it is equivalent to the widely used Eisenberg and Noe model.

The impact of the leverage ratio on client clearing with Jonathan Acosta-Smith and Francesc Rodriguez-Tous (2018)

Media/Citation: ISDA and BIS Report

Price awarded at the 27th finance forum: Best paper on Regulation

Abstract: As part of the post-crisis regulatory reform, many interest-rate derivative transactions are required to be centrally cleared. Nevertheless, the treatment of this type of transaction under the leverage ratio (LR) requirement does not allow for the use of initial margin to reduce the exposure, thereby increasing capital costs. As a result, LR affected clearing member banks may be more reluctant to provide central clearing services to clients given this additional cost. This in turn can prevent some real economy firms from hedging their risks. We analyse whether this is the case by exploiting detailed confidential transaction and portfolio level data as well as the introduction and posterior tightening of the LR in the UK in a diff-in-diff framework. Our results suggest that the LR had a disincentivising effect on client clearing, both in terms of daily transactions as well as the number of clients, but this impact seems to be driven by a reduced willingness to take on new clients.

Optimization under Model Uncertainty Job Market Paper (2014)

Abstract: This study proposes a novel methodology to deal with model uncertainty in forecasting stock returns. My main interest here is to overcome the tendency of Bayesian Model Averaging to give all of the weight to a single model. A potential solution of this problem is to capture the nature of the underlying data generating process by sampling from the space of possible combinations. After presenting the methodology in detail, I show that my approach increases the accuracy of out-of-sample forecasting. Moreover, I investigate the impact of improved forecasting on portfolio performances.

Genetic Algorithms on Portfolio Optimization (2012)

Abstract: Over the years researchers have attempted to develop asset allocation models to aid decision making in portfolio selection, based upon a statistical or a heuristic approach. Optimization of asset allocation is often complex and nonlinear with many local optima. For this reason, searching the global solution by analytical methods is computationally expensive and ineffective. In comparison with other local search algorithms, Genetic Algorithms (GA) assures a higher chance of reaching a global optimum by starting with multiple random search points and considering several candidate solutions simultaneously.

In Progress

Margin procyclicality with Evangelos Benos and Angelo Ranaldo (2019)

Equity Swaps: Trends and Regulatory Constraints with Christian Lundblad and Matt Roberts-Sklar (2019)