Michalis Vasios

An Investigation into the Procyclicality of Risk-Based Initial Margin Models

The initial margin requirements for a portfolio of derivatives are typically calculated using a risk model. Common risk models are procyclical: margin requirements for the same portfolio are higher in times of market stress and lower in calm markets. This procyclicality can cause liquidity stress whereby parties posting margin have to find additional liquid assets, often at just the times when it is most difficult for them to do so. Hence regulation has recognised that, subject to being adequately risk sensitive, margin models should not be ‘overly’ procyclical. There is, however, no standard definition of procyclicality. This paper proposes two types of quantitative measure of procyclicality: one that examines margin variation across the cycle and one that focuses on short-term margin increases. It then studies, using historical and simulated data, various margin models with regard to both their risk sensitivity and the proposed procyclicality measures. It finds that models which pass common risk sensitivity tests can have very different levels of procyclicality. The paper recommends that CCPs and major dealers should disclose the procyclicality properties of their margin models, perhaps by reporting the proposed procyclicality measures. This would help derivatives users to anticipate potential margin calls and ensure they have adequate holdings of or access to liquid assets.

Centralized trading, transparency and interest rate swap market liquidity: evidence from the implementation of the Dodd-Frank Act

We use transactional data from the USD and EUR segments of the plain vanilla interest rate swap market to assess the impact of the Dodd-Frank mandate that US persons must trade certain swap contracts on Swap Execution Facilities (SEFs). We find that, as a result of SEF trading, activity increases and liquidity improves across the swap market, with the improvement being largest for USD mandated contracts which are most affected by the mandate. The associated reduction in execution costs is economically significant. For example, execution costs in USD mandated contracts, where SEF penetration is highest, drop, for market end-users alone, by

Sell-Side Analysts' Career Concerns during Banking Stresses

3 million–

Profiting from Mimicking Strategies in Non-Anonymous Markets

4 million daily relative to EUR mandated contracts and in total by about

Identifying contagion in a banking network

7 million–

A Least Squares Regression Realised Covariation Estimation under MMS Noise and Non-Synchronous Trading

13 million daily. We also find that inter-dealer activity drops concurrently with the improvement in liquidity suggesting that execution costs may have fallen because dealer intermediation chains became shorter. Finally, we document that the Dodd-Frank mandate caused the activity of the EUR segment of the market to geographically fragment. However, this does not appear to have compromised liquidity. Overall, our results suggest that the improvements in transparency brought about by the Dodd-Frank trading mandate have substantially improved interest rate swap market liquidity.

Gauging Market Dynamics Using Trade Repository Data: The Case of the Swiss Franc De-Pegging

We propose a new approach to examine sell-side analysts’ career concerns by relating their forecast boldness to their employers’ news flows. Specifically, we use banking sector news to proxy for the severity of career concerns. Analysts follow more closely the consensus forecast when the prospects of the banking sector are negative (and vice versa). The effect is both economically and statistically significant after controlling for various firm, analyst, brokerage house, and forecasting characteristics, as well as sector and economy wide effects. The more established analysts, in terms of reputation and experience, are generally unaffected by banking sector news. In contrast, their less established peers tend to cluster their forecasts near the consensus after a sequence of negative news flows for banks. Collectively, our results support the notion that during banking stresses when job security is low analysts’ tendency to imitate others increases.

A Comparative Analysis of Tools to Limit the Procyclicality of Initial Margin Requirements

We explore the information content of counterparty identities and how their disclosure can be exploited by other investors in a post-trade transparent market. Using data from the Helsinki Stock Exchange, we form dynamic mean-variance strategies with daily rebalancing which condition on the net flow of individual brokers. We find that investors can benefit greatly, up to 36% in annualized risk adjusted returns, from knowing who has been trading. We demonstrate a link between the information content of broker order flow and the sophistication of their clients. Brokers who have clients that trade with a momentum style or who are predominantly institutions or foreign investors have much more informative flow than do others. In the Finnish setting, this means that brokers with large market share have uninformative flows.

Financial Stability Paper No 29: An investigation into the procyclicality of risk-based initial margin models

We present the first micro-level evidence of the transmission of shocks through financial networks. Using the network of credit default swap (CDS) transactions between banks, we identify bank CDS returns attributable to counterparty losses. A bank’s own CDS spread increases whenever counterparties from whom it has purchased default protection themselves experience losses. We find no such effect from losses of non-counterparties, nor from counterparties to whom the bank has sold protection. The effect on bank CDS returns through this counterparty loss channel is large relative to the direct effect on a bank’s CDS returns from its own trading losses.

Can investors benefit from market transparency? : an asset allocation perspective

We propose a least squares regression framework for the estimation of the realized covariation matrix using high frequency data. The new estimator is robust to market microstructure noise (MMS) and non-synchronous trading. Comprehensive simulation and empirical analysis show that our estimator performs as well as a set of popular estimators in the literature. More importantly, our framework allows for the unique identification of MMS noise moments. We find that these noise moments are related to measures of liquidity and contain predictive information that helps to significantly improve out-of-sample asset allocation.