Author: Barry

Lurking in the Shadows: Testing For Shadow Price Differences with Applications in Economic Regulation

This project (in collaboration with Timo Kuousmanen(School of Business, Aalto University,Helsinki, Finland) and Ronan Gallagher(Business School, The University of Edinburgh, United Kingdom))  introduces a novel approach for statistically testing group differences in shadow price estimates produced by non parametric efficiency models.  The test can be applied across groups being assessed using a common efficient frontier or across groups assessed using different efficient frontiers. We demonstrate empirical use cases of each by analysing the impact of regulatory imposition in European banking. Preliminary results suggests significant unintended consequences of supranational regulation in terms of bank credit risk.

Fiscal Policy Change and Corporate Strategy: Evidence from Northern Ireland

On the 18th November 2016, the devolved assembly at Stormont brokered a ‘Fresh Start’ agreement, with a resolution to the well-publicised welfare reform issue in Northern Ireland (NI).  The published document[1] also paved the way for the devolution of corporation tax power, with the legislation[2]  allow NI to set its own corporation tax levels. From April 2018, the rate will drop to 12.5%.

Academic literature suggests reduction in corporation tax has direct and indirect effects.  Directly, a cut in corporation tax would reduce tax receipts.  Indirectly there are three main effects.  Firstly, there is the cost of profit shifting by GB companies to NI.  Secondly, corporation tax receipts would be boosted by companies relocating their profits to NI. Thirdly, NI based sole traders and partnership could now choose to incorporate and pay lower taxes.  A recent HMRC consultation paper has considered all of these effects and several second-round tax effects due to higher tax earning from greater investment.[3]

Reduction in corporation tax is a competitive strategy to attract inward investment. This means the magnitude of the impact of corporation tax reduction in NI should be considered relative to the rates of GB and ROI.  Recently, to reduce the impact of closure to tax loopholes for international firms, ROI has announced a rate of 6.25% for profits associated with R&D.  Furthermore, the conservative government have aggressive perused a corporation tax reduction policy in GB and has announced a fall to 17% by 2020.  Competition is thus a key part to the impact of corporation tax reduction.

This research project seeks to understand these effects and their strategic implications for NI business community. Using a before and after survey analysis of the business attitudes to the corporation tax reduction, the project seeks to provide new corporate governance and strategy insights to fiscal policy reform.

[1] Northern Ireland Assembly 2015, “A Fresh Start: the Stormont Agreement and Implementation Plan”- http://www.cain.ulster.ac.uk/events/peace/stormont-agreement/Stormont_Agreement_2015-11-17.pdf

[2] Corporation Tax (Northern Ireland) Act 2015 (http://services.parliament.uk/bills/2014- 15/corporationtaxnorthernireland.html)

[3] HM Treasury, Rebalancing the Northern Ireland Economy.(https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/81554/rebalanci ng_the_northern_ireland_economy_consultation.pdf)

Pat Quinn ‘Mark 1’ Bodhrans

Every since Pat Quinn(my Da) made me a ‘wee’ Bodhran,  I have ditched my more expensive purchases in preference for these more leaner meaner versions.  Personally, I prefer a slightly smaller bodhran, (12” x 6”) and these are perfect as they pack the same ‘boom’ that a larger drum provide at a fraction of the space.  He made a few more and started selling them and the Pat Quinn Bodhran series was born.

There are a few of the first generation ones left (see slide show below) retailing at £175, and there will be a new and improved second generation coming soon.  If you are curious about how they sound pop down to the Duke of York any Sunday and the venerable Noel Maguire will be rattling away (if he gets bored of his mandalo-singing exploits).

The basic frame is laminated from aircraft quality Birch plywood, clad with exotic hardwood veneers. Pat manufactures the tuners himself, designed for ease of access and adjustment. The skins used are best- quality goat skins used for Lambeg drums. 5-point tuning systems.

Contact Details

Phone:- UK— 028 79632342 or MOB 0287851189103

ROI— 0044 2879632342 or 048 79632342 MOB. 00447851189103

E-mail[email protected]

Address—53, Meeting Street, MAGHERAFELT, CO Derry, BT45 6BW.

Liquidity risk and political instability in Northern Ireland banking

The banking industry in Northern Ireland (NI) mirrors the rest of the UK in its concentrated nature, with ten high street banks (Bank of Ireland, Danske Bank, First Trust Bank, Ulster Bank (RBS group), Barclays, Lloyds Banking Group, HSBC, Santander UK, Clydesdale and Nationwide Building Society) and a handful of banking businesses owned by retail groups (Co-operative bank, Sainsbury’s Bank, Tesco Bank and Post Office Money) who largely operating in the unsecured short term loans market1. Bank balance sheets in NI are facing some significant challenges as a result of the recent political instability. The EU exit and the assembly’s collapse (and subsequent polarised snap election results) due the mismanagement of the ‘ash for cash’ green energy scheme (RHI) 2 places real pressures on both business and consumer lending. The RHI scandal is likely to put pressure on a depleted small to medium enterprise (SME) lending portfolio adding further financial woes to the farming industry 3, while the economic fallout of an EU exit puts pressure on debt-fuelled consumer spending.

One way to understand these pressures better is to assess the liquidity risk of NI banks. Specifically, figure 1 presents an aggregate look at the funding liquidity ratio (borrowings a a proportion of deposits4) for individuals and a selection of SME business categories over the period 2013 Q3 to 2016 Q35.

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Over this period, lending to individuals averaged around 35% of the total loan book. Figure 2 presents the share of total SME lending by business category. The SME business categories investigated in figure 1 capture over 75% of SME loan book share and can be considered the biggest liquidity risk management. Finally, for comparison, figure 1 also includes a sector average.

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The calculated ratio approximates a bank’s liquidity position, with too high a value suggesting that a bank will not have enough funds available to absorb unforeseen funding shocks, while too low a value suggesting profitability issues due to a bank not earning as much as it could from its liquid assets. The data is sourced from the British Banking Association and captures over 95% of the banking business in Northern Ireland.

Some interesting points emerge. The large difference between quarterly SME (orange, light green, and dark green dots) and consumer lending (purple dots) ratios illustrates the stark differences in bank lending practices. SME business lending is secured against a combination of collateral, government backed income and EU subsidies. For example, EU and government backed income is purported to account for some 87% of farming income in NI 6. The SME sector has seen a reduction in the average liquidity ratio over the period, from 138% in 2013 Q3 to 82% in 2016 Q3, which is largely due to a depletion of the loan book size. This depletion has been driven by a deleveraging process in the real estate and construction industries7 due to large non-performing loan write offs and the sale of debt portfolios to investment companies such as New York investment fund Cerberus.

In 2016 Q3, liquidity risk in construction has now fallen below 100% while the real estate business remain well above the sector average at 181%. Agricultural business, while capturing a smaller portion of the SME lending stock (8% average for period), has remained a high liquid risk, well above the average liquidity ratio in every quarter.

This high liquidity risk position of agri-SME lending can be partly explained by the heavily subsidised nature of the industry. That said, a financial crisis in farming is an issue that has been raised a number of years ago when the big four banks were called to Stormont to give evidence8. A key concern for this SME category is the ‘ash for cash’ government backed scheme that has likely been used as lending collateral. In 2013, assurances by the finance department to the banks that the 12% rates of return on these scheme will be ‘grandfathered’ providing certainty regardless of future reviews, likely means that deleveraging in this sectors has been less of a concern to the banks over this period.9. With these guarantees now likely to be under review in a newly formed Assembly the high liquidity risk of agri-SME lending may be ossified by increased default risk if promised guarantees are now in doubt.

From a liquidity perspective, lending to individuals is increasingly well covered by more deposits, with the ratio falling from 73% in 2013 Q3 to 64% in 2016 Q3. That said, this may raise some profitability concerns as individual lending makes up a 35% of the bank lending in NI. GB and NI consumers have been largely unaffected by Brexit-induced economic slowdown or inflation pressure as UK consumer credit grew more than 11% in the year to December, an increase not seen since 2005 according to the Bank of England figures.

Banks in Northern Ireland, as in the rest of the UK, should be weary of such debt-fueled consumption as unsecured personal debt has historically made up 90% of the household loan losses in the UK. Mark Carney has cautioned on this type of debt-driven demand becoming more sensitive to income and employment levels. Another key concern is that UK households are saving at record low levels with saving rates as a proportion of disposable income dropping to 5% (20 year low). While employment levels are at their highest since records began in Northern Ireland, 70% according to the Department of the Economy, the real wage rate in NI has been falling steadily since 200910.

In summary, political uncertainty has some clear liquidity risk management challenges for NI bank balance sheets. While a large deleveraging process in construction and real estate has significantly lowered liquidity risk in SME lending this is likely to squeeze profitability in these areas. More importantly, lending to the farming industry will require careful risk recalibration as RHI scheme guarantees may prove illusory after a review. Perhaps more encouraging is the well covered individual lending portfolio which is 35% of the total loan book. This low liquidity risk may also be a challenge for bank profitability as they struggle to on-lending some of these highly liquid deposits assets.

  1. Northern Ireland also has a well established credit union sector which, while more developed than its UK counterparts, operate mainly in the short term unsecured lending market
  2. 2017 “Green energy scheme fuels Northern Ireland crisis” FT
  3. In 2013, the largest four banks were called to the assembly to provide information and opinion on a perceived financial crisis in NI farming. For details of these briefings see (2013) Financial Crisis in Farming Northern Ireland Assembly Briefings with Banks 2013. Danske Bank , Bank of Ireland, Ulster Bank, First Trust Bank.
  4. Borrowings are defined as total loans and overdrafts while deposits includes all current account balances
  5. This is the complete period for which data is available
  6. (2017) Northern Ireland shows the risks of Brexit FT
  7. Over the three year period lending stocks has halved in real estate and fallen by three quarters in the construction industry
  8. (2013) Financial Crisis in Farming Northern Ireland Assembly Briefings with Banks 2013. Danske Bank , Bank of Ireland, Ulster Bank, First Trust Bank.
  9. http://www.bbc.co.uk/news/uk-northern-ireland-38466327
  10. http://www.nicva.org/article/what-ashe-tells-us-about-real-wages-northern-ireland

Unintended Systemic Risk Implication of Regulatory Compliance

The recent financial crisis painfully demonstrated the risk large interconnected banks with complex activities pose to the financial system.  Pre-crisis, microprudential regulation was the norm, where a ‘bottom-up’ approach aimed to protect the consumer from exogenous risk. What is clear now is that a financial system as a whole behaves very differently from its individual component institutions and financial instability stems from endogeneous (within the systemic) risks.  This has meant policymaker’s in developed economies have initiated a myriad of international macroprudential regulatory programs which aim to monitoring and ultimately preventing systemic risk.

The finance literature provides us with some explanations as to why we observe bank herding behaviour which may have systemic risk implications.This literature assesses the incentives through which banks can be correlated with each other.  Specifically, through investment choices (Acharya & Yorulmazer 2007), diversification (Nijskens & Wagner, 2011;Allen et al., 2012), interbank insurance (Kahn & Santos, 2010), or through herding on the liabilities side (Segura & Suarez, 2011; Stein, 2012;Farhi & Tirole, 2012; Horvath & Wagner, 2017).

The economic literature provides an interesting strand on the rationale for the unintended consequences of regulation (Averch & Johnson, 1962; Merton, 1936; Stigler, 1971). These unintended consequences can stem from many sources: human error; the inability to model complex interactions amongst regulated actors; the ‘imperious immediacy’ of a single regulatory interest to the detriment of all others. Our initial findings lend weight to Merton’s (1936) imperious immediacy conjecture.

This project extends recent studies on bank performance, regulations and bank’s business models 1 2 by moving from performance to financial system stability and consider the systemic effects of the regulatory compliance and business model diversity.  We will consider financial stability using a myriad of systemic risk measures 3 which capture a financial institution’s contribution to systemic risk and its exposure to systemic distress.  This will then be used to answer the following questions:

  • Is regulatory compliance providing stability in financial systems?
  • Are supervisors actions engendering financial stability?
  • Are what supervisors are asking banks to do having unintended consequences for financial stability?
  • How sensitive is systemic risk to different forms of financial regulation?
  • Does diversity in banking business models matter for financial stability?
  1. Ayadi, R. et al., 2016. Does Basel compliance matter for bank performance? Journal of Financial Stability, 23, pp.15–32.
  2. Ayadi, R. & Pieter De Groen, W., 2016. Banking Business Models Monitor 2015 EUROPE, Centre for European Policy Studies. Available at: https://www.ceps.eu/system/files/Banking-Business-Models-Monitor-Europe-2015.pdf.
  3. We will use a number of the cross-sectional systemic risk measures described  in Bisias, D. et al., 2012. A survey of systemic risk analytics, Office of Financial Research, US Treasury Department.

Me, HEC and Research 2B

dsc_0532

One of the many good things about working as a Finance lecturer at Queen’s Management School is the opportunity to research overseas.  As a senior research associate of the International Research Centre for Cooperative Finance (IRCCF) at HEC Montreal,  I have the pleasure of visiting this eclectic bilingual city each year. Over the academic year I have two visits as part of my sabbatical leave.  The visits are centred around three exciting projects:

  1. Banking Business Model (BBM) diversity and financial sustainability.
  2. Cooperative Traits of Mergers.
  3. Systemic Risk and Basel Regulatory Compliance (in collaboration with the IMF and Cass Business School)

This research hopes to provide evidence that enlightens the following research puzzles:

  1. What business model features have engendered resilience in the Canadian financial system ?
  2. Do credit union mergers enshrine membership benefits?
  3. How does compliance with core supervisory standards set by the Basel Banking Supervision Committee  affect systemic risk in developed economies?

Projects 1 and 2 are Canada focused while project 3 takes a global approach.  Each of these projects pose very different quantitative challenges which I relish as an card-carrying empiricist.

In project 1 we are using a data clustering approach to identify distinct business models based on an institution’s funding and activities.  This model-free approach reveals the BBM diversity in the Canadian sector over the period 2010-2015.  Following the seminal work on BBM global monitors by my co-author Professor Rym Ayadi  (Director of the IRCCF) in Ayadi et al (2011 2014, 2015 and 2016)1 this exercise will illustrate the unique architecture of the Canadian financial services industry and shed light on factors that promote resilience to globally systemic banking problems.

Project 2 uses a proprietary data set from Deposit Insurance Corporation of Ontario (DICO) to investigate 20 years of consolidation in this region’s credit unions. The analysis will use a flexible model which captures the uniquely cooperative objective of membership benefit maximisation. The project will empirically expose the cooperative traits of mergers/amalgamations in credit unions and hopes to reveal the nature of the membership value of such activity.

Finally, project 3 is a global exercise which uses a number of quantitative measures to capture systemic risk of a financial institution and identify to what extend regulatory compliance can mitigate this risk.  This is a follow on piece of work from Ayadi et al (2016) 2. We have used a large slice of data science to compile a unique sample representing global banking and its regulatory infrastructure.  Our key variable measures the compliance of a financial system with the principles of regulatory best practice proposed by the Basel Committee for Banking supervision.

In short I have my work cut out!  Watch this space for some interesting preliminary results from these projects.

  1. Ayadi, R., Arbak, E. & Pieter De Groen, W., 2011. Business Models in European Banking: A Pre-and Post-Crisis Screening. Centre for European Policy Studies.

    Ayadi, R. & DeGreon, W.P., 2014. Banking Business Models Monitor 2014 Europe. Centre for European Policy Studies.

    Ayadi, R., Arbak, M. & GreonWP, D., 2015. Regulations of European Banks and Business Models: Towards a new paradigm.Centre for European Policy Studies.

    Ayadi, R. & De Groen, W.P., 2016. Bank Business Model Monitor for Europe 2015. International Research Center for Cooperative Finance.

  2. Ayadi, R., Naceur, S. B., Casu, B. & Quinn, B. 2016, Does Basel Compliance Matter for Bank Performance?,  Journal of Financial Stability. 23, p. 15-32

Stock market rally after Referendum? Evidence from the Options Market.

FTSE 100 Index options are a excellent tool for investors to benefit from/protect against large scale UK equity market moves.  Specifically, investors can use puts to protect against a decline in stocks after the OUT vote, and calls to reap the benefits of a rally after a REMAIN vote.

I investigate the extend of these two investment plays by assessing the implied volatility skew of the FTSE 100 Index.  Specifically, I assess the difference between 5% out of the money puts and calls for the at one month expiry.  Below I have graphed this spread for the past 2 years.

ImpliedVol1

We can see that the spread between bearish and bullish bets in the options market is at its highest in 2 years.  After a market fall, bearish purchases of puts are frequently met by the buying of calls on speculation that the negative sentiment is overdone.  We do not see this here.  The options markets are suggesting that investors are protecting themselves against a potential drop but few investors are trying to benefit from the potential for a rally.

So perhaps a rally in the FTSE 100 stocks will prove illusory after the referendum.

Expected Impact of Brexit on Currency Markets

This short post is inspired by some friends asking whether they should cash in their euros stash ahead of the Brexit result  today.  As a finance academic and a former currency market participant I think the best answer here is to ask

“what do the currency markets think ?”

More specifically, what is the expected impact of Brexit on the chunnel rate (the EUR/GBP currency pair) according to the FX option markets.  We can use expectation theory to provide us with an estimate of the impact based on the probability of Brexit  and the expected % change in the chunnel rate for either scenario.  Mathematically:

Bloomberg has some excellent estimates to fill in the blanks on the right-hand side of the above equation.  Specifically, they have used the EUR/GBP and USD/GBP FX options markets to produced both an implied probability of Brexit and its  % impact on EUR/GBP rate.  Table 1 shows these estimates averaged for the last 30 days1.  I have also included the implied probabilities of Brexit from a number of other sources as a comparison.

Source of ProbabilitiesProbability Brexit Probability Remain Expected Move in EUR/GBP | Brexit Expected Move in EUR/GBP | RemainExpect Impact of Brexit
Number Cruncher37.0262.986.82-6.82-1.77
Oddschecker26.5373.476.82-6.82-3.20
BBG FX Options20.2979.716.82-6.82-4.16
Bing Predicts41.558.56.82-6.82-1.16
All numbers are in percentages. I have made the assumption that there is a symmetrical impact between for the EUR/GBP rate. Columns 2-5 are 30 day averages.

 

From the last column in Table 1 in all scenarios the expected impact is negative for my mate’s stash of Euro.  In conclusion, cash in the stash before they are devalued!

UPDATE POST BREXIT:

Clearly my symmetrical assumption on Brexit impact was misguided and if I had considered my other post, this lack of symmetry would be obvious.  If i had assumed a no change assumption for a remain vote the calculations would have looked much different:

Source of ProbabilitiesProbability Brexit (%)Probability Remain (%)Expected Move in EUR/GBP | BrexitExpected Move in EUR/GBP | RemainExpect Impact of Brexit
Number Cruncher37.0262.986.820.002.53
Oddschecker26.5373.476.820.001.81
BBG FX Options19.4980.516.820.001.33
Bing Predicts41.5058.506.820.002.83
  1. Figure 1 and Figure 2 are time series graphs of underlying indices that these averages are based on:

    Figure 1: BRXBEUR Index (Bloomberg Expected Move in EUR/GBP in the Event of BREXIT)

    BRXBEUR Index (Bloomberg Expecte 2016-06-23 12-58-06

    Figure 2: BRXBLEAV Index (Bloomberg Probability of  BREXIT from the FX Options Markets)

    BRXBLEAV Index (Bloomberg Probab 2016-06-23 12-59-20

Credit Unions and Financial Stability

CUConcept

A credit union is one of the purest forms of cooperative banking, striving to balance both economic and social goals for the benefit of its membership.

Credit unions are a prevalent part of  society and have long been seen as a stable and risk-averse form of banking. In Canada, credit unions compete directly for market share with shareholder-owned banks, and dominate in some regions. Overall this heterogeneous banking system has been perceived to be relatively stable, especially since the recent financial crisis.

This research project aims to provide key insights into the dynamic contribution financial cooperatives make to the overall stability of a banking system. Focusing on the Canadian banking system, the analysis aims to assess key credit union characteristics that influence stability in a banking sector in periods of crisis and calm. The projects’ empirical design has four paradigms; business model heterogeneity; structural performance; survival, and viability.
This research project is part of my work as a Senior research associate of the International Research Centre for Cooperative Finance in HEC Montreal.