The Business Cycle and Basel III
Saturday, 16 October 2010 | 
John A Morrison
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I have been reading an interesting synopsis of Basel 3 this week. I noted this analytic of Basle III in relation to what asymptotix has been publishing about Ben Bernanke yesterday;
Monetary Policy Objectives and Tools in a Low-Inflation Environment
The quotation which really hammers the Basel III point home (and I am sorry if I am spoiling your corn-flakes) goes like this (it is in relation to the Capital Conservation Buffer of Basel II);-
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"Whilst no capital conservation buffer existed under Basel II, regulatory requirements under Basel III will require banks to retain a capital conservation buffer of 2.5% - as a means of "withstanding future periods of stress." As well as bringing the total common equity requirements to 7%, such a move "reinforces the stronger definition of capital agreed by Governors and Heads of Supervision in July and the higher capital requirements for trading, derivative and securitisation activities to be introduced at the end of 2011."
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"The capital conservation buffer is to "sit on top of Tier One capital."
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"Any bank whose capital ratio fails to retain the stipulated limit (which is in excess of the buffer), faces the threat of "restrictions" from supervisors on payouts which include dividends, share buy backs and bonuses."
The argument presented proceeds in its detail to get even more scary;-
"The purpose of the counter cyclical buffer is considered to be the achievement of "the broader macro prudential goal of protecting the banking sector from periods of excess aggregate credit growth." Further, the counter cyclical buffer is aimed at compelling banks to commence with build ups of such extra buffers ....... As is the case with the capital conservation buffer, counter cyclical buffers did not exist under Basel II. Basel III imposes a requirement of a counter cyclical buffer within a range of 0% and 2.5% of common equity or "other fully loss absorbing capital will be implemented according to national circumstances."
I better reveal my source article;
Basel III and responding to the recent Financial Crisis: progress made by the Basel Committee in relation to the need for increased bank capital and increased quality of loss absorbing capital
by Ojo, Marianne, Center for European Law and Politics, University of Bremen, Oxford Brookes University. 
Every cent, i.e. every 0.000000000001% of common equity (note "common equity" none of your funny hybrids or CoCos are allowed anymore under B3) is critical to the financial institution but more critically its to the shareholders this matters.
Therefore precision estimation of the institutional exposure to that buffering is fundamental. Thus every quantitative tool you have in your bag of tricks is going to have to be deployed to get that right. It may seem utterly ridiculous but the management in charge of financial institutions is going to have to get real about estimating the business cycle and its implications for their risk appetite and risk profile. You cant do this in an excel spreadsheet.
Asymptotix has been indicating gently over the years how this challenge might be addressed should it come to pass. Well now that it has, here is a synopsis of what we have been saying.
Back in June 2009, asymptotix said
"it looks as though the "Dynamic Provisioning" (DP) approach or technique to the computation of Bank Capital in the context of Banking Supervision is gaining the ascendancy in the UK and Europe as a solution to ensuring banks hold sufficient reserves such that a credit crisis never occurs again. In reinforcing the tripartite arrangements to Banking Supervision and Regulation in the UK, the Chancellor appears to be favoring this approach which developed in Spain and it has to be said was successful (relatively). One challenge that Dynamic Provisioning gets around is the issue of procyclicality of capital buffers, perceived to be a problem with Basel II. In effect what DP does is to integrate Basel II Pillar One and Pillar Two by making total capital explicit and transparent over the business cycle. DP requires however an econometric model of the business cycle to drive the capital estimates predicated on outlooks for default (as a function of the business cycle). This is an interesting approach since finally an accounting standard is being putatively harnessed to an econometric technique."
The reference is Dynamic Provisioning & Bank Capital Integrity
Other references from asymptotix, related to this argument, which you may find useful are;-
The Term Macroprudential - Origins And Evolution
Ministers Of Finance Support Introduction Of Dynamic Provisioning
Procyclicality And Financial Regulation
Further asymptotix has made some technical references to the methodology or the set of techniques which you might consider should you wish to embark on a programme to implement this kind of approach. In an nutshell the methodological perspective is to integrate a Dynamic Stochastic General Equilibrium Model of the Business Cycle to a Factor Model of your financial institution's Risk Appetite and Financial Ratios and thus provide modelling of risk capital demand over the cycle. Here are our technical references;-
Testing a DSGE Model of the EU Using Indirect Inference
A method to incorporate information from Dynamic Stochastic General Equilibrium (DSGE) models into Dynamic Factor Analysis
Over on my more technical blog on Analytic Bridge I have made reference to several important papers as blueprints of how to do this type of thing which maybe useful.
Evaluating and estimating a DSGE model of the UK
How to traction the European Commission Structural Economic Model of the EU for Stress Testing
Macroeconomic Models of the Euro area at the European Central Bank
DSGE Model-Based Forecasting of Non-modelled Variables
INFINITE-DIMENSIONAL VARS & Factor Models
Finally as a general reference, here is Asymptotix summary, published on the Sunday of the announcement of the Basel III Capital Requirements specifications;-
Group of Governors and Heads of Supervision announces higher global minimum capital standards Basel III
Back in 2008, hanging out on the Charles river myself (where Chairman Ben was at the beginning of this post) I treated myself to this book, I read it as an undergraduate and couldn't possibly have afforded to buy it then but I recommend it to you now, more than ever it is just as relevant today as it was then
Harvard University Press
Modern Business Cycle Theory
Edited by Robert Barro
Thanks to my colleague Peter for stimulating me to articulate some ideas which I have no doubt he has been thinking about.
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Comments
Reciprocal effects should not be ignored
Reciprocal effects should not be ignored
September 22, 2011
Deutsche Bank Research
Robert Barro Takes A Victory Lap
BUSINESS INSIDER MONEY GAME
Basel III, the Banks, and the Economy
Douglas J. Elliott
The Brookings Institution
July 23, 2010
Basel III, the Banks, and the Economy
STRESS TESTING FOR PILLAR 2 Economic (Risk) Capital - How To
STRESS TESTING FOR PILLAR 2
Economic (Risk) Capital - How To - References
This is one of the most popular references on asymptotix; it was developed for Basel II Pillar 2 but it is equally relevant here now. You will notice that on the referred page I apply a number 1 /2 /3 etc to the sections of the web page, these classify the sequential order which I recommend that the references be taken in which accidentally is consistent with the degree of difficulty or complexity of the modelling challenge. Indeed the "Business Cycle" theory stuff here (specifically oriented towards Basel III) could be regarded as Section 5 of the 'How To' References!
Link
Financial Cycles: What? How? When?
Normally I would specify the authors and quote the abstract (standard practice). In this case I need to first 'hat-tip' Cate Long on Twitter ('hat tip' is what the Colonials say when they mean 'thanks for the reference'); then I need to say that I am not going to quote the abstract of this paper, in this case I am going to quote the rather important claim this paper makes about itself in the introduction on page 5; you will find that this paper relies upon and references the core set of methodologies and techniques (basically "business cycle theory") as most of the other references do on this page; first the author/publisher citation, as usual;-
IMF Working Paper
Research Department
Financial Cycles: What? How? When?
Prepared by Stijn Claessens, M. Ayhan Kose and Marco E. Terrones
April 2011
here
Quotation from the introduction
We extend the literature on financial cycles in a number of dimensions. First, we provide the first detailed, cross-country empirical analysis documenting the main features of financial cycles and the interactions across different cyclical phases using a large sample. Second, in parallel with the business cycle literature, we use a well-established and reproducible methodology for the dating of financial downturns and upturns. Furthermore, since we employ quarterly data, rather than the annual data typically used in other cross-country studies, we can better identify and document the properties of financial cycles. Third, taking advantage of our large data set and using regression models, we study various factors associated with the duration and amplitude of financial cycles.
Banks and the Business Cycle
Banks, Oligopolistic Competition, and the Business Cycle:A New Financial Accelerator Approach
Alexander Totzek Department of Economics, Christian-Albrechts-University of Kiel
Abstract
In the last two decades a body of literature highlights the role of financialfrictions for explaining the development of key macroeconomic variables.Moreover, the financial crisis 2007-2009 again sheds light on theimportance of this topic. In this paper, we contribute to the literatureby simultaneously explaining two empirical observations. First, mark-upson the loan market react counter-cyclical. Second, the number of banksoperating in the economy significantly co-moves with GDP. Therefore, wedevelop a DSGE model which incorporates an oligopolistic banking sectorwith endogenous bank entry. The resulting model generates significant accelerating effects which are even larger than those obtained in the famous financial accelerator model of Bernanke et al. [Bernanke, B., Gertler, M., Gilchrist, S., 1999. The financial accelerator in a quantitative busi-ness cycle framework. In: Handbook of Macroeconomics. North-Holland, Amsterdam] and performs remarkable well when comparing the generated second moments of real and financial variables with those observed in the data.
here
Factor Modeling in the European Context: The ECB
The present paper focuses on the factor modelling, which has been widely used in macroeconomic forecasting in recent years. Within that approach, there are a number of technical issues that require attention. These include (i) the number of factors to include in a factor augmented forecasting equation, (ii) the speci?cation of the dynamics in the aforementioned equation, (iii) the information from which to extract the factors, including the issue of whether and how to select a subset of the available dataset on which factor analysis will be applied, and (iv) the benefits of combining factor-based forecasts. Of course those technical issues are interrelated and cannot be addressed in isolation. Providing answers to those technical issues, which have not been fully addressed in the literature, is the aim of this paper, with a focus on Euro-area data and issues (iii) and (iv).
Remarkably, we find that the use of as few as one fifth of the original variables yields the best results in terms of forecast accuracy. Interestingly, we can discern a pattern of variables that remain after pre-selection which are common to all the individual countries considered: these include real variables like retail trade, survey variables like consumer and price expectations, international variables like U.S. consumer expectations and financial variables like the price of raw materials.
by Giovanni Caggiano, George Kapetanios, and Vincent Labhard
EUROPEAN CENTRAL BANK
INFINITE DIMENSIONAL VARS & Factor Models
European Central Bank (ECB) Working Paper - INFINITE-DIMENSIONAL VARS & Factor Models
Vector autoregressive models (VARs) provide a flexible framework for the analysis of complex dynamics and interactions that exist between variables in the national and global economy. However, the application of the approach in practice is often limited to a handful of variables which could lead to misleading inference if important variables are omitted merely to accommodate the VAR modelling strategy. Number of parameters to be estimated grows at the quadratic rate with the number of variables, which is limited by the size of typical data sets to no more than 5 to 7. In many empirical applications, this is not satisfactory.
This paper introduces a novel approach for dealing with the curse of dimensionality in the case of large linear dynamic systems. Restrictions on the coeficients of an unrestricted VAR are proposed that are binding only in a limit as the number of endogenous variables tends to in?nity. It is shown that under such restrictions, an infinite-dimensional VAR (or IVAR) can be arbitrarily well characterized by a large number of infinite-dimensional models. The paper also considers IVAR models with dominant individual units and shows that this will lead to a dynamic factor model with the dominant unit acting as the factor. The problems of estimation and inference in a stationary IVAR with unknown number of unobserved common factors are also investigated. A cross section augmented least squares estimator is proposed and its asymptotic distribution is derived. Satisfactory small sample properties are documented by Monte Carlo experiments.
LINK
Stress testing banks'credit risk using mixture vector models
Stress testing banks'credit risk using mixture vector autoregressive models
Prepared by Tom Pak-wing Fong, Research Department, Hong Kong Monetary Authority and Chun-shan Wong, Department of Finance, The Chinese University of Hong Kong
This paper estimates macroeconomic credit risk of banks' loan portfolio based on a class of mixture vector autoregressive models. Such class of models can differentiate distributions of default rates and macroeconomic conditions for different market situations and can capture their dynamics evolving over time, including the feedback effect from an increase in fragility back to the macroeconomy. These extensions can facilitate the evaluation of credit risks of loan portfolio based on different credit loss distributions.
here
Basel Committee Issues Final Revisions to B3
Sullivan & Cromwell
ECB FSR Basel III
European Central Bank (ECB) Financial Stability Review (FSR) Special Feature on Basel III
here
BoE FSR Preserving Financial Stability
A network analysis of global banking:1978-2009
HERE
This is an IMF paper (+CateLong AGAIN) which utilises the same methodology as the BoE paper above. The network analysis gives you the why in each specific case for Banking Supervision it is also the best model we have to define relative systemic importance i.e. to quantify that indicatively. But what I think this bigger population coverage of the IMF paper brings is also an indicative "through a glass darkly" perspective on Friedmans v - velocity of money, that variable forever condemned to be latent - right? If you just look at the diagrams, the visualisations at the back of this paper, you can see I think that The IMF n/work analysis demonstrates that Freidman's multiplier called v (velocity) has @ a guess more than halved (REFINEMENT: Significantly Reduced) globally during the great crisis (since the summer of 07) - I make this analysis on the basis of the data i.e. if interconnected-ness is declining then velocity of money MUST a priori be declining. Of course this has the sad entailment that in a capitalist economy; the definition of 'connectedness' is number of transactions or flow of dollars: does that mean that alienation would be a smartphone doing highjumps and screaming for attention which is not yours ..?
Do Basel III and Unconventional Monetary Policy Work?
Liquidity Stress-Tester: Do Basel III and Unconventional Monetary Policy Work?
Jan Willem van den End
the Economics and Research Division of DeNederlandsche Bank (DNB)
December 2010
Abstract
This paper presents a macro stress-testing model for liquidity risks of banks, incorporating the proposed Basel III liquidity regulation, unconventional monetary policy and credit supply effects. First and second round (feedback) effects of shocks are simulated by a Monte Carlo approach. Banks react according to the Basel III standards, endogenising liquidity risk. The model shows how banks' reactions interact with extended refinancing operations and asset purchases by the central bank. The results indicate that Basel III limits liquidity tail risk, in particular if it leads to a higher quality of liquid asset holdings. The flip side of increased bond holdings is that monetary policy conducted through asset purchases gets more influence on banks relative to refinancing operations.
here
Procyclicality: The Adverse Feedback Loop
"Adverse Feedback Loop in the Bank-Based Financial Systems"
Adam Geršl
DeputyDirector
FinancialStability Department
CzechNationalBank
link
Impact of Regulatory Reforms (B3)
(The) Impact of Regulatory Reforms on Large and Complex Financial Institutions
IMF
B3: Counterparty Credit Risk and the Leverage Ratio
Linklaters
B3 - A Critical Assessment
Rafael Repullo CEMFI
MacroPrudential: The Concept
asymptotix
Basel III and Its Implications: A Closer Look
Sandler O'Neil + Partners
Basel III, the Banks and the Economy
Brookings
Integrated Models of Risk Capital (Basel III)
asymptotix
THINKING BEYOND BASEL III
OECD Journal: Financial Market Trends
Macroeconomic factors and micro-level bank risk
Deutsche Bundesbank
Claudia M. Buch (University of Tübingen, CESifo and IAW)
Sandra Eickmeier (Deutsche Bundesbank)
Esteban Prieto (University of Tübingen)
Abstract
The interplay between banks and the macroeconomy is of key importance for financial and economic stability. We analyze this link using a factor-augmented vector autoregressive model (FAVAR) which extends a standard VAR for the U.S. macroeconomy. The model includes GDP growth, inflation, the Federal Funds rate, house price inflation, and a set of factors summarizing conditions in the banking sector. We use data of more than 1,500 commercial banks from the U.S. call reports to address the following questions. How are macroeconomic shocks transmitted to bank risk and other banking variables? What are the sources of bank heterogeneity, and what explains differences in individual banks' responses to macroeconomic shocks?
Our paper has two main findings:
(i) Average bank risk declines, and average bank lending increases following expansionary shocks.
(ii) The heterogeneity of banks is characterized by idiosyncratic shocks and the asymmetric transmission of common shocks.
Risk of about 1/3 of all banks rises in response to a monetary loosening. The lending response of small, illiquid, and domestic banks is relatively large, and risk of banks with a low degree of capitalization and a high exposure to real estate loans decreases relatively strongly after expansionary monetary policy shocks. Also, lending of larger banks increases less while risk of riskier and domestic banks reacts more in response to house price shocks.
here
EUROPEAN COMMISSION Consultation Countercyclical Capital Buffer
here