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A Multi-Polar rather than a Bi-Polar Investment World
The results we have looked at so far with regard to this model assume a bi-polar world of money/cash or interest-bearing securities. Suppose however that our investment world is much more complex than that, involving equities, fixed income securities, money market funds and money/cash. As a central bank cuts interest rates, the effect of this should be spread across these asset classes, which in turn react in different ways. If a central bank cuts interest rates, this should cause the investor to cut their portfolio weighting in money market funds and increase it in equities. In the short term, it should also cause an increase in the weighting for fixed income securities as the capital gain should offset the lost income. Eventually, however, we should assume that it causes a reduction in the weighting for fixed income securities. Finally, a rate cut should also lead to an increase in the weighting of money/cash. The reduction in money market funds and fixed income securities should logically equal the sum of the increase in weighting in equities and money/cash. Since money/cash has to share its gains with equities, one should assume that the effect on money demand and therefore prices is reduced. Prices should rise less than they would otherwise do without the influence of equities. Consequently, as prices rise by less, the exchange rate should also depreciate by less than one would otherwise expect. In the same way, an interest rate increase should in this multi-polar investment world lead to less of an exchange rate appreciation than would be expected in a bi-polar investment world.
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Theory vs. Practice
However, as ever with exchange rate models, in an open economy with high capital mobility there remains the issue of delay in the transmission mechanism. Monetary models suggest that an increase in interest rates should lead to an increase in the investor’s weighting of interest-bearing securities and a corresponding reduction in the weighting of money/cash. This in turn should lead to a reduction in the demand for and therefore the price of goods, which according to PPP should result in an offsetting appreciation of the nominal exchange rate in order to restore equilibrium.
In practice, it may not take place exactly like this, at least in the short term. Say you are an investor in US Treasuries and the Federal Reserve tightens monetary policy by increasing interest rates. Depending on what were market expectations for Fed policy prior to that and also depending on where you were positioned on the US yield curve, you may be facing losses on your position due to the simple inverse relationship between bond yields and bond prices. Eventually, the incentive to hold interest-bearing securities will rise as interest rates rise, but only at the point where the investor believes interest rates have stopped rising. Until that time, the investor may in practice do the opposite of what the model suggests, by reducing their position in interest-bearing securities and reverting to money/cash in order to preserve capital. Theoretically, the investor will have more money/cash to spend on goods and this should push up prices, which in turn should lead to depreciation — rather than appreciation — of the exchange rate according to PPP to restore equilibrium.
Equally, the natural reaction of our US Treasury investor to a fall in interest rates is not necessarily to reduce the position, given that falling yields equal rising prices. Eventually, the reduction in income will not be offset by the capital gain, at which point the investor will indeed reduce the position in favour of other assets such as money/cash. Before that, they may well maintain or even increase the position in interest-bearing securities in order to reap the capital gains impact. Thus, a reduction of interest rates may at least initially lead to an actual reduction in money/cash within portfolios, in turn causing money demand and prices to fall and the currency to appreciate according to PPP to restore equilibrium.
I suspect that the very suggestion that a reduction in interest rates may lead to a reduction rather than an increase in money/cash may cause one or two economists reading this to foam at the mouth. The point is a serious one however, and it is this — the assumption that a change in monetary policy leads directly and automatically to a parallel change in the exchange rate is flawed for the following reasons:
There may be a delay in the transmission mechanism
The initial exchange rate reaction may be the exact opposite of what standard models assume This is not in any way to reduce the importance of the original work. Rather, it is to bring it into the context of modern-day trading and investing conditions. Over the medium to long term, the Mundell–Fleming model of policy combinations is an invaluable guide to future exchange rate direction. In the short term, however, as I have tried to show, there may be delays and distortions, which at least put off the anticipated results. -
Mundell–Fleming
Thanks to the work of Robert Mundell and J. Marcus Fleming we know that certain combinations of monetary and fiscal policy create specific exchange rate conditions. The Mundell–Fleming model illustrates how specific combinations of monetary and fiscal policy changes can cause temporary changes in the balance of payments relative to an equilibrium level. The exchange rate therefore becomes the transmission mechanism by which equilibrium is restored to the balance of payments. It must be noted within this that the degree of capital mobility is crucially important.
In an economy with high capital mobility, suppose that a central bank decides to loosen monetary policy by cutting interest rates. One must assume that it does this because of weak growth conditions and benign inflation. As we saw before when looking at money demand, lowering interest rates reduces the incentive to hold interest-bearing securities, thus on a relative basis increasing the incentive to hold money or cash. This increase in money demand can be put to work buying goods and should reflect a future rise in national income and growth. The standard monetary model thinks of this in terms of rising demand causing price increases, which in turn causes the exchange rate to depreciate via the concept of PPP. Looking at it another way, rising domestic demand will cause rising import demand, which should mean deterioration in the trade balance. This in turn should eventually lead to depreciation in the exchange rate to allow the trade balance to revert back towards an equilibrium level. Another way of expressing the same thing is that lower interest rates cause capital outflows, which in turn cause depreciation in the exchange rate. Conversely, the basic assumption is that tighter monetary policy through higher interest rates should lead either to weaker domestic demand and a positive swing in the trade balance, or capital inflows, both of which should cause exchange rate appreciation.
On the fiscal side, much depends on whether trade or capital flows dominate. On the one hand, looser fiscal policy, either through tax cuts or spending increases, should cause rising domestic demand, which in turn should cause deterioration in the trade balance. On the other hand, looser fiscal policy causes higher domestic interest rates, which in turn attract capital inflows. If trade flows dominate, then the exchange rate should depreciate. However, if capital flows dominate, then the exchange rate should appreciate.
Conversely, tighter fiscal policy should, according to Mundell–Fleming, lead to weaker domestic demand. On the trade flow side, this should result in reduced import demand, causing a positive swing in the trade balance. On the capital flow side, tighter fiscal policy should lead to lower interest rates, which in turn lead to capital outflows. Here, if trade flows dominate, the exchange rate should appreciate, whereas if capital flows dominate, the exchange rate should depreciate. In a world of perfect or at least high capital mobility, it is assumed that capital flows dominate over trade flows.
This model can be used for developed economies and the leading emerging market economies which have deregulated and liberalized barriers to trade and more importantly capital. The classic example of this used in text books is that of the US dollar in 1980–1985, when it appreciated dramatically as the Reagan administration’s military spending programme dramatically boosted the budget deficit, while the Volcker-led Federal Reserve waged war against inflation (caused at least in part by those budget deficits). The Plaza Accord of 1985, which helped to bring down the value of the US dollar, worked only because it was accompanied by significant policy changes. In the 1993–1995 period, the US had a somewhat different problem to 1980–1985. While the new US government was moving towards the idea of balancing the budget, and thus tightening fiscal policy, the Federal Reserve was in 1993 keeping a relatively loose monetary policy. Indeed, one could argue that the Fed maintained an inappropriately loose monetary policy for much of 1994 up until its tightening of November 1994, before policy was seen as appropriately tight. Perhaps not coincidentally, in 1994 the US Treasury market had its worst year on record. In line with this, the US dollar weakened up until November of that year. The above model and examples assume either perfect or high capital mobility. However, not all economies are like this. While the move towards liberalization of trade and capital has broadly increased capital mobility, there remain specific countries in the emerging markets where capital mobility remains low (e.g. China). In this case, therefore, one must assume that trade flows dominate over capital flows.
The Mundell–Fleming model has done much to explain how combinations of monetary and fiscal policy should affect exchange rates. Indeed, their model is the standard for this kind of work. -
PPP and the Real Exchange Rate
The real exchange rate is a function of the price or inflation differential and the nominal exchange rate. The relationship between the concept of PPP and the “real exchange rate” — or the nominal exchange rate adjusted for price differentials — is of necessity a close and important one. In line with this relationship is the core idea that if PPP is seen to hold over the long term, then the real exchange rate should remain constant. This is the case because if PPP holds relative price differentials between two countries will over the long term be offset by an appropriate nominal exchange rate adjustment. Granted, the real exchange rate may fluctuate significantly over the short term, with the result that such fluctuations can have potentially important economic impact, however, it should revert to mean over time assuming PPP holds. When the real exchange rate is constant, the international price competitiveness of a country’s tradable goods is maintained. Another way of expressing this is to say that when a country experiences high inflation, its tradable goods become proportionally uncompetitive. In order to restore price competitiveness, there has to be a depreciation of the nominal exchange rate. In order to gain competitiveness, a country needs a real depreciation, not simply depreciation in the nominal value of the exchange rate.
The behaviour of the real exchange rate and its components can be broken down into that existing under fixed and floating exchange rate regimes. Under a fixed exchange rate regime, the nominal exchange rate’s ability to move is of necessity limited, hence changes in the real exchange rate must be a direct function of the change in the inflation differential, and this is indeed what we find empirically. By contrast, under a floating exchange rate regime, both the nominal exchange rate and the inflation differential can change or “adjust” in economists’ jargon. Thus, the relationship between the real and the nominal exchange rates is considerably closer. Indeed, because inflation differentials adjust relatively slowly in floating exchange rate regimes, most of the adjustment to the real exchange rate comes from an adjustment in the nominal exchange rate. Hence, the same cautions of applying PPP to nominal exchange rate valuation should also apply to real exchange rate techniques.
To summarize this concept of PPP or the law of one price, it is a poor predictor of short-term exchange rate moves. However, it is considerably more accurate on a multi-month or multi-year basis. Note that in the case of the Euro–dollar forecasts, the 13% overvaluation noted in January 1999 and the 11% undervaluation noted in April 2001 was a multi-month guide to the future nominal exchange rate. Thus, a corporate Treasury department or a long-term strategic investor can find a PPP model highly useful in terms of providing a directional framework for medium- to long-term currency forecasting. A “macro” hedge fund or leveraged investor might also find this highly useful for spotting disparities between fundamental valuation and market perception. On the other hand, this is clearly less so for short-term traders whose perspective is measured in days or weeks.
Some final points to note with regard to PPP:
PPP provides a useful medium- to long-term perspective of currency valuation
If PPP holds, the real exchange rate remains stable over the long term
There can however be substantial short-term divergences from PPP
PPP may thus be particularly useful in currency forecasting for corporations, long-term investors and also leveraged investors, but much less so for short-term traders -
PPP and Corporate Pricing Strategy
The law of one price assumes the exchange rate will move over time so that the price of the same good is the same everywhere. However, corporations do not necessarily follow this as they may vary national prices of the same good to reflect a variety of factors in those countries such as local supply/demand dynamics, delivery costs, cultural tastes, customer price tolerance, target margin, competitor prices, market share considerations and so forth. To an economist, such price variations represent temporary distortions, which should over time be eliminated by market efficiency. To a corporate executive, faced with the frequently competing real-world priorities of profit maximization and raising market share, there may be nothing temporary about such “distortions”. As a result, PPP may in some cases not hold over the “short term” for homogeneous goods since such pricing strategies may not allow it to hold.
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IT systems project failure
Numerous cases of cancelled or failed IT projects in the finance industry happen on an alarming scale. Many within the banking world are unreported because of reputation risk, i.e. risk of losing clients or looking foolish in front of rivals.
There are four major reasons for IT systems failure:
The risk management system was initially unsuitable for the bank or fund and could not be successfully tailored for use.
The skills base of the business project implementation was not properly understood or resourced.
Organisational politics or budgetary problems hindered progress.
Operational errors or poor systems design ruined chances of success. -
Integration and straight-through processing (STP)
STP would help us reduce losses where the buy and sell orders are either mismatched or lost. STP can reconcile trades and place them in accounts automatically by software packages. It would be admirable to have fast turnaround and cut down mistakes on trades. Trade processing errors can be costly, and they can be cut out using STP. Exceptions are costly; automating exceptions when processing trades can reduce costs by 25 %. Yet only 30 % of 500 financial institutions surveyed have fully automated exception reporting.7 STP will help us detect errors within our bank or fund, but will STP ever be implemented?
STP is the ideal sold by many systems vendors. But, in the real world where a front office may have a 100 IT systems and subsystems, STP may be part of the Holy Grail. The prospect of no accounting errors or orders mismatches is not borne out by reality. If an accounting error creeps in, how are we to flag it or reconcile it? It would be wishful thinking to wave a magic wand over the risk elements of fraud, mismatched orders and operations mistakes.
The idea of STP convey seamless processing between all three stages or departments, without any hitches or significant delays. There is no universal IT package that will fulfil all functions in the front, middle and back office. Reality offers that one system vendor will eventually be called into the bank or fund and be instructed to connect its new system to all the existing legacy systems. This means that we are looking at a reduction of the number of IT systems and subsystems instead of an agglomeration under one “Big Brother” system. A bank may think of buying a “vanilla” IT package, but they really come in many different flavours.
The systems market is diminishing with the cut-backs in financial institution expenditure and more banking M&A. This means further cuts in the choice of systems suppliers. Choose one that survives.
Algorithmics, Barra, Sungard, eRisk, Pareto et al. are financial system vendors that offer “risk management” systems in one form or another. A web trawl can reveal a hundred names or more for systems providers. All systems suppliers write one IT system and hope to resell it many times. Their profits lie in amending previously written systems, not in tailoring each one from scratch for each customer. They are the greatest recyclers of our time.
For example, Reuters, Barra or Sungard should stress that there is no bog-standard “one- size fits all” package. Theirs is an adaptable systems tool-kit backed by a bespoke consultancy service that includes tailoring to the business and portfolio of the specific bank or fund manager. A company may buy a systems package with a fixed price, but have to add 300 % for the amendments, project implementation and support services.8 Even then, project success is not guaranteed in any way. -
FINANCIAL IT SYSTEM SUPPORT
Financial IT development projects took a massive boost in the mid-1980s following “Big Bang”. Open systems running on common client-server architecture became the industry standard at the beginning of the 1990s and system choice for banks and fund managers increased. There are now numerous vendors, e.g. Algorithmics, Barra, Erisk, Misys, Reuters, Sungard, who will be happy to entertain you. The hardest job is to select which one. Finding the right supplier can provide real business value-added service at a competitive price. Technology has enabled a huge number of private investors to take part in whatever investment at a touch of a button. This has resulted in an unprecedented widening of the clientele within global exchanges. But, technology increased the potential for IT and systems failures, commonly lumped into the catch-all “operational risk”. Some banks have met spectacular failures, or have been taken over by more capable and risk-aware banks.
Choose substance and not style in risk management systems. Many system vendors promise to provide you with the “best” systems for every business line. We must choose the “best” IT systems supplier to design and install our specific business environment.
Good use of IT is not about buying fancier computer boxes and designing jazzier websites. All computer-based financial modelling tools and complex IT systems promise to help you. The Loss Database for Basel II is one product that holds a lot of potential. The question is whether it will deliver. The key to success lies in its project implementation.
The Basel II Loss Database project
The new Basel II banking regulations are geared to raising the overall level of risk management in banking and fund management portfolios. Basel II will enable regulators to request advanced operational risk-managed financial institutions to set up and maintain the Loss Database. It has two business drivers, one a mandatory requirement and an optional “nice-to-have”.
First – all financial institutions wishing to have the status of an “Advanced” risk-managed company must comply with the Basel II. One of the requirements is the formation of the Loss Database.
Second – there is the goal of detecting consistent patterns of loss, and extrapolating from the data to predict the likely level of future business losses.
The ultimate objective is to reduce their level of losses and increase the predictability of the remaining losses. The downside risk of this project is an expensive business and an IT white elephant that does not meet business expectations.
A large global bank can have an expensive loss database, both in terms of number and value of loss items, plus the huge project costs of creating the database. They cannot afford to get it wrong because to do so would be both costly and embarrassing. Backing out a failed loss database project from all global branches would also be a high-profile noticeable loss (compare: Reputational risk).
An operational loss database, driven by the desire for good management or by the regulators, represents a large investment. An empowered band of financial specialists can reap real rewards for the company, supported by IT systems staff to “drill-down” within the loss database. This data-mining involves finding out lines of causality for:
who
when
how much was lost
how much could have been lost
why it all happened in the first place.
Loss databases will have to prove themselves against resilience-based approaches. These data will be analysed time and time again under different data-mining angles. The real test will be that of continual testing and review for cost-benefit analysis.
The loss database is a potentially good corporate risk management tool, but, it is likely to fail where it attracts little support within the corporation. Loss data are input for risk management decision making, and it needs a lot of massaging into acceptable reports before it can help to formulate director-level actions. The initiative stands or falls on whether top management supports and funds it.
The benefits are easier to predict than the costs. An advanced-certified operational risk-managed bank will have lower Basel II regulatory capital charges because its risk management processes are highly developed and evaluated as a lower overall risk. From previous regulatory examples within credit risk, a bank could find its regulatory capital reserve falling by some 6 %.4
How much this will translate into similar savings for OpRisk is to be decided by the regulators interpreting the Basel II guidelines.
Risk appetite becomes more directly linked to risk offer, but risk appetite is also covered by Basel II regulatory capital. Risk support systems alert the danger of capital becoming inadequate to cover expected losses.
The loss database business rationale may be a search for lower risk ratings and knowledge data-mining, forced on them by the regulator. The compliance “Big stick” approach of the regulator may be better at explaining the need for the database, instead of the more complex business cost-benefit analysis.
Losing money has never been in the interests of a bank, nor of its clients. Yet banks and investment funds continue to lose money without knowing where or why. There is some hope that this integrated database, linked to advanced modelling tools, can help make investing less risky. It will most likely be a complex and expensive project to set up, mainly because of the complexity and size of the data collected.
The formation of a complex loss database is a knowledge management structure that we are actively constructing. It requires a lot of data and system integration to link the disparate elements in a global bank. Some call this risk management system a “data warehouse” where information is packaged into one compatible format for analysis (see Enterprise application integration – EAI). The benefits are the harnessing of market intelligence to understand: who, when, how and how much money has been lost. Then, we can reinforce risk management procedures to avoid such a loss recurring, or to reduce the loss when the hazard strikes again. -
RISK MANAGEMENT METHODOLOGY – RAMP
Activity A: Analysis and project launch
Define risk strategy.
Appoint a risk analyst or problem owner.
Outline the objectives and investment project scope.
Estimate people and skills required, investment complexity, budget and timetable.
Establish an investment project plan with baselines.
Estimate the “most likely” outcome, plus alternative pessimistic scenario.
Activity B: Risk review
Identify project risks, both likely and unlikely.
Analyse risks and their frequency plus probable impact.
Generate mitigation options and discuss them briefly.
Create a risk matrix applicable to this project (cf. Basel II Loss Database).
Consult a Delphi group of experts familiar with similar projects.
Spotlight risks needing deeper scenario analysis and mitigation measures.
Pick cost-effective mitigation for each risk.
Define plan for each mitigation option.
Devise actions for handling residual risks.
Check risk measures with third parties.
Plan financing of the risk management measures.
Get approval for commencing the risk management project with key stakeholders.
Activity C: Risk management
Implement the risk management plan.
Check that risk management plan is compatible with current management processes.
Check that contracts, financing and insurance are compatible.
Confirm that that the risk management plan is properly staffed, resourced and funded for
successful implementation.
Monitor the expected plan results against realised.
Monitor changing market conditions and the extent of risks present.
Revise plan actions where necessary.
Evaluate whether the investment project should continue.
Activity D: Project close down
Summarise the risk events with impact in relation to risks predicted.
Pick out the residual risks and risks unforeseen.
Conclude how successful the project was in financial and risk management terms.
Close down the project with a report for key stakeholders.
Putting this into the RAMP context we can derive a risk management project plan.