market feature, which is notsubmerged by transaction costs, then it will slowly haul ahead of a passive benchmark. Section9.2 tells us that this can happen at active manager universe level as well.Box 10.3
‘Star’ investors
‘Star’ investors are a modern, and interesting, phenomenon. They exist in many activeinvestment management ﬁrms, and they are believed by clients and their employers aliketo possess exceptional investment skills which translate into exceptional investment performance. How far are these perceptions based on reality, and how much are they createdand promoted by the investment ﬁrm and the stars? There are two obvious tests to apply toanswer these questions. The ﬁrst is to ascertain whether the ﬁrms in question are able, withany consistency or reliability, to beat the benchmarks against which they are measured. Thesecond is whether this performance is portable with respect to the stars – that is, if theymove ﬁrm does their performance move with them?Both of these questions are hard to answer, but the ﬁrst is easier. Most active equitymanagers do not have records of consistent outperformance. There is an active debatewhether US equity managers are, as a group, so sophisticated and so dominant in the USequity market that all inefﬁciencies have been removed. This would both undermine thecase for the ‘star’ system and for active managers in general. American investors wouldargue that active outperformance is still possible in the ‘less sophisticated’ foreign equitymarkets, although such evidence as there is appears to point to country (and currency)selection as being the greatest contributors to outperformance.The evidence for ‘portability’ of performance is almost impossible to establish. Firms donot provide performance by portfolio manager; much of the support for the ‘star’ system istherefore anecdotal, or explainable by chance and survivor bias. Hedge funds are a potentialsource of evidence in this area, since they are often linked to (or founded by) particularinvestment stars. However, the hedge funds with long track records are so few, and thebenchmarks against which to measure them so debatable, that it is impossible to abandonthe hypothesis that active equity outperformance is the result of chance.In currency overlay management the star system does not really exist. Most overlaymanagers have adopted highly quantitative approaches to active management, and it is thereputation of the quality of the analysis and modelling process, rather than reliance on a‘star’, that is the key distinguishing feature of overlay managers.
10.2.3 What about active management without models?Successful active managers are like successful chess players. They need to be clever, knowledgeable and resourceful, and be able to respond to new situations in an effective way, andultimately, they need to beat their peers. It is therefore likely in many markets that personalknowledge (knowledge of target companies, knowledge of market behaviour) is the best routeto beating the other market participants. However, even a game-theory-driven game like chess
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can be codiﬁed. With modern computers, all but the very best players are now being routinelybeaten by computer models. Even the best player in the world was beaten by a computer modelin the celebrated 1997 series.5Very good players, in markets as much as in chess, operate in a non-random way whichcould in theory be codiﬁed into a model. Arguably the most successful living investor, WarrenBuffet, has publicly set out his criteria for investment in the stock of a company. He has (largely)stuck to his ‘model’ despite it looking poor in some circumstances (e.g. the Internet bubble).The criteria are simple, and in theory easy to copy. They have not been widely copied formany reasons, the most pressing being that those asset managers with enough money undermanagement to be able to take strategic stakes in top-of-sector companies are required by theirclients to deliver index-like returns. Warren Buffet-like investing is inconsistent with index-likereturns.Models only codify consistent ways of behaving, however complex the thinking and theinput data. In the currency market ‘personal’ styles of active management have been lesspopular and successful than more quantitative approaches because the levels of noise are high,the horizons long, the quantity of data high, the ‘inside information’ scarce or non-existent,and the ‘reﬂexive’ element quite low. The latter is a consequence of the huge size and diversityof the market. However, the previous section explained that the most likely long-term winnersin active currency will have relatively simple models, and these do not necessarily need to beformally codiﬁed as long as their rules are consistently applied.10.2.4 Dealing and practical executionThe best models in the world are no good unless an active currency manager has a well-designedprocess for converting model output (or indeed discretionary judgement) into live deals. Thisis no easy task, the difﬁculty of which is much underestimated.Let us suppose that a manager is developing or reﬁning a model. The inputs to the modelwill include the current exchange rate (spot and forward), a variety of historical rates (spotand forward), and an array of other, exogenous variables. To be effectively tested, the testingprocedure must exactly match the live procedure to be adopted once the model is live. Otherwise it’s like testing a new car on a smooth, ﬂat, test-track, and the customer taking it outon real (rough and hilly) roads, expecting it to perform to test-bed speciﬁcation, and beingdisappointed.10.2.5 Timeliness of inputsFor all modellers with an eye to forecasting, a ﬁrm understanding of the timeliness of inputsis critical. Let us suppose that one of the exogenous input variables is (say) balance of payments data. This data may be released regularly at, say, 10.00am local time once a month.Whatever use the model makes of this data, it must always ensure that it uses it (and only contemporarily available information) after this date and time. If the announcement also includesrevisions of past data (as is common), then only those contemporary revisions are capable ofbeing used. It is common for economic data to have a series of revisions; and unless each of5
Kasparov vs. IBM’s Deep Blue.
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these is independently stored as the latest information on a particular date, then the modelis in danger of targeting post-dated data. If only (ﬁnally) revised data is stored in one timeseries, then the model will be anachronistic – using data at time t that was only available attime t + 1.Similar problems occur with price data. If the technical component of an active currencymanagement model uses contemporary prices, exactly what prices are these, and how are theyreﬂected in the live execution process? Let us suppose a technical model is designed with,and runs on, daily prices. It assumes (not unreasonably) that it can execute deals at the storedhistorical daily price, with an adjustment for bid/offer spreads. Does the execution process onlyrun on daily prices, or does the dealer have a price-level signal stored in the computer readyto deal at any time once the level is breached? If the latter, then this is a signiﬁcant deviationfrom the model, and either the model should be revised to use higher-frequency prices, or theexecution should be amended to deal at only once-daily price ‘ﬁxings’. Maintaining a gap inexecution process between the model and live dealing signiﬁcantly reduces the quality of themodel, because the model has no information about higher frequency data.10.2.6 Judgement and modellingThe exercise of discretion in the execution of a quantitative model in the currency market sitsvery uneasily with the concept of the scientiﬁc method in modelling.Let us suppose a model generates a signal. The signal is passed to the portfolio manager,who then exercises his judgement as to the timing and manner of the execution (and indeed theadvisability of the deal at all). The question that needs to be answered of such a process is this:does the discretion of the portfolio manager improve the operation of the process?6 To answerthis, the ﬁrm will need accurate record-keeping of the tradeable prices at the time the model’ssignal is issued, and of the ﬁnal (judgement-inﬂuenced) deal. If this data is available, thenthe portfolio manager’s input can be analysed, and value-added and risk measures applied todetermine its success or otherwise. The model’s live track record (which is the non-judgementinﬂuenced trades) can be analysed and compared to the simulations on which the model wasconstructed. Quite separately, the portfolio manager’s deals (the gap between the model’s dealsand actual execution) can be analysed. If the portfolio manager is consistently successful, thenhe is a generator of currency ‘alpha’,7 and not dependent on being ‘given’ trades by the model.It may also be worth the ﬁrm trying to understand what (further) inefﬁciency the portfoliomanager is exploiting (not being fully exploited by the model) that allows this value added.Finally, the ﬁrm might consider whether this skilled input from the portfolio manager can becodiﬁed, and itself turned into a model.If the portfolio manager is not consistently successful, then there might be good reason toeliminate this source of uncertainty from the ﬁrm’s process. If the data for this ‘alpha attribution’is not available to the ﬁrm (or the client), how can either be sure that the discretionary exerciseby the portfolio manager is worth doing? Clients who get the opportunity should ask a ﬁrmwith discretionary override to justify its policy with data.6This section refers to value-added processes in currency markets, but not necessarily to other, less liquid markets. Because of theliquidity of currency markets, model-generated deals do not need to be ‘ﬁnessed’ to get good execution. The quoted price is in mostinstances instantaneously tradeable at narrower than quoted spreads. See Chapter 4 for more on this.7‘Alpha’ is commonly used to denote ‘value added’ versus a benchmark.
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10.2.7 Deal executionCurrency managers will be judged on their ‘after-dealing-costs’ performance. While this shouldprove a strong spur to ensure that their deals do not attract excessive bid/offer spreads fromthe foreign exchange market, there may be some factors which inﬂuence an overlay manager’schoice of counterparty for his client. The most obvious factor (and potential conﬂict) is wherean overlay manager is owned by a bank with an active FX dealing room. Deals undertakenby the overlay manager with his parent will always create an element of uncertainty – andprobably the most transparent solution is for such a manager to forbid themselves deals withtheir parent.Box 10.4
Active currency styles
Currency overlay managers are commonly categorised into three styles: fundamental, technical and dynamic. There is a fourth, option-based, which in many ways (but not all) is adifferent manifestation of dynamic hedging.Fundamental managers believe they can exploit price inefﬁciencies using models andprocesses in which economic and ﬁnancial data is used as the ‘exogenous’ variables. Examples of such data include balance of payments, capital ﬂows, relative price levels, relativemonetary conditions, etc. Fundamental managers have to accept that there may be quitelong lags (measured in years not months) where their models may not work effectively,and this has reduced to a handful the number of overlay managers who use exclusivelyfundamental techniques.Technical managers are philosophically the ‘opposite’ of fundamental managers. Theytend to ignore completely external economic variables, and argue that price and price historyprovide the most effective mechanism for exploiting inefﬁciencies. Their typical approachwould be to model price history to determine successful trading rules – say like buying acurrency when its 5-day moving average price crosses its 25-day moving average. Goodtechnical managers in currency overlay have added value over time.Dynamic managers are a group that aims to create an asymmetric return – runningproﬁts and cutting losses. Many dynamic managers use currency option technology to dothis. Most use forward contracts for this process, but a smaller group use option instruments(see below).Option-based managers’ approach is to exploit what they see as systematic differencesbetween implied and actual future volatility. In many ways the inefﬁciencies they exploit,and the pattern of excess returns, are similar to dynamic managers’.There is a live debate as to whether these categories are accurate or appropriate. Few of theoverlay managers active at the time of writing would fully subscribe to these descriptions.
10.3 ACTIVE MANAGEMENT STYLESI want to turn next to the common styles in active currency management. The naming of stylesin asset management is always a little contentious – each style is named by its proponents insuch a way to make it attractive or ‘natural’. Two popular styles in active equity managementare ‘growth’ and ‘value’. Both sound good. But they are simply two halves of the spectrum ofone particular value or group of values – essentially price/earnings ratios. High ratio = growth
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stock; low ratio = value stock. They can’t both simultaneously outperform the combination ofthe two (which is the whole market).Active management styles are in reality a reﬂection of the types of model and model inputthat a manager chooses to employ. Once a manager chooses to accept a particular relationshipas having predictive power, it will determine his ‘style’ as seen by the outside world. The mostcommon categories in active currency overlay (see Box 10.4) are as follows:
r Fundamentalr Technicalr Dynamicr Option-basedI will take each in turn, although I will look at option-based styles before dynamic hedging,since this gives the introduction to option-like payoffs that dynamic hedging is based on.10.3.1 FundamentalFundamental managers believe that currency markets are not semi-strong efﬁcient. That is,they believe there is non-currency-price public information that they can (regularly) obtainthat will give them greater than 50% success rate in their exchange rate positions at somehorizon in the future – i.e. make their clients a proﬁt.Fundamental forecasters are deluged with possible contenders for explanatory data – inﬂation, interest rates, money supply, GDP growth, consumer spending, central bank reserves,bond yields, cross-border asset ﬂows, balance of payments, foreign direct investment, centralbank intervention policy, government borrowing, ﬁnances and debt, etc. The list goes on and on.I could list virtually every category in each government’s statistical output of economic data,and hardly any would be completely irrelevant. How do managers sift this data for relevance,and how do they build effective models?The sifting of data is generally determined by the hypotheses that a manager wants to test.If a manager does not have a clear ex ante hypothesis, then that is a reliable signal that themodel is going to end up with a strong ‘data-mining’ element.8 Let me give an example ofhow the testing process might work. (I have kept it simple to illustrate the process, not to builda credible model.)10.3.1.1 HypothesisLet us suppose that the hypothesis is that exchange rates that are higher than purchasing powerparity levels have a tendency to go down (increasing with the distance from parity levels),and that rates that are lower than purchasing power parity levels have a tendency to go up(increasing with the distance from parity levels). It is intuitively appealing and the inputs arerelatively simple.However, it is not quite as simple as it may seem. This hypothesis may be true, but still notbe a way for the manager to make money. This is because the manager can only deal in interestrate-adjusted spot rates – i.e. currency surprise. So for the model to be any use, the hypothesismust be changed to: ‘spot exchange rates that are higher than purchasing power parity levels8Data mining is the (dismissive) name given to models which are built by choosing as explanatory variables those series that ﬁtthe data, rather than those which reﬂect a well-deﬁned theory. It is pretty much the same thing as ‘curve ﬁtting’.
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Box 10.5
Capital account
National accounts’ economists separate balance of payments data into current and capitalaccount. The way the national accounts process works is that physical imports and exportsare recorded in detail, while imports and exports of services (including interest and dividendsin both directions) are much more roughly estimated (since there is no port documentation)as ‘invisibles’. If these two do not add up to zero, then the balancing item is called ‘capitalaccount’. Interestingly, countries with exchange control regulations may have much betterinvisibles data than those that don’t, although exchange controls will almost certainly alsospawn a black market, where there is by deﬁnition no data.Capital account transactions, being a residual, are enormously varied. Taking, say, theUS perspective, the most obvious ones are cross-border lending and investment in securities – e.g. Japanese public and private sector purchase of US Treasury and other securities‘ﬁnancing the US deﬁcit’. Other less obvious forms of capital account transactions are,for example, US investment institutions hedging their foreign exchange exposure or aquoted US company building new US plant with the proceeds of a share placing. All theseare examples of capital inﬂows into the US. The last case (where I am assuming someforeign ownership of the quoted company) is that foreign owners subscribe to the shareplacing.The current/capital split is defended on the grounds that current account performance isa great deal more predictable than capital account. But it is worth remembering that thesum of the current and capital account is always zero; which implies to the logical noneconomist that the capital account is just as predictable as the current account. The onlyquestion is the exchange rate that is necessary to achieve that.have a tendency to generate negative currency surprises (increasing with the distance fromparity levels), and spot rates that are lower than purchasing power parity levels have a tendencyto generate positive currency surprises (increasing with the distance from parity levels)’. Ifthis hypothesis proves to be supported by the evidence, and is stable over time, then in theoryit could be the basis for a fundamental model.10.3.1.2 Inputs1. We can proxy the changes in purchasing power with the relative change in consumer priceindices in the relevant countries (we will need the date that the later of the two inﬂationlevels is announced to avoid the anachronistic mistake).2. We need a level for purchasing power parity. This could perhaps be the inﬂation-adjustedaverage spot rate over a long data period (say 20 or 30 years). But see below the discussionof purchasing power parity.3. We will have to have a series of spot rates to calculate where current rates are in relation topurchasing power – and forward rates have no role in this calculation.4. We will need a series of currency surprise – or more particularly an interest-rate-adjustedindex of the exchange rates. We need the latter because we might want to vary the horizonover which this hypothesised effect applies. It might not work in a month, but might workover two years.
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We also need to pick a currency pair to start with. We will use USD/GBP as an example, butin a live modelling environment, this would be many pairs. We need to choose a modellingfrequency: most econometric models are no more frequent than monthly, whereas tradingmodels are almost always daily frequency.10.3.1.3 RegressionMost econometric models use a technique called ‘regression’, whose basic principles areas follows. A hypothesis is established, say that future changes in log currency surprise aredetermined in part by the distance that the spot rate is from purchasing power parity. If interestrate-adjusted log currency index = ct , then future changes at horizon n months = ct+n − ct .Suppose that xt = % over/undervaluation of sterling using PPP values as the benchmark. Theregression equationyt = a + bxt + ewhere yt = ct+n − ct is a standard linear regression equation, testing whether y is explainedby x; a and b are constants (which will be calculated in the regression), and e is the error term(i.e. the unexplained variations in y).10.3.1.4 CorrelationBecause we are only testing the relationship between one external factor (PPP) and changes infuture exchange rates, we can run correlations between these two series. We will have about20 years of monthly data, so about 240 points in the series. For this number of observations,correlation coefﬁcients above about 0.13 (or −0.13) are statistically signiﬁcant (i.e. we canreject the hypothesis that the two series are unrelated).10.3.1.5 Trading modelAs well as testing the statistical relationship between series, we can also test whether tradingrules set up to attempt to exploit the relationship can make money. Any trading will be conductedin the currency surprise series (since this is exploitable), not the spot return series (since thisis not investible). The rules can be inﬁnitely varied. I have shown three. The ﬁrst two takepositions for certain predetermined horizons (say 24 months): they are a variable position(i.e. proportionately shorter of sterling as it gets increasingly overvalued) and a ‘long/short’switch as the spot rate crosses PPP. The third model does not have a horizon, instead it takesproportionate positions each month dependent on sterling over/undervaluation vs. PPP. Theseare adjusted end-month on the basis of new PPP levels.10.3.1.6 PPPAll three modelling approaches require additional inputs from the modellers to get results. Thehorizon (the time over which PPP is presumed to work) is one variable; perhaps more criticalis the PPP reference levels. Calculating deﬁnitive PPP is not a science. While it is possibleto analyse the price of traded goods, and determine exchange rates that equalise those prices,there are a wide variety of imponderables that make the calculation not deﬁnitive. Taxation
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differences, product speciﬁcation differences, differences in ﬁnancial structure of the respectiveeconomies, transport and distribution hurdles, and so on.9 One way out of this uncertainty isperhaps to use the long-term inﬂation-adjusted historical average of exchange rates, on thebasis that these will have oscillated round a long-term equilibrium (presumably somethingnear to PPP). The problem with this is: what historical period do you use? If the period iscontemporary with the testing period (in this case 1980–2002), then there is anachronistic datain the model. If it is earlier, it may be less relevant, and pre-1971, there were ﬁxed exchangerates in which market clearing could not operate effectively.Box 10.6
Purchasing power parity
PPP is an important theory for the determination of exchange rates. It is important becauseit is the bedrock of the argument as to whether exchange rates are over- or undervalued.The core theory runs as follows. A large part of industrialised economies’ GDP is tradeablegoods (cars, food, drink, clothes, consumer durables). We can take a basket of these goods,and convert their domestic prices in different countries into a common currency (say theUSD) at current exchange rates. This will show a dispersion of countries’ price levels – e.g.that a hamburger in Japan is twice as expensive as a hamburger is the US, implying that theyen is overvalued against the dollar.The PPP argument is that relative price levels will tend towards parity as arbitrage takesover. If an identical CD player can be sold for twice as much in Sweden as in Singapore,then traders will export more CD players to Sweden, and less to Singapore. If repeatedacross the economy, this will increase the balance of deﬁcit in Sweden, and increase thebalance of payment surplus in Singapore, depressing the Swedish exchange rate and raisingthe Singaporean, tending to equalise the prices.While superﬁcially attractive, there are a number of major ﬂaws in this theory. Thekey ﬂaws from the economic perspectives are: non-tradeable country-speciﬁc factors likelabour costs, taxes, property prices and infrastructure affect domestic prices, but are notarbitrageable. There are also the effects of tariffs, transport costs, differential product speciﬁcation costs and differential indirect taxes on damping arbitrage for the elements whichare tradeable. Finally, and perhaps most important, is the dominance of capital ﬂows in theshort-term determination of exchange rates. This can allow PPP overvaluation, togetherwith large balance of payments deﬁcits, to persist for many years without exchange ratemovements to compensate (or even with perverse exchange rate movements).For a quantitative 100-year study, see Alan M. Taylor, A Century of Purchasing-PowerParity, National Bureau of Economic Research Working Paper No. w8012, November 2000.It also has a very good bibliography.I have chosen to test the following models on data 1980–2002, with PPP equilibrium levelscalculated from 1971–1990 average actual price and exchange rate levels. This assumes thatthe secular differences in economic structure will be reﬂected in the 30-year averages. Henceif an exchange rate appears constantly overvalued in the OECD statistics (like, say, JPY), thissystematic bias is eliminated by adjusting for the 30-year averages. In other words, the 30-year9The OECD (Organisation for Economic Co-operation & Development) publishes monthly PPP values for OECD member states.They publish, inter alia, the ratio of actual exchange rates to those that would give common price levels across member countries.Such published statistics, however, do not solve any of the problems listed above.
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average PPP level for each currency is deﬁned as 100. A currency is therefore overvalued if itis trading at an adjusted PPP level of >100, and undervalued if it is trading at a level of <100.In this example, I have already mentioned that I use only one pair (USD/GBP), although ofcourse in the live modelling environment I would look at all major pairs (and probably someminor).With all these caveats, and remembering that this is an artiﬁcial exercise, let’s look at theresults on actual data.10.3.1.7 Model resultsEach of these models gives different results (Table 10.1). Model 1 gives very high correlations,rising continuously with horizons to about 80 months, falling thereafter. At these horizons itis possible to get 0.8 correlation. Prima facie evidence, perhaps, that future currency surpriseis explained by deviations from PPP? Model 2 strongly supports this contention, showingincontrovertible t-stats for the explanatory variable of >10. Likewise, the trading models areall proﬁtable, but with different horizons showing peak performance. It all looks good, but isit the makings of a successful fundamental model? Not necessarily.There are a number of points to make. The most obvious is that when the PPP equilibriumlevel is varied, to test the sensitivity of the model to this value (which should be important if the theory is right), two of the three trading models are insensitive to the changes.Both models still make money when every trade is ‘short’ of sterling, or alternatively everytrade is ‘long’ of sterling. For this to be the case, the market must have exhibited a meanreverting element over this period (at least at the horizons tested). This means that the model‘looks’ like a fundamental model, but is in fact a technical model, making money out of meanreversion.Table 10.1 Fundamental model resultsType of model
Target to be modelled
External variables
Results
1 Correlation
Change in futurecurrency surpriseChange in futurecurrency surprise
% spot over/undervaluedvs. PPP% spot over/undervaluedvs. PPP
Cumulative proﬁts
% spot over/undervaluedvs. PPP
>0.8 – peak horizon85 monthst-stat for PPP >10depending onhorizonProﬁts peak at 13months horizon
Cumulative proﬁts
% spot over/undervaluedvs. PPP
Proﬁts plateaubetween 15–30months horizon
Cumulative proﬁts
% spot over/undervaluedvs. PPP
Proﬁtable for all PPPlevels
2 Regression3 Trading model – ﬁxedhorizon forwardpositionproportionate toratio4 Trading model – ﬁxedhorizon forwardposition +/−depending onover/undervaluation5 Trading model –variable horizonforward position+/− depending onover/undervaluation
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We can cast further doubt on the model by a split test – the model makes ﬁve times as muchin 1980–91 as it does in 92–02, and in 98–02 makes almost nothing.Finally, this exercise has looked at one bilateral currency relationship. A fully-formed fundamental model needs to look at many more than one currency pair. It may need to rank thestrength of the signals generated to ensure that risk is being most efﬁciently rewarded, and itwill need to ﬁt into the risk/return requirements that the investor demands.10.3.1.8 Forward rate biasThe foreign exchange market, like the commodities markets, not only has spot prices activelyquoted and traded every day, but also a range of forward prices up to one year horizon.As we have seen, the forward discounts and premiums (vs. spot) are themselves a sourceof information, and since they are additional information outside the univariate price series,they probably have to be put in the ‘fundamental’ style pot. This is a little tenuous: mostcurrency overlay managers would not call the forward rate bias a ‘fundamental’ effect, but inthe arbitrary styles currently fashionable, FRB probably goes in this pot. I will not spell out herean active process which exploits the forward rate bias, since I covered this quite extensively inChapter 9.10.3.1.9 Summary on fundamental modellingI have not brought the modelling exercise to a conclusion. I have not looked at the volatilitiesof the value added, and so the quality of the value-added series is as yet untested. But theexperience described above is common: powerful relationships at ﬁrst look, and then withcloser and closer attention the reasons for the relationships become less clear, or emerge ascounterintuitive, or are ‘picking up’ the statistical inﬂuences of other variables. The evidence,which regrettably is mostly anecdotal, is that fundamental inputs into currency overlay areinadequate, on their own, to provide an effective active overlay process. The key shortcomings are instability of explanatory relationships, and the unresponsive nature of fundamentalmodels.Instability is a very serious shortcoming indeed in modelling. It arises from explanatorymodels that are generally incomplete; that is, they are missing signiﬁcant explanatory elementswhich, at least for some periods, are important contributors to overall price moves. Fundamentalmodelling faces the almost impossible task of including all these variables if a model is to bestationary (the opposite of unstable in this context). But as a modeller increases the number ofthese variables (e.g. is the Malaysian Central Bank active today Y/N?), the danger increasesthat the intuitive basis for the model – the core hypotheses – are diluted and lost. This willlead to high R 2 s and t-stats in the modelling period, and huge disappointment in the liveperiod.Even if a robust stationary model can be constructed, there is the problem of ‘responsiveness’. The model constructed above had an element of responsiveness built in – the PPPover/undervaluation calculation is dependent on the spot rate, and this introduces an elementof ‘lagged dependent variable’ into the equation. ‘Lagged dependent variable’ describes thesituation where part of an explanatory variable (PPP over/undervaluation) is itself an earliervalue (exchange rate) of the value we are trying to predict (also the exchange rate). Laggeddependent variables bring their own statistical problems, and we will look at these in Section10.4, where lagged dependent variables are the sine qua non.
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10.3.2 TechnicalManagers who use the ‘technical’ currency overlay style believe that the currency marketsare not weak form efﬁcient. In other words, they believe that the markets’ own price historyhas predictive power for future prices. I have already stated earlier at the start of this chapter that I believe the evidence strongly supports this position. The chapters (Chapter 4 andChapter 9) that describe the structure of the currency market, and its dominance by non-proﬁtsensitive participants, explain why this belief can be well founded, and that this inefﬁciency canpersist.This forms the background to technical managers. The detail is largely statistical. If there isinformation in the price available at time t which is predictive (however indirectly) of futureprices,