Testing for Efficiency of Foreign Exchange Markets Finance Essay

A capital market is said to be efficient if prices in the market ‘fully reflect’ available information. When this condition is satisfied, market participants cannot earn economic profits (i.e. unusual, or risk adjusted profits) on the basis of available information. This classic definition, which was developed formally by Fama (1970), applies to the foreign exchange market as well as to other asset markets. As stated, the definition is too general to be tested empirically. The term ‘fully reflect’ implies the existence of an equilibrium model, which might be stated either in terms of equilibrium prices or equilibrium expected returns. In an efficient market, we would expect to have actual prices ‘conform to’ their equilibrium values, and actual returns ‘conform to’ their equilibrium expected values.

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Foreign Market Efficiency

The exchange rate between domestic and foreign currency is a major economic policy variable. Therefore, the efficiency or otherwise of a foreign exchange market is very important for policy makers of any country. An efficient foreign exchange market indicates that a government cannot influence the movement of exchange rates as the exchange rates are not predictable. The government can make informed decisions on exchange rates, take actions to reduce exchange rate volatility and evaluate the consequences of various economic policies for exchange rates. Participants in the foreign exchange market can devise various trading rules or techniques to make abnormal profits from transactions in the foreign exchange market. However, they should consider the costs involved in such activities to determine their profitability. Future researchers can corroborate the results of this study by employing other econometric techniques such as asymmetric and nonlinear models and high-frequency data. About a generation ago the Efficient Market Hypothesis was widely accepted by the financial economists to be the prevalent norm. It was the general belief that securities markets were extremely efficient in the sense that they were able to absorb information very quickly which was reflected immediately. This meant that investors cannot benefit either from the technical analysis. Previous studies have suggested an increase in correlation among the world’s FX markets as many developing countries have introduced capital account convertibility. The idea that the expected risk-adjusted excess return on foreign exchange is zero implies a sensible statement of the efficient markets hypothesis in the foreign exchange context: Exchange rates reflect information to the point where the potential excess returns do not exceed the transactions costs of acting (trading) on that information. In other words, you can’t profit in asset markets (like the foreign exchange market) by trading on publicly available information. This description of the efficient markets hypothesis appears to be a restatement of the first principle of technical analysis: Market action (price and transactions volume) discounts all information about the asset’s value. There is, however, a subtle but important distinction between the efficient markets hypothesis and technical analysis: The efficient markets hypothesis posits that the current exchange rate adjusts to all information to prevent traders from reaping excess returns, while technical analysis holds that current and past price movements contain just the information needed to allow profitable trading. What does this version of the efficient markets hypothesis imply for technical analysis? Under the efficient markets hypothesis, only current interest rates and risk factors help predict exchange rate changes, so past exchange rates are of no help in forecasting excess foreign exchange returns-i.e., if the hypothesis holds, technical analysis will not work. How do prices move in the hypothetical efficient market? In an efficient market, profit seekers trade in a way that causes prices to move instantly in response to new information, because any information that makes an asset appear likely to become more valuable in the future causes an immediate price rise today. If prices do move instantly in response to all new information, past information, like prices, does not help anyone make money. If there were a way to make money with little risk from past prices, speculators would employ it until they bid away the money to be made. For example, if the price of an asset rose 10 percent every Wednesday, speculators would buy strongly on Tuesday, driving prices past the point where anyone would think they could rise much further, and so a fall would be likely. This situation could not lead to a predictable pattern of rises on Tuesday, though, because speculators would buy on Monday. Any pattern in prices would be quickly bid away by market participants seeking profits. Indeed, there is considerable evidence that markets often do work this way. Moorthy (1995) finds that foreign exchange rates react very quickly and efficiently to news of changes in U.S. employment figures, for example. Because the efficient markets hypothesis is frequently misinterpreted, it is important to clarify what the idea does not mean. It does not mean that asset prices are unrelated to economic fundamentals. Asset prices may be based on fundamentals like the purchasing power of the U.S. dollar or German mark. Similarly, the hypothesis does not mean that an asset price fluctuates randomly around its intrinsic (fundamental) value. If this were the case, a trader could make money by buying the asset when the price was relatively low and selling it when it was relatively high. Rather, “efficient markets” means that at any point in time, asset prices represent the market’s best guess, based on all currently available information, as to the fundamental value of the asset. Future price changes, adjusted for risk, will be close to unpredictable. Believers in efficient markets point out those completely random price changes-like those generated by flipping a coin-will produce price series that seem to have trends. Under efficient markets, however, traders cannot exploit those trends to make money, since the trends occur by chance and are as likely to reverse as to continue at any point. Grossman and Stiglitz (1980) identified a major theoretical problem with the hypothesis termed the paradox of efficient markets, which they developed in the context of equity markets. As applied to the foreign exchange market, the argument starts by noting that exchange rate returns are determined by fundamentals like national price levels, interest rates, and public debt levels, and that information about these variables is costly for traders to gather and analyze. The traders must be able to make some excess returns by trading on this analysis, or they will not do it. But if markets were perfectly efficient, the traders would not be able to make excess returns on any available information. Therefore, markets cannot be perfectly efficient in the sense of exchange rates’ always being exactly where fundamentals suggest they should be. Of course, one resolution to this paradox is to recognize that market analysts can recover the costs of some fundamental research by profiting from having marginally better information than the rest of the market on where the exchange rate should be. In this case, the exchange rate remains close enough to its fundamental value to prevent less informed people from profiting from the difference. Partly for these reasons, Campbell, Lo, and MacKinlay(1997) suggest that the debate about perfect efficiency is pointless and that it is more sensible to evaluate the degree of inefficiency than to test for absolute efficiency.

Need For Conducting This Study-

The miserable empirical performance of standard exchange rate models is another reason to suspect the failure of the efficient markets hypothesis. In an important paper, Meese and Rogoff (1983) persuasively showed that no existing exchange rate model could forecast exchange rate changes better than a “no-change” guess at forecast horizons of up to one year. This was true even when the exchange rate models were given true values of future fundamentals like output and money. Although Mark (1995) and others have demonstrated some forecasting ability for these models at forecasting horizons greater than three years, no one has been able to convincingly overturn the Meese and Rogoff (1983) result despite 14 years of research. The efficient markets hypothesis is frequently misinterpreted as implying that exchange rate changes should be unpredictable; that is, exchange rates should follow a random walk. This is incorrect. There is, however, convincing evidence that interest rates are not good forecasters of exchange rate changes. According to Frankel (1996), this failure of exchange rate forecasting leaves two possibilities: Fundamentals are not observed well enough to allow forecasting of exchange rates. Exchange rates are detached from fundamentals by (possibly irrational) swings in expectations about future values of the exchange rate. These fluctuations in exchange rates are known as bubbles. Which of these possibilities is more likely? One clue is given by the relationship between exchange rates and fundamentals when expectations about the value of the exchange rate are very stable, as they are under a fixed exchange rate regime. A fixed exchange rate regime is a situation in which a government is committed to maintaining the value of its currency by manipulating monetary policy and trading foreign exchange reserves. Fixed exchange rate regimes are contrasted to floating regimes, in which the government has no such obligation. For example, most countries in the European Union had a type of fixed exchange rate regime, known as a target zone, from 1979 through the early 1990s. Fixed exchange rates anchor investor sentiment about the future value of a currency because of the government’s commitment to stabilize its value. If fundamentals, like goods prices, or expectations based on fundamentals, rather than irrationally changing expectations, drive the exchange rate, the relationship between fundamentals and exchange rates should be the same under a fixed exchange rate regime as it is under a floating regime. This is not the case. Countries that move from floating exchange rates to fixed exchange rates experience a dramatic change in the relationship between prices and exchange rates. Specifically, real exchange rates (exchange rates adjusted for inflation in both countries) are much more volatile under floating exchange rate regimes, where expectations are not tied down by promises of government intervention. The above figure illustrates a very typical case: When Germany and the United States ceased to fix their currencies in March 1973, the variability in the real $/DM exchange rate increased dramatically. This result suggests that, contrary to the efficient markets hypothesis, swings in investor expectations may detach exchange rates from fundamental values in the short run.

LITERATURE REVIEW-

1>

Almeida, Alvaro, Charles Goodhart & Richard Payne (1998), “The Effects of Macroeconomic “News” on High Frequency Exchange Rate Behaviour”, Journal of Financial and Quantitative Analysis, vol. 33, no. 3 (September), pp. 383-408; revised version of LSE Financial Markets Group Discussion Paper, no. 258(February 1997)

This paper studies the high frequency reaction of the DEM/USD exchange rate to publicly announced macroeconomic information emanating from Germany and the U.S. The news content of each announcement is extracted using a set of market expectation figures supplied by MMS International. By using data sampled at a high (5 minute) frequency we are able to identify systematic impacts of most announcements on the exchange rate change in the 15 minutes post-announcement. The impacts of “news” on the exchange rate, however, can be seen to lose significance very quickly when the observation horizon for the exchange rate is increased, so that for most announcements there is little effect of “news” on the exchange rate change by the end of the three hours immediately after release. Both the responses to U.S. and German “news” are broadly consistent with a monetary authority “reaction function” hypothesis, i.e., the market expects the Fed or the Bundesbank to respond to “news” on increased real activity, for example, by raising short term interest rates in order to head off the possibility of future inflation. Further, the use of German data allows us to examine two questions the previous literature could not tackle, because, unlike U.S. announcements, German announcements are not scheduled. First, we show that the time-pattern of the reaction of the exchange rate to the U.S. scheduled announcements is different from the reaction to the German non-scheduled announcements, the former being much quicker. Second, we are able to examine the effect on the exchange rate change of the proximity of other events to the announcement. Results show that German “news” is most influential when released just prior to a Bundesbank council meeting. Finally, subsidiary results demonstrate the efficiency of the intra-day FX market with respect to these announcements and map the pattern of volatility these releases cause.

2>

Andersen, Arrogant Torben & Tim Bollerslev (1997b), “Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns”, Journal of Finance, vol. 52, no. 3 (July), pp. 975-1005; revised version of NBER Working Paper, no. 5752 (September 1996)-

Recent empirical evidence suggests that the long-run dependence in financial market volatility is best characterized by a slowly mean-reverting fractionally integrated process. At the same time, much shorter-lived volatility dependencies are typically observed with high-frequency intradaily returns. This paper draws on the information arrival, or mixture-of-distributions hypothesis interpretation of the latent volatility process in rationalizing this behaviour. By interpreting the overall volatility as the manifestation of numerous heterogeneous information arrivals, sudden bursts of volatility typically will have both short-run and long-run components. Over intradaily frequencies, the short-run decay stands out most clearly, while the impact of the highly persistent processes will be dominant over longer horizons. These ideas are confirmed by the empirical analysis of a one-year time series of intradaily five-minute Deutschemark- U.S. Dollar returns. Whereas traditional time series based measures for the temporal dependencies in the absolute returns give rise to very conflicting results across different intradaily sampling frequencies, the corresponding semi parametric estimates for the order of fractional integration remain remarkably stable. Similarly, the autocorrelogram for the low-pass filtered absolute returns, obtained by annihilating periods in excess of one day, exhibit a striking hyperbolic rate of decay.

3>

Baestaens, Dirk-Emma, Willem Max van den Bergh & H. Vaudrey (1995), “The Marginal Contribution of News to the DEM/USD Swap Rate”, Proceedings of the First International Conference on High Frequency Data in Finance, 29-31 March, Olsen & Associates, Zurich, vol. 3-

This paper attempts to estimate the return on the DM/USD money market swap rate by both a linear regression and nonlinear neural network model. Since all variables strongly exhibited an hour of the (statistical) week effect both within- and out-of-sample, variables have been adjusted to remove this effect. The residual return pattern then is mainly driven by strongly negative autocorrelated lagged returns as well as by the “impact effect” of Reuter’s Money Market Headline news flashes. This effect has been measured by pairing standardised news sentences to successive return patterns in the train set and applying this information to predict the residual return out-of-sample. Some news flashes systematically generate positive (negative) residual returns. The set of 51,000 standardised news sentences established during the first six months accounted for most news flashes occurring during the second half of the dataset. News flashes therefore display a sufficiently systematic pattern to be useful for prediction. The neural network model outperforms the regression model on the basis of the standard mean squared error again highlighting the fact that nonlinear modelling appears to be the most promising avenue to deal with this high-frequency dataset.

TARGET AREA AND DATA SOURCE-

A major European economy (with Germany under consideration- if data is taken in the pre-Euro period or U.K.- if data is taken in the post-Euro period). The reason for choosing an European Economy is the relative stability with respect to their foreign exchange markets as in comparison to the U.S or Latin American Economies. Data Source and Frequency- Yet to be determined based on availability and suitability of data.

METHODOLOGY:-

This study is aimed at testing the weak and semi-strong form efficiency of the forex market in the target economy. Weak-form efficiency is examined using unit-root tests while semi-strong form efficiency is tested using co-integration and Granger causality tests and finally using variance versions in the form decomposition analysis while testing for technical efficiency The traditional testing efficiency equations are reviewed and a model is developed that incorporates Bayesian revisions in the form of devaluation expectations. A number of propositions regarding the pattern of the coefficients in efficiency testing equations are established. The results are confirmed by empirical estimation of the model for the forex market. Another mode of estimation is investigation of the relative market efficiency in financial market data, using the approximate entropy method(ApEn) method for a qualification of randomness in time series. For that we can use data for multiple time periods of two nations to the test the relative market efficiencies during crisis periods. A major bone of contention is to model the return series while testing for efficiency base on the Efficient Market Hypothesis (EMH). Based on the returns data we can conduct either a macro-econometric study (when we take the country’s trade balance as returns data) or a micro level one when we conduct a study on a particular firm engaged in the forex business. This will be determined at later stages depending on the availability as well as suitability of data.

Efficient Market Hypothesis Testing

In this note we re-examine the foreign exchange market efficiency hypothesis, which is a hotly debated topic in the area of international finance. It is basically the theory of informationally efficient markets applied to the foreign exchange arena. The present literature is far from conclusive and inconsistencies abound. With the genesis of the concept of nonstationarity and cointegration came a new approach to testing market efficiency. A multitude of procedures are available, but the standard methodology has been to examine the forward market unbiasedness hypothesis, which tests whether forward rates are unbiased and efficient estimators of the future spot rate. Acceptance of this hypothesis implies that the spot and forward foreign exchange rates have a tendency to move together over time, i.e., they are cointegrated in the Engle-Granger (EG 1987) sense. The estimated model is St+k = AZA±+AZA²-ft,k+Aµt+k -1 Where, st+k is the natural log of the future spot exchange rate k periods ahead, ft,k is the natural log of the k period ahead forward foreign exchange rate. If st+k and ft,k are I(1), i.e., nonstationary and integrated of order 1, then the necessary (weak form) and sufficient (strong form) condition for unbiasedness/market efficiency is the existence of a vector (a, AZA²) such that the residual series Aµt+k is stationary and (a,AZA² ) = (0,1). Stationarity of the residuals from the estimation of equation (1) would indicate that the spot and forward rates are cointegrated. This is what we refer to as weak form efficiency. In addition to this, if the parameter restriction of (a,AZA²)= (0,1) holds, then the forward rate can be called an unbiased and efficient predictor of the future spot rate, and we refer to this condition as strong form efficiency. EG propose a two-stage process in which we first estimate equation (1) by ordinary least squares (OLS) and then examine the stationarity of the residual vector Aµt+k. The problem is that the nonstationarity of the variables under consideration precludes an examination of the parameter restriction (a,AZA²) = (0,1). Phillips-Hansen (PH 1990) propose a fully modified (FM-OLS) method which corrects for both the long run endogeneity in the data and the asymptotic bias in the coefficient estimates, i.e., it can test for the parameter restrictions without imposing them. The weakness of this procedure is the assumption of no cointegration in the residual vector, a process which has low power against stable autoregressive alternatives with near unit roots. This is due to the fact that classical tests of unit roots in the residuals of the cointegrating regression (two variables will be cointegrated only if the residuals of the cointegrating regression are stationary) have a tendency to accept the null hypothesis of unit roots in the residual series (no cointegration) unless there is strong evidence against it. Thus, even if the root is close to unity (but not exactly equal to one), classical tests will still indicate the presence of unit roots in the residual series.

CONCLUSION-

Technical analysis is the most widely used trading strategy in the foreign exchange market. Traders stake large positions on their interpretations of patterns in the data. Economists have traditionally rejected the claims of Rational Expectations based on technical analysts because of the appealing logic of the Efficient Markets Hypothesis. More recently, however, the discovery of profitable technical trading rules and other evidence against efficient markets have led to a rethinking about the importance of institutional features that might justify extrapolative technical analysis such as private information, sequential trading, and central bank intervention, as well as the role of risk. The weight of the evidence now suggests that excess returns have been available to technical foreign exchange traders over long periods. Risk is hard to define and measure, however, and this difficulty has obscured the degree of inefficiency in the foreign exchange market. There is no guarantee, of course, that technical rules will continue to generate excess returns in the future; the excess returns may be bid away by market participants. Indeed, this may already be occurring. Continued research on high-frequency transactions data or experimental work on expectations formation may provide a better understanding of market behaviour. The Study will answer the question of whether the efficient market hypothesis is effectively applicable to the foreign exchange market.

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Testing For Efficiency Of Foreign Exchange Markets Finance Essay. (2017, Jun 26). Retrieved December 5, 2022 , from
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