Still after more than thirty years of free floating exchange rates, large parts of exchange rate dynamics remain a puzzle. As this book shows, much progress has been made in explaining exchange rate movements over longer horizons. It also shows, however, that short-run movements are far more challenging to explain. The book is based upon a variety of papers, many of them released recently. A key aspiration of the literature has always been not only to explain past exchange rate behavior but also to forecast out of sample and to compare it to the simple random walk outcome. Here some development has been made after Meese and Rogoff’s (1983) truculent verdict of the performance of common exchange rate models. By means of empirical analysis and descriptive statistics this book further supports the established long-run relationships between exchange rates and fundamentals such as expected productivity growth, real GDP growth, domestic investment, interest rates, inflation, government spending, and current account balances. It finds that these fundamentals affect the exchange rate to varying degrees over time. Turning to short-term exchange rate dynamics, it turns out that a different set of forces is at play. The key to explaining short-run movements is to be found in an extensive micro-foundation that factors in a pronounced heterogeneity among market participants and information asymmetries, as well as the possibility of sudden shifts in sentiment, beliefs, and the degree of risk aversion. Promising results have been obtained by order-flow analysis and high frequency data. Also, the consideration of chartism and speculators facilitates understanding for otherwise puzzling exchange rate movements. The last attempt to tackle the understanding of exchange rate behavior is the use of frequency domain analysis and in particular spectral analysis which tries to track down any cyclical patterns in the various moments of time series. And as we shall see forex indeed incorporates cycles as well.

Text Sample: Chapter 3.4.1, Empirical Evidence: It has become clear in the previous chapter that not all information is publicly available. Apart from the macroeconomic-related news there exists microeconomic-related information only available to some agents. Institutional portfolio rebalancing, hedging and liquidity demands, as well as shifts in risk appetite and expectations are examples and consequences of such private information which leaks out to the market via order flow and can, therefore, only be observed indirectly and delayed by all agents on the market. Generally, order flow is defined as the net difference between buyer-initiated trades and seller-initiated trades during some interval. Consequently, it can indicate a direction of trade for a currency. In fact, it is not even necessary that private agents possess superior information. If they only trade out of allocational motives like export transactions or earnings repatriation, the resulting cumulated transaction flow will convey information about the economy and cause agents to revise their expectations about fundamentals. A survey among professional traders and fund managers conducted by Gehrig and Menkhoff provides evidence that after technical analysis (attached weight of importance: 40.2%) and fundamental analysis (36.3%), the analysis of order flow (23.5%) is a third type of information widely used.In addition, more than 62 percent of participants believe that order flow delivers useful information for exchange rate movement from intraday to a few days only, while 15 percent do so for horizons longer than 2 months. Lately, various order flow data sets have been examined and overwhelmingly contributed to the understanding of short-run exchange rate behavior. For example, Evans and Lyons (2002) analyze a four-month sample between May 1st and August 31st, 1996 which covers worldwide direct interdealer trades on Reuters Dealing 2000-1 trading system2 for DM/USD and JPY/USD. For each 24 hours order flow is expressed as a cumulated unit value. For instance, if a purchase (sale) for the DM/USD ask (bid) quote is initiated, then order flow is +1 (-1). Specifically they regress where is the change of interdealer order flows between yesterday and today. The results can be seen in table 28. Indeed, order flow is able to explain 64 percent of daily changes in log mark/dollar and 46 percent of log yen/dollar exchange rate movement. The positiveindicates that net dollar purchases lead to a higher exchange rate, i.e. a dollar appreciation. Alsois significant and positively signed and, therefore, in line with UIP. The magnitude of 2.14 forof the DM/USD exchange rate means that if on any particular day there occur 1,000 more dollar purchases than sales, the dollar will on average appreciate by 2.14 percent. In absolute terms, considering an average trading size of .9 million, this means that a billion excess of dollar purchases leads to an exchange rate appreciation of 0.54 percent (=2.1/3.9). Different versions of equation 34 (as also shown in table 28) show that order flow really is a driving force of short run exchange rate dynamics, and that, generally, the absolute nominal interest rate differential (but not its change) turns out to be insignificant. The finding that prices increase with buying pressure is a seemingly natural and causal relationship. However, for exchange rates it has conceptual implications since traditional macro models do not necessarily or sufficiently demand actual trades for exchange rate movement! Evans and Lyons present research work over an extended period of time and a different data set. It spans from 1993:1 to 1999:6 and comprehends all of Citibank's end-user order flow, meaning nonfinancial corporations, investors, and leveraged traders (such as hedge funds or proprietary trading desks) in the USD/EUR exchange rate spot and forward market. Citibank's market share is in the 10-15 percent range. Before attempting to forecast exchange rates over one to twenty trading days based on order flow, Evans and Lyons basically confirm the results of Meese and Rogoff over their considered time horizon. They find that forecasting with help of the interest rate differential produces larger MSE than the naïve random walk. However, they clearly beat the random walk with help of two different order flow based microstructure models. The first model is based on aggregated order flow from the six end-user segments of U.S. and non-U.S. market transactions by the three end-users mentioned earlier: The second microstructure model is based on disaggregated order flow from each segment : The results, as shown in table 29, show that the aggregated model beats the random walk at forecast horizons of 10 trading days or longer at the one percent significance level with a minimum MSE-ratio of 0.90 at 20 days. The disaggregated model even beats the random walk from one day forecast horizon onwards with a minimum MSE-ratio of 0.81 at 20 days. Generally, (as always) the predictability accuracy increases as the horizon rises. At 20 days, the disaggregated model accounts for almost 16 percent of the sample variance. Rime et al. (2007) contribute further evidence in line with Evans and Lyons. They analyze a data set which is obtained from the Reuters trading platform (D2000-2) but which covers a whole year from 2004:02:13 to 2005:02:14 for the USD versus the three major currencies EUR, JPY, and GBP during the main trading hours between 07:00 and 17:00 GMT. In a regression equal to equation (34), they find highly significant and positivefor the contemporaneous order flow of all currencies, among which the impact is highest for the JPY with a coefficient of 12.4 and smallest for GBP with a coefficient of 1.36. A detailed overview can be found in table 30. Further on, they show that innovative shocks to fundamentals (as calculated from the Money Market Survey, MMS) have mostly significant (at ten percent) effects on order flow, explaining up to 18 percent of its daily variance. Also, this news has significant effects on the exchange rate itself, confirming earlier presented evidence in chapter 3.3.1. Interestingly, regressing both news and order flow onto the daily change of the exchange rate significantly enhances the explanatory value by up to 7.7 times (as in case of the JPY) as opposed to regressing them individually. Precise results can be found in table 31. Once again, this indicates that macroeconomic news influence exchange rates not only directly but also indirectly because order flow gradually conveys information on heterogeneous beliefs about these fundamentals. Noticeably, order flow and exchange rates also show high cross-correlation across currencies. As table 32 shows, daily exchange rate returns correlate positively with changes of other currency pairs in a range between 0.20 (for ) and 0.53 (for ). Partly, of course, this is due to the same denomination in U.S.-dollars. In further analysis, Rime et al. test if three different micro forecast models can outperform the random walk and if positive out-of-sample returns could have been generated after correcting for transaction cost and risk aversion.

• exchange rates

• random walk

• underlying fundamentals

• PPP

• UEP

• speculation

• chartism

Falkmar Butgereit, Jahrgang 1983, nahm nach seinem Abitur in Berlin in 2003 sein Studium zum Diplom Volkswirt auf. Volkswirtschaftliche Zusammenhänge im Generellen und Wechselkurse im Speziellen übten stets eine große Faszination auf ihn aus und bewegten ihn dazu dieses Buch mit Abschluss seines Studiums in 2009 an der J.W. Goethe Universität Frankfurt am Main fertig zu stellen. Mit diesem Werk kam er unter die finalen Top 10 im Rahmen des DZ BANK Karrierepreises 2010. Bereits während seines Studiums arbeitete Butgereit als Analyst in der Hedge Fund Branche. Heute arbeitet er in einer großen Bank im Investment Banking und profitiert unter anderem von dem Know How und Research-Ergebnissen dieses Buches.