Exchange Rate Determination Puzzle - Long Run Behavior and Short Run Dynamics

Diplomarbeit von Falkmar Butgereit

Introduction:

As the foreign exchange rate market operates twenty-four hours a day and seven days a week it can be described as a global marketplace trading in continuous time. The importance of this market place on weal and woe of economies and agents cannot be overestimated. Long lasting disputes about exchange rate over- and under-evaluation between countries (as most prominently the case between China and the USA) and its implications for international trade, growth rates of economies, unemployment levels, financial money flows, and so forth illustrate this point.

As reported by the Bank of International Settlement in its triennial Central Bank Survey 2007, covering 54 countries and jurisdictions, the daily average foreign exchange turnover as of April 2007 has reached a mind-staggering $3.21 trillion. This amount marks an increase of 69 percent compared to the $1.97 trillion three years earlier and highlights the still increasing importance of the exchange rate markets. The U.S. dollar is by far the most important currency as it is involved in 86 percent of all transactions amounting to some $2.7 trillion per day. This is by far bigger than the volume of U.S. international trade in goods and services which for the month April 2007 amounted to (imports + exports) $317.5 billion.1 Indeed, only 17 percent of exchange market turnover has been reported to occur with non-financial customer counterparties, while 43 percent of transactions occur between reporting dealers (i.e. the interbank market) and 40 percent occur between reporting and non-reporting financial institutions (e.g. hedge funds, mutual funds, pension funds, insurance companies). Accordingly, more than 2/3 of the turnover was traded as derivatives such as foreign exchange swaps, outright forwards, or options, while only 1/3 constituted spot rate transactions.

These are important facts to consider when talking about forces of exchange rate determination. On ground of these figures one may reasonably explain why old-fashion standard models like the monetary model or purchasing power parity may only hold in the very long run and exchange rate movements may be much more subject to trades based on heterogeneous expectations incurred by investors, speculators and market makers. Particularly at the short-run exchange rates exhibit considerably greater volatility than macroeconomic time series leaving an impression of noisy and chaotic behavior.

Throughout this work it will become evident that heterogeneous beliefs and actions of market participants are the key to understand short-run exchange rate dynamics from daily to monthly horizons. Over longer horizons of one month and longer standard fundamentals like money, inflation, productivity, interest rates and output will shimmer through and push the exchange rate towards a fair equilibrium value.

This thesis is structured as to firstly looking at exchange rate driving forces over longer periods. Afterwards in chapter 3 it will start by examining the low predictive power of standard macroeconomic exchange rate models and present more recent successes in forecasting and explaining exchange rates. It continues with analyses of chart-technique, impact of news, and order flow which all constitute important building blocks of exchange rate determination and prediction over shorter horizons. Part 4 presents some more evidence on the non-linear behavior of exchange rates and the relationships between today's exchange rate and its historical movements as well as fundamentals (particularly interest rates) at different frequencies. Chapter 5 concludes.

Table of Contents:

1.	Introduction	3
2.	Long-Run Exchange Rate Behavior	4
2.1	Purchasing Power Parity	4
2.2	The Simple Monetary Exchange Rate Model	9
2.3	Long-Term Cycles	13
2.4	The Macroeconomic-Balance Approach	17
3.	Short-Run Exchange Rate Dynamics	19
3.1	Only Random Dynamics?	19
3.2	Technical Traders and Speculators	26
3.2.1	Evidence of the Role of Chartists and Modeling Their Behavior	26
3.2.2	Views of Practitioners	30
3.2.3	Chart-Technique Predicting Future Movements?	32
3.3	The Impact of News	36
3.3.1	Immediate Response	36
3.3.2	Delayed Response	41
3.4	Order Flow and Investor Heterogeneity	45
3.4.1	Empirical Evidence	45
3.4.2	Modeling Order Flow	51
3.4.3	Uncovered Equity Parity	53
4.	Spectral Analysis	58
4.1	The Approach and Numerical Analysis	58
4.2	Graphical Analysis	63
5.	Conclusion	69
	Appendix	73
	References	110
	Auxiliary Means and Declaration of Honor	115

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 $3.9 million, this means that a $1 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.