Intradaily Exchange Rate Movements

172 Pages·2000·4.802 MB·English

by  Dominique M. Guillaume (auth.)

INTRADAILY EXCHANGE RATE MOVEMENTS
Intradaily Exchange
Rate Movements
by
Dominique M. Guillaume
Catholic University ofL ouvain, Belgium and
Oxford University, United Kingdom
SPRINGER SCIENCE+BUSINESS MEDIA, B.V.
A c.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN 978-1-4613-7089-5 ISBN 978-1-4615-4621-4 (eBook)
DOI 10.1007/978-1-4615-4621-4
Printed on acid-free paper
All Rights Reserved
© 2000 Springer Science+Business Media Dordrecht
Originally published by Kluwer Academic Publishers in 2000
Softcover reprint of the hardcover 1s t edition 2000
No part of the material protected by this copyright notice may be reproduced or
utilized in any form or by any means, electronic or mechanical,
including photocopying, recording or by any information storage and
retrieval system, without written permission from the copyright owner.
Contents
Preface ix
Acknowledgements and Copyrights xv
1 A Typology of Foreign Exchange Rates Models 1
1.1 Models with fixed point .... 5
1.2 Models with multiple equilibria 8
1.3 Models with chaotic attractors 11
1.4 Models with multiple attractors . 12
1.5 Models with no attractor .. . . 12
1.6 Models with no attractor but some structure 13
2 From the Bird's Eye to the Microscope: A Survey of
New Stylised Facts 15
2.1 Introduction .................. . 15
2.2 Description of the Foreign Exchange Market . 17
2.3 Definition of the Variables of Interest . 21
2.3.1 Definition 1: the price ..... 22
2.3.2 Definition 2: the change of price 24
2.3.3 Definition 3: the volatility . . . . 24
2.3.4 Definition 4: the relative spread. 25
2.3.5 Definition 5: the tick frequency . 26
2.3.6 Definition 6: the volatility ratio . 27
2.3.7 Definition 7: the directional change frequency . 28
2.4 Stylised facts . . . .. . ....... . . . ... . . 29
v
VI CONTENTS
2.4.1 Distribution . . . . . . . 29
2.4.2 Price formation process 33
2.4.3 Heterogeneous Structure . 36
2.5 Conclusion 50
2.6 Appendix . 55
2.6.1 Appendix 1 55
2.6.2 Appendix 2 57
3 Chaos in the Foreign Exchange Markets 59
3.1 Methodology . . . . . . . . . . . . . . . . . . . .6 1 . .
3.1.1 Reconstruction of the embedding phase space 61
3.1.2 Computation of the fractal dimension . . . . 66
3.1.3 Detection of chaos: a summary of the steps . 67
3.2 Results... . 69
3.2.1 Data . 69
3.2.2 Results 70
3.3 Conclusion . . 76
4 Sources of Nonlinearities in the Foreign
Exchange Markets 81
4.1 Introduction. 81
4.2 Methodology 82
4.2.1 Time scale transformations 82
4.2.2 Tests . 85
4.3 Results . . . . 86
4.3.1 The correlogram study . 87
4.3.2 The BDS-test results . 90
4.4 Conclusion ... . . . ... . 93
5 On the Intradaily Performance of G ARCH Processes 95
5.1 Introduction . ... . .. 95
5.2 Description of the data. 97
5.3 Alternative time scales 99
5.4 Results. . .. . .... . 103
CONTENTS vii
5.4.1 Impact of the seasonal heterogeneity . · 106
5.4.2 Impact of the temporal heterogeneity .107
5.4.3 Stability across the sample period .109
5.5 Conclusion · 110
5.6 Appendix . . 113
6 Do Technical Trading Rules Generate Profits? 117
6.1 Introduction.... .. . . 117
6.2 Data and methodology. . 119
6.2.1 Data..... . . . 119
6.2.2 The basic trading rule . 121
6.2.3 Accounting for transaction costs . 124
6.3 Results. . ........ . ...... . . . 125
6.3.1 Brief summary ofresults from sample (a) . 130
6.3.2 Results from sample (b) . 133
6.4 Conclusion · 138
Bibliography 141
Index 161
Preface
In recent years, a broad consensus has emerged among economists
that the different vintages of macro-economic models have largely failed
to account for the foreign exchange rates movements in the short to
medium run, that is, from daily up to quarterly or even yearly frequen
cies (see e.g. Frankel and Rose (1995) for a recent survey). Only in
the very long run do these models appear to have a predictive power
superior to a random-walk, which is the simplest benchmark one could
consider (Mark, 1995).
Therefore, rather than to try linking foreign exchange rates with
fundamentals, another route pursued by researchers was to question the
rational expectation hypothesis assumed in these models. Using sur
veys amongst traders, they brought to light the heterogeneity of their
expectations (e.g., Takagi, 1991). More important even, it seems that
traders have different expectations depending on the time horizon at
which they are looking. In the short run, expectations tend to be desta
bilising whereas in the long run they are regressive towards a long run
fundamental equilibrium. This is quite understandable as it also ap
peared that traders' preferred category of models in the short run is
technical analysis, a broad class of techniques that extrapolate the in
formation contained in past prices (Frankel and Froot, 1990; Allen and
Taylor, 1992). The use of technical analysis as a systematic explana
tion of the behaviour of exchange rates movements is, however, still a
matter of debate because it would imply the existence of unexploited
arbitrage opportunities. On the other hand, it has gained some acad
emic acceptability since it was rationalised in theoretical models combin
ing fundamentals and extrapolative expectations (De Long et al., 1990;
De Grauwe and Dewachter, 1993). Some simulation studies have also
demonstrated the potential profitability of technical analysis (Levich and
Thomas, 1992).
Another route followed by researchers was to investigate the statis
tical process of foreign exchange rate returns. Indeed, foreign exchange
IX
x
rate returns do not have to be identically and independently distrib
uted as in a random walk to follow a martingale process. Dependencies
could come through the second moment or even higher moments with
out implying predictability of the mean. As a matter of fact, already in
the 60s, Mandelbrot (1963) had shown that periods of quiescence tend
to be followed by periods of quiescence rather than periods of turbu
lence and vice-versa. This clustering of the volatility process was first
made endogenous in the Auto-Regressive Conditional Heteroskedastic
process (ARCH) by Engle (1982) and its generalised version by Boller
slev (1986). The ARCH model has now expanded into a whole family
of models (Bollerslev et al., 1992) as further properties of the volatil
ity process, such as its long run memory, were unveiled. Though, an
economic explanation for the clustering of volatility is yet to be given.
Debate also arose on whether the statistical process underlying the
distribution of foreign exchange rates returns was more akin to, for in
stance, the ARCH process, a Student-Tor a Cauchy distribution. These
developments are especially important if one considers foreign exchange
rate returns from a portfolio optimisation perspective where the first two
moments are the central parameters of interest, the mean representing
the average return and the variance the risk involved.
The next natural step to broaden the understanding of foreign ex
change markets was to look at intradaily foreign exchange movements
since more than 70% of the 1.4 billion USD average foreign exchange
daily trading volume takes place between traders who close their posi
tion at the end of the day (B.I.S., 1993). However, it was not before the
very end of the eighties that research on the intradaily exchange rates
was begun, mainly due to technical obstacles. These obstacles are quite
formidable and range from the actual purchase of the data from data
vendors such as Reuters and their massive storage on computers, to the
transformation and filtering of the data into clean time series that can
be used. Fortunately, very much at the same time as Goodhart (1989)
published the first paper using a series of two weeks of intradaily foreign
exchange rates, Olsen & Associates, a Swiss private research institute,
Xl
had made the necessary technological, human, and indeed capital invest
ment required for a more systematic collection of this data. When the
work contained in this book was started in 1993, it benefited from the
enormous generosity of Olsen & Associates who shared their data which
by then covered a period of seven years, that is, a couple of millions of
data points. An extensive study of this data set has resulted in the dif
ferent chapters of this book, some of which were written in collaboration
with the Olsen & Associates research team.
The research covered in this book has two main purposes: the sys
tematic study of the characteristics of intradaily data and an empirical
investigation using intradaily data of the three approaches to the mod
elling of foreign exchange rates outlined above.
In an introductory chapter, a more systematic attempt to classify
the successive waves of foreign exchange rate models is proposed using
a typology based on the type of asymptotic equilibria.
The first objective of the book is then addressed to in the second
chapter which surveys the main stylised facts on the intradaily foreign
exchange rates . These facts provide a partial answer to some of the
questions raised above. For example, thanks to the extremely large
number of data available a study of the tails of the distribution could
help categorise the distribution of exchange rates returns without falling
into the non-negligible small sample bias usually associated with such
studies. The nature of the volatility clustering captured by the Auto
Regressive Conditional Heteroskedastic family of models could partly be
understood in terms of the interaction between different markets. The
relevance of macro-economic modelling could be tested in a nonlinear
dynamic framework which does not imply predictability per se. And, the
very short-lived impact of macro-economic news announcements could
be verified.
The analysis of this extremely large data set of intradaily foreign ex
change rates has also uncovered new regularities of the foreign exchange
rates. For example, parameters could not be disaggregated from daily
frequency estimates into intradaily estimates as predicted theoretically
Xli
for a homogenous GARCH process, a hint at the presence of a different
expectation process at the intradaily frequency than, say, at the daily or
even monthly frequencies. On the other hand, intradaily data revealed
the existence of a fractal law that links the average absolute foreign
exchange price changes measured at different frequencies to these fre
quencies in a very different way from the Gaussian, Student-T, Cauchy
or GARCH distributions. The seemingly random large changes of the
persistence parameters of the volatility in the Auto- Regressive Condi
tional Heteroskedastic model over relatively long periods is consistent
with important structural breaks which are yet to be explained in terms
of changes of the underlying fundamentals, institutions, policy decision
rules or contagion effects.
Another important issue is the price formation process that takes
place every other couple of seconds or minutes, also termed 'market
microstructure' as first coined by Garman (1976). Whereas there is a
huge theoretical and empirical literature on the topic for stock markets
(see, for example, (O'Haara, 1995), progress in the understanding of the
price formation process of the foreign exchange markets has been limited
partly due to institutional differences from stock markets and partly due
to data limitations. For instance, foreign exchange markets differ from
most of stock exchange markets in that they are decentralised rather
than centralised markets which prevents the easy transposition of stock
markets models. Another difference from stock markets is that foreign
exchange markets are virtually continuously open around the world. As
a consequence, whereas stock market micro- structure explanations for
the widening of the bid-ask spread at the end of the trading day may be
valid for one particular market, say Europe, they cannot account at the
same time for the absence of widening of this bid-ask spread in another
simultaneously opened market, say New York (see, for example, Hsieh
and Kleidon, 1996).
In common with most other studies, our data set has important
shortcomings for tests of microstructure hypotheses in that it is consti
tuted of indicative quotes rather than transaction prices (which overes-