Market Microstructure and Nonlinear Dynamics: Keeping Financial Crisis in Context

322 Pages·2014·4.88 MB·English

by  Gilles Dufrénot, Fredj Jawadi, Waël Louhichi (eds.)

Gilles Dufrénot
Fredj Jawadi
Waël Louhichi Editors
Market
Microstructure
and Nonlinear
Dynamics
Keeping Financial Crisis in Context
Market Microstructure and Nonlinear Dynamics
ThiSisaFMBlankPage
Gilles Dufre´not (cid:129) Fredj Jawadi (cid:129) Wae¨l Louhichi
Editors
Market Microstructure
and Nonlinear Dynamics
Keeping Financial Crisis in Context
Editors
GillesDufre´not FredjJawadi
GREQAM/DEFI UniversityofEvry
Aix-MarseilleUniversity Evry
LesMilles France
France
Wae¨lLouhichi
ESSCASchoolofManagement
Boulogne-Billancourt
France
ISBN978-3-319-05211-3 ISBN978-3-319-05212-0(eBook)
DOI10.1007/978-3-319-05212-0
SpringerChamHeidelbergNewYorkDordrechtLondon
LibraryofCongressControlNumber:2014944558
©SpringerInternationalPublishingSwitzerland2014
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Preface
Overview
The financial markets have experienced important and rapid developments since
the end of the eighties. Such an evolution has several causes. On the one hand,
themarketshavebeenliberalized,deregulated,andintegratedthankstotheincrease
of capital mobility and the volume of foreign investments in international equity
markets.Thederegulationoffinancialmarketsandthefavorablemonetarypolicies
implied a decrease in the investor’s risk premium and an increase in their invest-
ment appetite.On the otherhand,financialglobalizationwas accompanied by the
acceleration of market microstructure development. Indeed, the high-frequency
trading—that consists of trading securities in very short-time intervals—is
the major change that has characterized the financial markets over the last years.
TheintroductionofdifferentalternativetradingsystemsandtheEuropeandirective
MiFID (Market in Financial Instruments Directive) has improved the high-
frequency trading and the rapid development of financial systems and it also
increased the competition between financial markets. Also, several econometric
parametric and nonparametric techniques have been applied in order to better
capturethewholecharacteristicsofthefinancialassetreturnsandrisk.Accordingly,
theinvestorsarebeingabletointeractinmodernfinancialmarketswhileusingrecent
modelingtools.
From atheoretical viewpoint,this evolution has twoconsequences.Firstly, the
modern microstructure systemsarebeneficialastheyshouldenable toautomatize
transactions, reduce transaction costs, and facilitate the information access and
therefore increase the liquidity and to ensure financial stability (Brogaard et al.
2014; Hendershott and Riordan 2013; Hendershott et al. 2011). Secondly, the
application of recent econometric modeling should be helpful to better model the
financial data and to improve the forecasts of future events leading to effective
investmentsandportfoliochoiceandtoefficientmarkets.
Fromanoperationalviewpoint,theviabilityofafinancialmarketdependsonits
abilitytoattractandretainbothlistedfirmsandinvestors.Tobecompetitiveeach
v
vi Preface
financial market should optimize the confrontation between the supply of and
demand for capital to provide enough liquidity and to determine the appropriate
price at low costs. Therefore, one of the main missions of the market regulation
authorities is to define the operating rules and the market structure which reduce
the transaction costs and improve the liquidity as well as price efficiency. Market
microstructure theory provides a framework that can help understanding how
tradingrulesaffectthecharacteristicsofthefinancialmarketsintermsofefficiency,
liquidity,andinformationasymmetry.
From a practical viewpoint, the availability of high-frequency data enables
to improve the understanding of trading process, price formation, and traders’
behavior.Forexample,competitivepressurebetweenregulatedmarketswasampli-
fiedbytheemergenceofAlternativeTradingSystems(ElectronicCommunication
Networks, Multilateral Trading Facilities, dark pools, etc.), which led to a wave
of mergers and acquisitions in the financial markets in order to create synergies
and reduce the costs. In the early 2000s, the adoption of the Euro as a single
European currency has facilitated the merger of Amsterdam, Brussels, and Paris
stock exchanges and led to the creation of a single trading platform: Euronext.
Lisbon Stock Exchange and LIFFE joined the group in 2002. In 2007, after the
implementation of the European directive MiFID, Euronext and NYSE merged
to create the largest exchange group in terms of number of listed companies
and market capitalization. NYSE Euronext has adopted a new trading platform
(Universal Trading System), which connects all of its regulated markets: NYSE,
the derivatives market NYSE Liffe, and stock markets of Euronext Amsterdam,
Brussels, Lisbon, and Paris. Furthermore, NYSE Euronext has adopted a new
multilateraltradingfacility(NYSEArcaEurope)andadarkpool(Smartpool).
In such an environment, the European Directive MiFID—for example—aims
atstimulatingcompetition,ensuringtheinvestor’sprotection,encouragingtechno-
logical innovation, and reducing transaction costs. This directive states that a
financial asset can be traded on a regulated market and on a multilateral trading
facility to provide better execution. Accordingly, this new regulation framework
leadstothebreakingofregulatedstockmarketsmonopolies,thefragmentationof
theorderflow,andtheexistenceofseveralpricesforthesameasset.
Surprisingly the recent global financial crisis (2008–2009) showed the high
fragility of the financial systems and the failure of econometric tools to forecast
market downturns and market structure defaults. Indeed, in addition to financial
stability, the crisis implied a more general instability and important bankruptcies
and losses in several financial markets. Interestingly, this crisis had its root in the
mostdevelopedandimportantmarketintheworld:theUSmarket.Thisunexpected
first-rate breakdown of financial markets showed that something was getting
wrong (Barnett 2012). Economists and analysts started addressing several critics
about financial market regulation (Aglietta 2009), market pricing (Shiller 2009),
high-frequencytrading,andmarketquality(BiaisandFoucault2014),etc.
The main objective of this volume is to focus on the recent developments on
high-frequencytradingandnonlineareconometricmodelingforfinancialdata.The
chapters are selected among papers that were presented at the First International
Preface vii
WorkshoponMarketMicrostructureandNonlinearDynamics(Evry(France),June
13–14, 2013). They discuss different topics, apply recent techniques, use recent
data,andprovideinterestingfindings.Theyareorganizedintwoparts.Thefirstpart
includes six chapters about Market Microstructure. These studies focus on the
evolution of market structure and systems and analyze data in calm and crisis
periods. They develop exciting challenges about market structure reforms and
innovations.
The second part focuses on Nonlinear Dynamics and consists of five chapters.
They propose different empirical investigations highlighting new tools to capture
asymmetricandnonlineardynamicsanddetectoutliers inordertoimprovefinan-
cialdatamodelingandforecasting.
Market Microstructure
The first chapter by Carole Gresse (University of Paris Dauphine, France), titled:
“Market Fragmentation and Market Quality: The European Experience,” investi-
gates the effect of market fragmentation on Liquidity. The author proposes an
overview of the implementation of the MiFID in Europe in November 2007 and
discusses the impact of order fragmentation on liquidity and price quality. Using
different recent high-frequency data the author concludes to the absence of a
significant impact of order fragmentation on price quality, while suggesting that
thisrelationshipdependsonfrequencyandmarketdata.
The second chapter, entitled “Pre-trade Transparency and the Information
Content of the Limit Order Book,” is coauthored by Huu Nhan Duong, Petko
Kalev, and Kevin Sun (University of South Australia, Australia). It studies the
effect of improved pre-trade transparency on the information content of the limit
order book. The investigation of pre-trade transparency is of great interest for
traders and regulators. This chapter is based on two natural experiments:
(1) when the Sydney Futures Exchange (SFE) increased the disclosure of limit
order book from the best bid and ask level to the best three price levels in 2001
and (2) when the SFE further increased the disclosure from the best three to
the best five price levels for selected contracts in 2003. The authors show that
the limit order book can provide significant information about future return and
volatility.Moreover,theempiricalresultsshowthatthelimitorderbookbecomes
moreinformativefollowingthetwoimprovementsinpre-tradetransparency.This
findingshowsabeneficialreductionofadditionalorderbookdisclosure.
“Trading Mechanisms in Financial Markets: A Comparison between Auction
and Dealership Markets” is the title of the third chapter by Moez Bennouri
(NEOMA Business School, France). This chapter investigates a question related
to the functioning rules of financial markets that differ according to market
concentration, order timing, order submission, etc. The author compares two
different market structures: centralized order-driven and fragmented quote-driven
markets.Thecomparison is based onseveral marketperformance criteria:market
viii Preface
viability,informationalefficiency,pricevariance,informedtradingaggressiveness,
andmarketliquidity.Theauthorfindsthatauctionmarketsarelesssensitivetothe
asymmetricinformationproblemandthatitexhibitsahigherlevelofinformational
efficiencythandealershipmarkets.
Chapters4and5dealwithmarketbehavioraroundpublicinformationreleases.
In the fourth chapter entitled “News Trader, Liquidity and Transaction Costs,”
coauthored by Timm Kruse (School of Mathematics, KIT, Germany), Edward
Sun (KEDGE Business School, France), and Min-Teh Yu (National Chia Tung
University,Taiwan),theauthorsproposeamodeltofindanoptimaltradingstrategy
for block market orders submitted by financial institutions around news releases.
Todothis,theycharacterize thepricedynamicthrougheitheraBrownianmotion
orgeometricBrownianmotionandusesimulationtechniquestochecktheperfor-
mance of their model while comparing the implemented trading strategy to a
benchmark of alternative trading strategies. They show that the performance of
their analytical solution is significantly better than the other alternative trading
strategies,especiallywhenthemarketturnstobeextremelybullishorbearish.
Chapter 5, entitled “What moves Euro-Bund futures contracts on Eurex?
Surprises!,” is coauthored by Franck Moraux (University of Rennes 1, France)
andArnaudRichard(VarianceArbitrageSAS,France).Usingrecentdatabases,this
study investigates the impact of public information on the Bund Futures contract.
The authors find a significant and instantaneous impact of unexpected macro-
economic news on price returns and volatility. While the news effect on Bund
returnsisinstantaneous,theeffectofvolatilityseemstobetimelydelayedreflecting
some asymmetry in the transmission of macroeconomic news. This also implies
thattheuseofnewsassociatedwithmacroeconomicvariablesenablestoimprove
euro-bundfuturedynamics.
In the last chapter of this first part, entitled “Individual Investors’ Trading
ActivitiesandPriceVolatility,”HuuNhanDuonga(MonashUniversity,Australia)
and Petko Kalev (University of South Australia Business School, Australia)
examine the volume–volatility relationship. This chapter investigates the effect
ofthenumberoftradesandaveragetradesize,institutionalandindividualtrading,
and order imbalance on volume–volatility relationship. Using a detailed data
from the Australian market, the authors highlight a positive relationship between
trading volume and volatility. The number of trades seems to have the most
significant impact on volatility. Finally, the authors find that individual trading
has a more significant role in explaining price volatility than institutional trading.
Suchfindingsimplythatindividualtradingvolumemightbeinformativetoforecast
changesinvolatility.
Nonlinear Dynamics
The second part of this volume includes five chapters examining the dynamics of
different financial markets (Stock Market, Exchange Market, Bond Market) and
Preface ix
applyingrecenteconometrictools.Theinvestigationoffinancialmarketsdynamics
over calm and turbulent periods enables the identification of different regimes
and implies the extension of econometric modeling to new specifications that
are more robust to capture time-varying statistical properties (non-normality,
asymmetry,structuralbreak,shift,nonlinearity,etc.).
Chapters 7 and 8 deal with Bond market. Chapter 7, entitled “Finance and
growth Causality: Empirical Evidence for Emerging Europe,” is proposed by
Iuliana Matei (University of Paris 1 Pantheon Sorbonne, France) and focuses
onbondmarket.UsingdynamicalpanelVECM(VectorErrorCorrectionModel),
the author investigates dynamic causal relationships between the government
bond market and growth rates for ten European non-EMU countries over the
period 2002–2012. This issue is of relevant interest because it implies a real
challengeintheliteratureonthedifferenttransmissionchannelsbetweeneconomic
growthandfinance.Theauthorfindssignificantandnegativerelationshipsbetween
growth rates and term spreads. This chapter investigates this hypothesis through
differentsubperiods(beforeandafterthesubprimecrisis)andsuggeststherobust-
nessofherresults.
Chapter 8, entitled “Anticipated Macroeconomic Fundamentals, Sovereign
Spreads and Regimes Switching: the Case of Euro Area,” by Gilles Dufre´not
(Banque de France, CEPII, and AMSE, France), Olivier Damette (University of
Loraine, France), and Philippe Froute´ (University of Paris-Est Creteil, France)
focuses on the relationship between expected macroeconomic fundamentals and
sovereignspreads.Theauthorsshowthattherelationshipbetweenthesevariablesis
regime switching. They highlight several multiple “equilibrium relationships”
between spreads and macroeconomic variables through the implementation of
recent methodology associated with Time-Varying Probability Markov-Switching
Models. The latter have the advantage that it captures time-varying bond spread
reactiontoexpectedchangesinpublicdeficitdebtratiosorinflation.Thenoveltyof
this study consists in applying recent technique to sovereign bond markets. The
main finding is that macroeconomic news may be useful to forecast structural
changesinbondreturns.
The volatility dynamics through the use of high-frequency data is investigated
in Chapters 9 and 10. Chaker Aloui (University of Economic and Management
Sciences of Tunis, Tunisia) and Abdelaziz Krim (University of Economic and
Management Sciences of Tunis, Tunisia) coauthor Chapter 9, entitled “Impact of
Anti-Crisis Measures on the Volatility of the Stock Market Stress Index in the
Euro Zone.” Their study focuses on an interesting current topic associated with
financialinstability.Todothis,theauthorschecktheeffectofthecrisisonthestock
market volatility. They measure volatility through an EGARCH (Exponential
Generalized Autoregressive Conditional Heteroskedasticity Models) model and
proposeanewmeasureforstockmarketstressindexusefulintimesofdestabilizing
stockmarkets.
Chapter10entitled“Shift-VolatilityTransmissioninEastAsianEquityMarkets:
New Indicators” by Marcel Aloy (AMSE, France), Gilles De Truchis (AMSE,
France), Gilles Dufre´not (Banque de France, CEPII, and AMSE, France), and