Regime-Based Asset Allocation

126 Pages·2014·3.48 MB·English

by  Peter Nystrup

M.Sc. Thesis
MasterofScienceinEngineering
Regime-Based Asset Allocation
Do Profitable Strategies Exist?
Peter Nystrup
KongensLyngby
July2014
TechnicalUniversityofDenmark
DepartmentofAppliedMathematicsandComputerScience
Matematiktorvet,Building303B
2800KongensLyngby,Denmark
Phone[phone]
[email protected]
www.compute.dtu.dk
Short Contents
ShortContents i
Abstract iii
Resumé v
Preface vii
Acknowledgements ix
Acronyms xi
Contents xiii
1 Introduction 1
2 IndexData 11
3 Markov-SwitchingMixtures 27
4 StrategicAssetAllocation 61
5 Regime-BasedAssetAllocation 71
6 SummaryandConclusion 83
References 87
A R-code 93
B ParameterEstimates 103
C AdditionalFiguresandTables 107
ii
Abstract
Regime shifts present a big challenge to traditional strategic asset allocation,
demanding a more adaptive approach. In the presence of time-varying invest-
ment opportunities, portfolio weights should be adjusted as new information
arrives. Regime-switching models can match the tendency of financial markets
to change their behavior abruptly and the phenomenon that the new behavior
oftenpersistsforseveralperiodsafterachange. Theyarewellsuitedtocapture
the stylized behavior of many financial series including skewness, leptokurtosis,
volatility persistence, and time-varying correlations.
Thisthesisbuildsonthisempiricalevidencetodevelopaquantitativeframework
forregime-basedassetallocation. Itinvestigateswhetherregime-basedinvesting
can effectively respond to changes in financial regimes at the portfolio level
in an effort to provide better long-term results when compared to more static
approaches. Thethesisextendspreviousworkbyconsideringbothdiscrete-time
and continuous-time models, models with different numbers of states, different
univariateandmultivariatestate-dependentdistributions,anddifferentsojourn
timedistributions. Out-of-samplesuccessdependsondevelopingawaytomodel
the non-linear and non-stationary behavior of asset returns.
Dynamicassetallocationstrategiesareshowntoaddvalueoverstrategiesbased
onrebalancingtostaticweightswithrebalancinginitselfaddingvaluecompared
tobuy-and-holdstrategiesinanassetuniverseconsistingofaglobalstockindex,
a global government bond index, and a commodity index. The tested strate-
giesbasedonanadaptivelyestimatedtwo-stateGaussianhiddenMarkovmodel
outperform a rebalancing strategy out of sample after accounting for transac-
tion costs, assuming no knowledge of future returns, and with a realistic delay
between the identification of a regime change and the portfolio adjustment.
Keywords: Regime switching; Markov-switching mixtures; Non-linear Time
SeriesModeling;DailyReturns;Adaptivity;Leptokurticdistributions;Volatility
clustering; Dynamic asset allocation.
iv
Resumé
Regimeskift udgør en stor udfordring for traditionel strategisk aktivallokering,
da de sætter krav til en mere adaptiv tilgang. Ved tilstedeværelsen af tidsvari-
erende investeringsmuligheder bør porteføljevægte opdateres, efterhånden som
nyinformationkommertil. Regimeskiftsmodellerkanmatchefinansiellemarked-
ers tendens til pludseligt at skifte opførsel og det fænomen, at den nye opførsel
ofte varer ved længe efter et skift. De er velegnede til at fange den stiliserede
opførsel, der er kendetegnende for mange finansielle serier, herunder skævhed,
leptokurtosis, volatilitetsklumpning og tidsvarierende korrelationer.
Formålet med denne afhandling er at udvikle en kvantitativ ramme for regime-
baseretaktivallokering. Detundersøges,hvorvidtregimebaseretinvesteringkan
reagerepåeffektivvispåændringerifinansielleregimerpåporteføljeniveaumed
detformålatskabebedrelangsigtederesultatersammenlignetmedmerestatiske
tilgange. Afhandlingenudvidertidligerestudiervedatinkluderemodelleribåde
diskret og kontinuerttid, modeller med forskelligeantalregimer, forskelligeuni-
variate og multivariate regimebetingede fordelinger og forskellige opholdstids-
fordelinger. Out-of-sample succes afhænger af udviklingen af en model, der
beskriver finansielle afkasts ikke-lineære og ikke-stationære opførsel.
Det bliver vist, at dynamiske aktivallokeringsstrategier tilfører værdi sammen-
lignetmedstrategierbaseretpårebalanceringtilstatiskevægte. Rebalanceringi
sigselvtilførerværdisammenlignetmedkøb-og-holdstrategierietaktivunivers,
der består af et globalt aktieindeks, et globalt statsobligationsindeks og et rå-
vareindeks. De testede strategier, baseret på en adaptivt estimeret gaussisk
skjult Markov model med to regimer, outperformer en rebalanceringsstrategi
out-of-sample efter handelsomkostninger, uden kendskab til fremtidige afkast
og med en realistisk forsinkelse mellem identifikationen af et regimeskift og im-
plementeringen af portføljetilpasningen.
Nøgleord: Regimeskift; Markov-skiftende miksturer; Ikke-lineær tidsrække-
modellering; Daglige afkst; Adaptivitet; Leptokurtiske fordelinger; Volatilitets-
klumpning; Dynamisk aktivallokering.
vi
Preface
This thesis was prepared at the Department of Applied Mathematics and Com-
puter Science at the Technical University of Denmark (DTU) in partial fulfill-
ment of the requirements for acquiring the M.Sc. degree in engineering with
honors.
Thethesisdealswithdifferentaspects ofmathematicalmodelingofthestylized
behavior of financial returns using regime-switching models with the aim of
developing a quantitative framework for regime-based asset allocation. The
developed strategies are tested out of sample under realistic assumptions to
ensure that the conclusions have practical relevance.
The process that led to this thesis began in the spring of 2013 when I attended
a class on hidden Markov models at DTU. In the summer of 2013, I began a
specialcourseonregimemodelingoffinancialdatawithHenrikMadsenatDTU.
Erik Lindström later joined the special course that led to the preparation of an
article(Nystrupetal.2014). IgottheideaforthethesiswhenIreadthearticle
on Markov-switching asset allocation by Bulla et al. (2011). In continuation of
theworkonthearticletherewasaninitialfocusontheestimationofcontinuous-
time models, but the strategies ended up being based on adaptively estimated
discrete-time hidden Markov models.
Copenhagen, July 2014
Peter Nystrup
viii