Mathematical and Statistical Methods for Actuarial Sciences and Finance

190 Pages·2014·2.48 MB·English

by  Cira Perna

Cira Perna
Marilena Sibillo
Editors
Mathematical and
Statistical Methods
for Actuarial
Sciences and Finance
Mathematical and Statistical Methods
for Actuarial Sciences and Finance
Cira Perna (cid:2) Marilena Sibillo
Editors
Mathematical and
Statistical Methods
for Actuarial Sciences
and Finance
Editors
CiraPerna MarilenaSibillo
DepartmentofEconomicsandStatistics DepartmentofEconomicsandStatistics
UniversityofSalerno UniversityofSalerno
Fisciano,Salerno,Italy Fisciano,Salerno,Italy
ISBN978-3-319-05013-3 ISBN978-3-319-05014-0(eBook)
DOI10.1007/978-3-319-05014-0
SpringerChamHeidelbergNewYorkDordrechtLondon
LibraryofCongressControlNumber:2014933795
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Preface
Thisvolume1 aimstocollectnewideaspresentedinformof 4-pagespapersdedi-
catedtomathematicalandstatisticalmethodsinactuarialsciencesandfinance.The
cooperationbetweenmathematiciansandstatisticiansworkingininsuranceandfi-
nanceisaveryfruitfulfieldandprovidesinterestingscientificproductsintheoretical
modelsandpracticalapplications,aswellasinthescientificdiscussionofproblems
ofnationalandinternationalinterest.
Fromthetheoreticalandapplicativepointofview,thetopicscoveredinthebook
are: actuarial models; alternative testing approaches; behavioural finance; cluster-
ingtechniques;coherentandno-coherentriskmeasures;credit-scoringapproaches;
data envelopment analysis; dynamic stochastic programming; financial contagion
models; financial ratios; intelligent financial trading systems; mixture normality
approaches; Monte Carlo-based methodologies; multi-criteria methods; nonlinear
parameterestimationtechniques;nonlinearthresholdmodels;particleswarmopti-
mization;performancemeasures;portfoliooptimization;pricingmethodsforstruc-
tured and non-structured derivatives; risk management; skewed distribution anal-
ysis; solvency analysis; stochastic actuarial valuation methods; variable selection
models;timeseriesanalysistools.
In the light of the successful cooperation between the above two quantitative
approaches,theEditorsofthevolumeorganizethebiennialconferenceonMathe-
maticalandStatisticalMethodsforActuarialSciencesandFinance(MAF),bornat
theUniversityofSalernoin2004andjustarrivedatits6theditionthisyear.
Salerno CiraPernaandMarilenaSibillo
April2014
1PublishedwiththecontributionofDipartimentodiScienzeEconomicheeStatistiche,Università
degliStudidiSalerno.
v
Contents
CanPersonalDependencyPathsHelptoEstimateLifeExpectancy
FreeofDependency? . . . . . . . . . . . . . . . . . . . . . . . . . . 1
IreneAlbarrán,PabloAlonso,AnaArribas-Gil,andAureaGrané
EvaluationofVolatilityForecastsinaVaRFramework . . . . . . . . . 7
AlessandraAmendolaandVincenzoCandila
OptimalCut-OffPointsforMultipleCausesofBusinessFailureModels 11
AlessandraAmendolaandMarialuisaRestaino
Maximum Empirical Likelihood Inference for Outliers
inAutoregressiveTimeSeries . . . . . . . . . . . . . . . . . . . . . 17
RobertoBaragona,FrancescoBattaglia,andDomenicoCucina
The Role of Fund Size and Returns to Scale in the Performance
ofMutualFunds . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
AntonellaBassoandStefaniaFunari
ARobustnessAnalysisofLeast-SquaresMonteCarloforR&DReal
OptionsValuation . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
MartaBiancardiandGiovanniVillani
The Common Pool Problem of Intergovernmental Interactions
andFiscalDiscipline:AStackelbergApproach . . . . . . . . . . . 31
GiovannaBimonteandPietroSpennati
EvaluatingCorrelationsinEuropeanGovernmentBondSpreads . . . . 35
SimonaBoffelliandGiovanniUrga
ProbabilityofDefault:AModernCalibrationApproach. . . . . . . . . 41
StefanoBoniniandGiulianaCaivano
DevelopmentofaLGDModelBasel2Compliant:ACaseStudy . . . . 45
StefanoBoniniandGiulianaCaivano
vii
viii Contents
ModellingtheLatentComponentsofPersonalHappiness . . . . . . . . 49
StefaniaCapecchiandDomenicoPiccolo
MeasuringtheImpactofBehaviouralChoicesontheMarketPrices . . 53
MassimilianoCaporin,LucaCorazzini,andMicheleCostola
ANoteonNaturalRiskStatistics,OWAOperatorsandGeneralized
GiniFunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
MartaCardin
The Estimation of Standard Deviation of Premium Risk Under
Solvency2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
RoccoRobertoCerchiaraandVittorioMagatti
TheSolvencyCapitalRequirementManagementforanInsurance
Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
MariarosariaCoppolaandValeriaD’Amato
DirectMulti-StepEstimationandTimeSeriesClassification . . . . . . 69
MarcellaCorduas
AlternativeAssessmentsoftheLongevityTrends . . . . . . . . . . . . . 73
ValeriaD’Amato,StevenHaberman,GabriellaPiscopo,and
MariaRussolillo
Combinatorial Nonlinear Goal Programming for ESG Portfolio
OptimizationandDynamicHedgeManagement . . . . . . . . . . . 77
GordonH.DashJr.andNinaKajiji
OntheGeometricBrownianMotionwithAlternatingTrend . . . . . . 81
AntonioDiCrescenzo,BarbaraMartinucci,andShelemyahuZacks
EmpiricalEvidencesonPredictiveAccuracyofSurvivalModels . . . . 87
EmiliaDiLorenzo,MicheleLaRocca,AlbinaOrlando,CiraPerna,
andMarilenaSibillo
RedES™, a Risk Measure in a Pareto-Lévy Stable Framework
withClustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
RiccardoDonatiandMarcoCorazza
Run-OffErrorintheOutstandingClaimsReservesEvaluation . . . . . 95
NicolinoEttoreD’OrtonaandGiuseppeMelisi
TrajectoryBasedMarketModels.ArbitrageandPricingIntervals . . . 99
SebastianFerrando,AlfredoGonzalez,IvanDegano,and
MassoomeRahsepar
AStatisticalTestfortheHestonModel. . . . . . . . . . . . . . . . . . . 105
GiannaFigà-Talamanca
ThresholdRandomWalkStructuresinFinance . . . . . . . . . . . . . 109
FrancescoGiordano,MarcellaNiglio,andCosimoDamianoVitale
Contents ix
StochasticMortalityModels.ApplicationtoCRMortalityData. . . . . 113
JánGogola
RiskAdjustedDynamicHedgingStrategies . . . . . . . . . . . . . . . . 117
MartinHarcek
PricingandHedgingVariableAnnuities . . . . . . . . . . . . . . . . . . 121
AbdouKélaniandFrançoisQuittard-Pinon
MonetaryRiskFunctionalsonOrliczSpacesProducedbySet-Valued
RiskMapsandRandomMeasures . . . . . . . . . . . . . . . . . . 125
DimitriosG.KonstantinidesandChristosE.Kountzakis
AProbabilityInequalityRelatedtoMardia’sKurtosis . . . . . . . . . . 129
NicolaLoperfido
Integrating Industrial and Financial Analysis into a Rating
Methodology for Corporate Risk Detection: The Case
oftheVicenzaManufacturingFirms . . . . . . . . . . . . . . . . . 133
GuidoMaxMantovani,GiancarloCoro,PaoloGurisatti,and
MattiaMestroni
RiskMeasurementUsingtheMixedTemperedStableDistribution . . . 137
LorenzoMercuriandEditRroji
CorporateFinance...WhatElse?TheCaseoftheProductiveChain
NetworksinNorth-EastItalyandtheScaffoldingFinanceAdopted
byTheirLeader . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
MattiaMestroni,ElisabettaBasilico,andGuidoMaxMantovani
BEKK Element-by-Element Estimation of a Volatility Matrix.
APortfolioSimulation . . . . . . . . . . . . . . . . . . . . . . . . . 145
AlessiaNaccaratoandAndreaPierini
TheEffectsofCurvatureandElevationoftheProbabilityWeighting
FunctiononOptionsPrices . . . . . . . . . . . . . . . . . . . . . . 149
MartinaNardonandPaoloPianca
A Multivariate Approach to Project the Long Run Relationship
BetweenMortalityIndicesforCanadianProvinces . . . . . . . . . 153
AchilleNtamjokouen,StevenHaberman,andGiorgioConsigli
MeasuringandManagingtheLongevityRisk:AnEmpiricalEvidence
FromtheItalianPensionMarket . . . . . . . . . . . . . . . . . . . 163
AlbinaOrlando,GovannadiLorenzo,andMassimilianoPolitano
Pricing and Hedging Basket Options Under Shifted Asymmetric
Jump-DiffusionProcesses . . . . . . . . . . . . . . . . . . . . . . . 167
TommasoPaletta,ArturoLeccadito,andRaduTunaru
x Contents
On a Data Mining Framework for the Identification of Frequent
PatternTrends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
MarinaResta
RiskProcesseswithNormalInverseGaussianClaimsandPremiums . . 177
DeanTenengandKalevPärna
APortfolioModelfortheRiskManagementinPublicPension . . . . . 183
TadashiUratani
BlackScholesOptionSensitivityUsingHighOrderGreeks . . . . . . . 187
YvesRakotondratsimba
Can Personal Dependency Paths Help
to Estimate Life Expectancy Free
of Dependency?
IreneAlbarrán,PabloAlonso,AnaArribas-Gil,andAureaGrané
Abstract Theagingofpopulationisperhapsthemostimportantproblemthatde-
veloped countries must face in the near future. In fact, one of the eight tackling
societal challenges of the European program Horizon 2020 is concerned with it.
Dependencycanbeseenasaconsequenceoftheprocessofgradualaging.There-
fore,itsprevalenceonthepopulation,itsintensityandevolutionoverthecourseof
aperson’slifehaverelevanteconomic,politicalandsocialimplications.Fromdata
base EDAD 2008 the authors constructed a pseudo panel that registers personal
evolutionofthedependencyscaleaccordingtotheSpanishlegislationandobtained
individual dependencycurves. In this work, our aim is to estimate life expectancy
freeofdependencyusingcategoricaldataandthefunctionalinformationcontained
inthesetrajectories.
Keywords Dependency·Functionaldata·Lifeexpectancy
1 Introduction
When talking about dependency two fundamental aspects must be considered.
Firstly,thedefinitionitself.ResolutionR(98)oftheCouncilofEuropedefinesde-
pendencyas“suchstateinwhichpeople,whomforreasonconnectedtothelackor
loss of physical, mental or intellectual autonomy, require assistance and/or exten-
sivehelpinordertocarryoutcommoneverydayactions”.Secondly,theassessment
I.Albarrán·A.Arribas-Gil·A.Grané(B)
UniversidadCarlosIIIdeMadrid,Getafe,Spain
e-mail:[email protected]
I.Albarrán
e-mail:[email protected]
A.Arribas-Gil
e-mail:[email protected]
P.Alonso
UniversidaddeAlcalá,AlcaládeHenares,Spain
e-mail:[email protected]
C.Perna,M.Sibillo(eds.),MathematicalandStatisticalMethodsforActuarialSciences 1
andFinance,DOI10.1007/978-3-319-05014-0_1,
©SpringerInternationalPublishingSwitzerland2014