Package ‘CASdatasets’

1 Package casdatasets December 11, 2020 TypePackageTitleInsurance Dutang [aut, cre], Arthur Charpentier [ctb]MaintainerChristophe collection of datasets, originally for the book 'Computational Actuarial Science with R'edited by Arthur Charpentier. Now, the Package contains a large variety of actuarial (>= ), xts, spImportslatticeLicenseGPL (>= 2) , , documented:asiacomrisk ..3ausautoBI8999 ..4auscathist ..5ausNLHYby ..6ausNLHY glossary .. 10ausNLHY lloyd .. 12ausNLHY total .. 13ausNSW .. 16ausprivauto .. 17austriLoB .. 19beaonre .. 20besecura .. 21bragg .. 22brautocoll .. 23brgeomunic .. 24brvehins .. 2612 Rtopics documented:canlifins.

2 28 casdatasets .. 29credit .. 32danish .. 34 Davis .. 35 ECBY ieldCurve .. 36eqlist .. 36eudirectlapse .. 38eusavingsurrender .. 39 FedYieldCurve .. 40forexUSUK .. 41fre4 LoBtriangles .. 42freaggnumber .. 43frebiloss .. 44freclaimset .. 45freclaimset2 .. 45frecomfire .. 47freDisTables .. 48fredpt17 .. 51fremarine .. 52freMortTables .. 53fremotorclaim .. 56freMPL .. 59freMTPL .. 62freportfolio .. 64hurricanehist .. 66 ICB .. 67itamtplcost .. 71linearmodelfactor .. 72lossalae .. 73norauto .. 74 Norberg .. 75norfire .. 75nortritpl8800 .. 76nzcathist .. 77 PnCdemand .. 78pricingame.

3 80sgautonb .. 84sgtriangles .. 86 SOAGMI .. 87spacedata .. 88swautoins .. 90swbusscase .. 91swmotorcycle .. 92swtriangles .. 93tplclaimnumber .. 94ukaggclaim .. 95ukautocoll .. 96usautoBI .. 97usautotriangles .. 98usexpense .. 99usGLtriangles .. 100asiacomrisk3ushurricane .. 101ushustormloss4980 .. 103uslapseagent .. 103usmassBI .. 105usmedclaim .. 106usprivautoclaim .. 107usquakeLR .. 108ustermlife .. 109uswarrantaggnum .. 110usworkcomp .. 111 Index113asiacomriskLarge commercial risks in Asia-PacificDescriptionA completed project by the insurance Risk and Finance Research Centre ( ) hasassembled a unique dataset from Large Commercial Risk losses in Asia-Pacific (APAC) coveringthe period 2000-2013.

4 The data was generously contributed by one global reinsurance companyand two large Lloyd s syndicates in London. This dataset is the result of the project co-lead by DrMilidonis (IRFRC and University of Cyprus) and Enrico Biffis (Imperial College Business School),which can be referred to as the IRFRC LCR expected, the dataset is fully anonymised, as the LCR losses are aggregated along a few dimen-sions. First, data is categorised based on the World Bank s economic development means that losses either come from developed or developing countries. The second dimen-sion used to aggregate the data is the time period covered. Data is grouped into (at least) twotime-periods: the period before and after the 2008 large commercial risk (LCR) is defined as a loss caused by man-made risks ( fire, explosion,etc.)

5 We exclude natural catastrophe events, and started by focusing on claims that made thedata provider incur a loss amount of at least EUR 1 million. We then extended our dataset toinclude claims leading to loss amounts smaller that EUR 1 million. Given time constraints, weonly partially extended loss data by obtaining FGU losses larger than EUR 140k. One should notethat any selection bias arising from the data collection exercise is driven by both data quality andreliability. Based on our experience, the latter two attributes are homogeneous across developedand developing countries APAC further details, see the technical report: Benedetti, Biffis and Milidonis (2015a).

6 Usagedata(asiacomrisk)Formatasiacomriskc ontains 7 columns:PeriodA character string for the period:"2000-2003","2004-2008","2009-201 0","2011-2013".FGUFrom the Ground Up Loss (USD).TIVT otal Insurable Value (TIV) replaced with Total Sum Insured (TSI) when the TIV is not avail-able (USD).4ausautoBI8999 CountryStatusA character string for the country status:"Developped","Emerging".UsageA character string for the type of exposure hit by the loss:"Commercial","Energy","Manufacturin g","Misc.","Residential".SubUsageA character string for a precised type of exposure hit by the loss:"Commercial","Energy","General industry","Metals/Mines/Chemicals","Misc .","Residential","Utility".DRA numeric for the destruction rate (FGU divided TIV capped to 1).

7 SourceIRFRCR eferencesBenedetti, D., Biffis, E., and Milidonis, A. (2015a).Large Commercial Risks (LCR) in insurance :Focus on Asia-Pacific, insurance Risk and Finance Research Centre Technical , D., Biffis, E., and Milidonis, A. (2015b).Large Commercial Exposures and Tail Risk:Evidence from the Asia-Pacific Property and Casualty insurance Market, Working , V., Embrechts, P., and Hofert, M. (2015).An extreme value approach for mod-eling operational risk losses depending on Journal of Risk and # (1) load of data#data(asiacomrisk)dim(asiacomrisk)# (2) basic boxplots#asiacomriskboxplot(DR ~ Usage, data=asiacomrisk)boxplot(DR ~ SubUsage, data=asiacomrisk)boxplot(DR ~ Period, data=asiacomrisk)boxplot(DR ~ CountryStatus, data=asiacomrisk)ausautoBI8999 Automobile bodily injury claim dataset in AustraliaDescriptionThis data set contains information on 22036 settled personal injury insurance claims in claims arose from accidents occurring from July 1989 through to January 1999.

8 Claimssettled with zero payment are not (ausautoBI8999)auscathist5 FormatausautoBI8999is a data frame of 8 columns and 1,340 rows:AccDate,ReportDate,FinDateThe accident date, the reporting date, the finalization date, notethat the day is always set to the first day of the ,ReportMth,FinMthThe accident month, the reporting month, the finalization month: 1 =July 1989, .., 120 = June 1999).OpTimeThe operational ,InjType2,InjType3,InjType4,InjType5 The injury code for the people injured (upto five).InjNbNumber of injured character string for: Has the policyholder a legal representation?AggClaimAggregate settled amount of De Jong and Heller (2008),Generalized linear models for insurance data, Cambridge Uni-versity # (1) load of data#data(ausautoBI8999)dim(ausautoBI899 9)head(ausautoBI8999)auscathistAustralia n catastrophe historicDescriptionHistorical disaster statistics in Australia from 1967 to (auscathist)6ausNLHYbyFormatauscathistis a data frame of 9 columns:Yeara numeric for the numeric for the quarter of the character string for the for the first day of natural for the last day of natural catastrophe, when character string describing the factor describing the event type among the list.

9 "Cyclone","Earthquake","Flood","Flood,St orm","Hailstorm","Other","Power outage","Storm","Tornado","Weather","Bus hfire".Locationa character string describing the cost in million of Australian dollars (AUD).NormCost2011 Normed cost in million of 2011 Australian dollars (AUD) taking into account in-flation, change in wealth and cost in million of 2014 Australian dollars (AUD) computed as the inflatedcostNormCost2011using # (1) load of data#data(auscathist)# (2) plot of data#plot(ecdf(auscathist$NormCost2014)) ausNLHYbyAustralian Market - non-life insurance (company, state, public level)DescriptionFinancial performance and financial position of insurers operating in Australia between 2005 and2010 (company, state, public level).

10 AusNLHYby7 Usagedata(ausNLHYC laimByState)data(ausNLHYPremByState)data (ausNLHYCapAdeqByComp)data(ausNLHYFinPer fByComp)data(ausNLHYFinPosByComp)data(au sNLHYPrivInsur)data(ausNLHYFinPerfPublic )data(ausNLHYFinPosPublic)data(ausNLHYOp IncExpPublic)data(ausNLHYPremClaimPublic )data(ausNLHYPubInsur)FormatausNLHYPremB yState(Table 10) andausNLHYC laimByState(Table 11) are data frames of 6 columns(values are in million of Australian dollars (AUD)): Class: Class of business. NSWACTYYYYMM: New South Wales / Australian Capital Territory for yearYYYY. VICYYYYMM: Victoria in yearYYYY reported onDateYYYYMM. QLDYYYMM: Queensland in yearYYYY reported onDateYYYYMM. SAYYYYMM: South Australia in yearYYYY reported onDateYYYYMM.