Crafting asset allocation for a re-insurer via portfolio optimisation
| dc.contributor.advisor | Von Leipzig, Konrad | en_ZA |
| dc.contributor.author | Abdulla, Mubeen | en_ZA |
| dc.date.accessioned | 2022-02-03T08:42:44Z | en_ZA |
| dc.date.accessioned | 2022-04-29T09:17:42Z | en_ZA |
| dc.date.available | 2022-02-03T08:42:44Z | en_ZA |
| dc.date.available | 2022-04-29T09:17:42Z | en_ZA |
| dc.date.issued | 2022-04 | en_ZA |
| dc.description | Thesis (MEng)--Stellenbosch University, 2022. | en_ZA |
| dc.description.abstract | ENGLISH SUMMARY: One of the most challenging tasks faced by nancial advisors and consultants, relates to the phenomena of portfolio selection. This process typically entails selecting asset classes based on their risk and reward attributes. Striking an optimal balance between risk and reward is no easy task, given its con icting nature. This phenomena is referred to as portfolio optimisation and is commonly formulated and solved via the well-known mean-variance optimisation procedure, based on the pioneering works by Harry Markowitz. The objective function is formulated as a quadratic programming problem, that seeks to maximise expected return whilst minimising risk. While this approach presents an auspicious foundation to solve a portfolio optimisation problem, it does not incorporate the unique liabilities (such as future payments or claims) inherent to most institutional investors. The aim of the study is therefore to provide a roadmap outlining how assets and liabilities are dovetailed to enhance the decision making process around portfolio optimisation. To achieve this, the notion and premise of asset-liability management (ALM) and liability-driven investing (LDI) are introduced to better manage both assets and liabilities, coherently. This would ultimately ensure an institutional investor's long term nancial sustainability. To add a practical ingredient to this thesis, a real-world case study for a re-insurer is examined. Essentially, the roadmap is applied to a case study to solve a complete portfolio optimisation problem, from an LDI perspective. The results of the unconstrained asset allocation reveal the optimiser's preference to allocate chie y to a small range of asset classes. While this outcome may be theoretically appropriate, this presents a practical challenge given potential concentration risks, and lack of portfolio diversi cation opportunities. For this reason, constraints are imposed within the optimisation procedure, resulting in a more diversi ed and larger array of asset classes to include within a portfolio. To aid with the model validation component and to serve as credence, subject matter experts are consulted. The outcome of this validation was that the process embarked upon as well as the results produced are reasonable and resonates with industry standards. To supplement the model validation and to serve as a reasonability check, a comprehensive sensitivity analysis was undertaken on key input parameters such as expected return to assess the impact this has on the optimal portfolio of assets. | en_ZA |
| dc.description.abstract | AFRIKAANS OPSOMMING: Een van die mees uitdagendste take wat nansi ele adviseurs en konsultante in die gesig staar, hou verband met die proses van portefeuljeseleksie. Dit behels tipies die keuse van bateklasse op grond van hul risiko- en opbrengskenmerke. Gegewe die teenstrydige aard, is dit nie 'n maklike taak om 'n optimale balans tussen risiko en opbrengs te vind nie. Hierdie verskynsel word portefeulje-optimalisering genoem en word algemeen geformuleer en opgelos deur middel van die bekende gemiddelde-variansie-optimaliseringsprosedure, gebaseer op die werk van Harry Markowitz. Die doelwit funksie is geformuleer as 'n kwadratiese programmeringsprobleem wat daarop gemik is om die verwagte opbrengs te maksimeer, terwyl dit die risiko verminder. Alhoewel hierdie benadering 'n gunstige grondslag bied om 'n probleem met portefeuljeoptimalisering op te los, bevat dit nie die unieke aanspreeklikhede (soos toekomstige betalings of eise) wat inherent is aan meeste institusionele beleggers nie. Die doel van die studie is dus om 'n padkaart te gee waarin uiteengesit word hoe bates en laste saamgevoeg kan word om die besluitnemingsproses rondom portefeuljeoptimalisering te verbeter. Om dit te bereik, word die idee en uitgangspunt van bate-aanspreeklikheidsbestuur (ALM) en aanspreeklikheidsgedrewe belegging (LDI) bekendgestel om bates en laste, samehangend, beter te bestuur. Dit sou uiteindelik 'n institusionele belegger se nansi ele volhoubaarheid op lang termyn verseker. Om 'n praktiese bestanddeel by hierdie proefskrif te voeg, word 'n werklike gevallestudie vir 'n herversekeraar ondersoek. Die padkaart word in wese toegepas op 'n gevallestudie om 'n volledige probleem met die optimalisering van portefeuljes, vanuit 'n LDI -perspektief, op te los. Die resultate van die onbeperkte batetoewysing onthul die optimiseerder se voorkeur om hoofsaaklik aan 'n klein reeks bateklasse toe te ken. Alhoewel hierdie uitkoms teoreties gepas kan wees, bied dit 'n praktiese uitdaging, gegewe moontlike konsentrasie-risiko's en 'n gebrek aan portefeuljediversi seringsgeleenthede. Om hierdie rede word beperkings opgel^e binne die optimaliseringsprosedure, wat lei tot 'n meer gediversi seerde en bre er verskeidenheid bateklasse wat in 'n portefeulje ingesluit moet word. Om hulp te verleen met die modelvalideringskomponent en om as geloofwaardigheid te dien, word deskundiges geraadpleeg. Die uitkoms van hierdie bekragtiging was dat die proses wat onderneem is, sowel as die resultate wat geproduseer is, redelik is en ooreenstem met aanvaarde industriestandaarde. Om die modelvalidasie aan te vul en as redelikheidstoetsing te dien, is 'n omvattende sensitiwiteitsanalise uitgevoer oor belangrike insetparameters, soos verwagte opbrengs, om die impak wat dit op die optimale portefeulje van bates het, te bepaal. | af_ZA |
| dc.description.version | Masters | en_ZA |
| dc.description.version | bs202207 | en_ZA |
| dc.format.extent | xvi, 133 pages : illustrations | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/124520 | en_ZA |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.subject | Quadratic programming | en_ZA |
| dc.subject | Asset-liability management | en_ZA |
| dc.subject | Portfolio management | en_ZA |
| dc.title | Crafting asset allocation for a re-insurer via portfolio optimisation | en_ZA |
| dc.type | Masters | en_ZA |