Credit and debit value adjustment estimations in the data sparse South African market
| dc.contributor.advisor | Van der Merwe, Carel Johannes | en_ZA |
| dc.contributor.author | De Jager, Louis Porter | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science. | en_ZA |
| dc.date.accessioned | 2017-02-13T09:31:22Z | |
| dc.date.accessioned | 2017-03-29T11:37:47Z | |
| dc.date.available | 2017-02-13T09:31:22Z | |
| dc.date.available | 2017-03-29T11:37:47Z | |
| dc.date.issued | 2017-03 | |
| dc.description | Thesis (MCom)--Stellenbosch University, 2017. | |
| dc.description.abstract | ENGLISH SUMMARY : During 2014, the International Accounting Standards Board (IASB) implemented a new standard for measuring the fair value of assets through the International Financial Reporting Standards (IFRS) 13 guidance. The newly introduced guidelines have probed market participants to adjust their valuation of financial positions for material counterparty credit risk (CCR) in the over-thecounter (OTC) market Five different models are implemented in this research for the purpose of calculating the credit value adjustment (CVA) and debit value adjustment (DVA) of an interest rate swap portfolio between a South African corporate treasurer, Eskom, and a generic South African tier 1 bank. The models differ from simple to complex. The Monte Carlo (MC) simulation model is assumed to be the most accurate, since it involves the simulation of expected exposure and the modelling of the short-rate. Corporate treasurers do not always have the necessary resources to calculate CVA by means of a sophisticated approach. Due to input data and resource challenges, corporate treasurers need to consider creative alternative methods to include CCR in their fair value adjustments. Therefore, semi-analytic methods and input approximation methods were considered in this research. It was found that simpler semi-analytic approximation methods do not possess the complexity needed to deal with the complexity of netting and collateral agreements. They serve as good approximations to quickly estimate a ball-park CVA, but lack the accuracy of the MC based approach. | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING : Die International Accounting Standards Board (IASB) het gedurende 2014 ‘n nuwe standard geimplementeer ten opsigte van die meting van die billike mark-waarde van bates onder die nuwe International Financial Reporting Standard (IFRS) 13 leiding. Hierdie nuwe leiding het mark belanghebbers gepeil om aanpassings te maak tot hul finansiele posisies ten opsigte van teenparty kredietrisko in die oor-die-toonbank mark. Vyf verskillende modelle word in hierdie studie geimplementeer vir die berekening van kredietwaardeaanpassing en debietwaardeaanpassing, van ‘n portefeulje bestaande uit rentekoers uitruilkontrakte tussen die Suid-Afrikaanse korporatiewe tesourier Eskom en ‘n generiese Suid-Afrikaanse vlak 1 bank. Die modelle wissel van eenvoudig tot kompleks. Die Monte Carlo model word aanvaar as die mees akkuraatste, vanwee sy komplekse onderliggende modellering van die kort-rentekoerse, asook sy onderliggende verwagte krediet blootstelling simulasie. Korporatiewe tesouriers beskik dikwels nie oor die nodige hulpbronne om kredietwaardeaanpassing s te bereken met ‘n gesofistikeerde model nie. As gevolg van data en ander hulpbron uitdagings, berus dit op die korporatiewe tesouriers om met kreatiewe alternatiewe voorendag te kom vir die hantering van kredietwaardeaanpassings tot hul finanisiele posisies. Dus moet semi-analitiese metodes en data beramings ondersoek word. In die studie word gevind dat hierdie eenvoudiger semi-analitiese metodes nie oor die nodige kompleksiteit beskik om komplekse netting en kollateraal kontrakte, wat met baie afgeleide instrumente gepaard gaan, te hanteer nie. Hulle dien egter as goeie metodes om vining ‘n beraming van kredietwaardeaanpassing te bereken, alhoewel hulle nie so akkuraat is soos die meer kompleke Monte Carlo en Swaption modelle nie. | af_ZA |
| dc.format.extent | xiv, 114 pages ; illustrations, includes annexures | |
| dc.identifier.uri | http://hdl.handle.net/10019.1/100846 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | |
| dc.rights.holder | Stellenbosch University | |
| dc.subject | Derivative securities -- South Africa | en_ZA |
| dc.subject | Counterparty risk -- South Africa | en_ZA |
| dc.subject | Financial statements -- Standards | en_ZA |
| dc.subject | Credit value adjustment | en_ZA |
| dc.subject | Debit value adjustment | en_ZA |
| dc.subject | UCTD | |
| dc.title | Credit and debit value adjustment estimations in the data sparse South African market | en_ZA |
| dc.type | Thesis | en_ZA |