Speaker

  • Dr Mesias Alfeus (Stellenbosch University)

    Dr Mesias Alfeus

    Stellenbosch University

Dr Alfeus is a Lecturer in the department of Statistics and Actuarial Science at Stellenbosch University, South Africa. He has a PhD in Quantitative Finance from the University of Technology Sydney (UTS) in Australia with a dissertation entitled "Stochastic Modelling of New Phenomena in Financial Markets". He is a Mathematician by training with a BSc degree in Mathematics and Physics from the University of Namibia. He holds masters and honours degrees in Financial Mathematics both with Cum Laude from Stellenbosch University, South Africa. He holds a long list of academic awards, and he was the winner of the 2018 International Young Investigator Training Program (YITP) prize at the XIX Workshop on Quantitative Finance held at the University of Rome Tre in Italy. He held an academic visiting position at the University of Padova, northern Italy. He previously worked as a Risk Analyst at Namibian Financial Institutions Supervisory Authority (NAMFISA), a Research Associate at UTS Finance, a Lecturer of Financial Mathematics at the University of Wollongong Australia, and AIFMRM Postdoctoral Research Fellowship at the University of Cape Town. His current research interests focus on Computational and Mathematical Finance, more specifically in numerical methods for pricing of options and model calibration including model empirical analysis. 

Abstract

Quantitative Finance underwent significant development over the past decade. For example, modelling of the term structure of interest rates after the GFC poses a unique challenge. The persistent phenomena of market basis spreads are an indication that markets are pricing various risks which are not captured in classical models. We pioneer a roll-over risk modelling framework to provide empirical evidence to the observed basis spreads, i.e., a spread between LIBOR of different tenors and LIBOR-OIS spread. This roll-over risk consists of two components, a credit risk component due to the possibility of being downgraded and thus facing a higher credit spread when attempting to roll over shortโ€“term borrowing, and a component reflecting the (systemic) possibility of being unable to roll over shortโ€“term borrowing at the reference rate (e.g., LIBOR) due to an absence of liquidity in the market. The modelling framework is of "reduced form" in the sense that the source of credit risk is not modelled (nor is the source of liquidity risk). We show how such model can be calibrated to market data, and used for relative pricing of interest rate derivatives, including bespoke tenor frequencies not liquidly traded in the market.

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