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Event Abstract:
Quasi-Monte Carlo (QMC) methods based on Sobol’ sequences have been used extensively in financial applications for almost 30 years due to their superior convergence rates over Monte Carlo (MC). However, unlike MC, QMC lacks a practical error estimate. A randomized QMC (RQMC) method combines the best of two methods. RQMC based on scrambled Sobol’ sequences demonstrate the superior performance over standard QMC showing increased convergence rates and providing practical error bounds around the estimated values. This talk will compare different scrambling methods and applications of RQMC in finance.
About the Speakers:
Sergei Kucherenko received his MSc degree and PhD in applied mathematical physics from Moscow Engineering Physics Institute in Russia. He has held a number of research and faculty positions in various universities in Russia, the United States, the UK and Italy and has also worked in investment banking. Currently he holds a position of Senior Research Fellow at Imperial College London. He is also a founder of BRODA Ltd. which provides consultancy services to investment banks and financial companies in the area of MC and Quasi MC simulation and other advanced numerical techniques used in quantitative finance.
Julien Hok holds a PhD in financial mathematics from Ecole Polytechnique France. He started as a quantitative analyst in equity at Santander in London for six years and worked at Citi Group for two years focusing on interest rates. After, he joined CA-CIB as quantitative analyst in the hybrid desk at London for four years. Currently, he is a front office quantitative analyst manager and responsible for Equity and FX Derivatives desk at INVESTEC Bank.
