matlab computational finance conference 2018 -凯发k8网页登录

diederick potgieter

bank of england

diederick potgieter is a risk specialist at the prudential regulation authority, bank of england. his responsibilities include icaap assessments, stress testing, and capital management technical reviews. he holds a ph.d. in mathematical statistics and his specialities include credit risk, operational risk, concentration risk, stress testing, and economic capital frameworks. before joining the fsa/pra in 2011, he was director of capital modelling at barclays bank.

jos martin

mathworks

jos martin is the principal architect for parallel computing tools at mathworks. before moving to a development role within mathworks, he worked as a consultant in the u.k., writing large-scale matlab applications, particularly in the finance and automotive sectors. prior to joining mathworks in 2000, he held a royal society post-doctoral fellowship at the university of otago, new zealand. his area of research was experimental bose-einstein condensation (bec), a branch of low-temperature atomic physics.

manoj bhaskar

hsbc

manoj bhaskar is the head of global risk analytics function for hsbc, where he leads a team responsible for managing analytics across risk and compliance functions across the group. in his role, he is the model risk steward for the group and leads the engagement on various regulatory initiates on use of internal models in the bank. in the last few years, he has been an active participant at various industry forums and part of various discussions with regulators globally.

model risk management principles for stress testing

in december 2017, the prudential regulation authority brought forward a consultation document highlighting themes around effective model management to further initial communications in the 2017 annual concurrent stress tests. the consultation document outlines four principles, which address the need for a comprehensive governance framework, including clear identification of models and purpose, an appropriate governance structure, defined roles of stakeholders, model developers, model owners, and control functions. for model lifecycle management, key themes include development, validation, independent review, use of judgement, implementation, and the use of models supported by adequate documentation, it systems, and appropriate levels of reporting to senior management.

in this talk, diederick potgieter discusses the four principles on model risk management for stress testing outlined in the consultation paper, as well as offers thoughts on model risk management and governance structures more generally.

streamlining financial modelling: from development to approval to production with matlab

in 2018, financial institutions face significant demands to generate a multitude of models for managing risk and generating returns across diverse activities and markets.  furthermore, internal reviewers and external regulators are scrutinizing the associated model governance and management processes ever more closely.  in this context, overall time to implementation has become a point of competitive advantage.

this talk describes how matlab^® is being used to reduce end-to-end model development time and risk through:

• supporting smooth data preparation and modelling workflows from exploration to automation
• enabling the practical application of machine learning and optimisation techniques
• combining automatically generated model documentation with expert insights and feedback
• bundling data, models, and documentation for internal and external review
• simplifying the deployment of models into production
• orchestrating the ongoing validation of models in service

benefits of a cloud environment: an example of operational risk capital model

giles spungin will discuss recent work on improving and extending hsbc’s pillar 2 operational risk model from a desktop-based process to a dynamically scalable cloud-based solution. he will examine how the model was scaled, first in serial by translating the legacy model into matlab^® and then in parallel running the application on the cloud, reducing the model execution time from days to hours. he will provide his thoughts on the opportunities that cloud implementation can offer financial modellers running matlab applications.