Events

AFIR-ERM: Risk management with local least squares Monte Car

The least squares Monte Carlo method has become a standard approach in the insurance and financial industries for evaluating a company’s exposure to market risk. However, the non-linear regression of simulated responses on risk factors poses a challenge in this procedure. This article presents a novel approach to address this issue by employing an a-priori segmentation of responses. Using a K-means algorithm, we identify clusters of responses that are then locally regressed on their corresponding risk factors. The global regression function is obtained by combining the local models with logistic regression. We demonstrate the effectiveness of the proposed local least squares Monte Carlo method through two case studies. The first case study investigates butterfly and bull trap options within a Heston stochastic volatility model, while the second case study examines the exposure to risks in a participating life insurance scenario.
AFI-ERM Webinar

June 25, 2024
08:00 - 09:00 EST
(click here to check your local time)


The least squares Monte Carlo method has become a standard approach in the insurance and financial industries for evaluating a company’s exposure to market risk. However, the non-linear regression of simulated responses on risk factors poses a challenge in this procedure. This article presents a novel approach to address this issue by employing an a-priori segmentation of responses. Using a K-means algorithm, we identify clusters of responses that are then locally regressed on their corresponding risk factors. The global regression function is obtained by combining the local models with logistic regression. We demonstrate the effectiveness of the proposed local least squares Monte Carlo method through two case studies. The first case study investigates butterfly and bull trap options within a Heston stochastic volatility model, while the second case study examines the exposure to risks in a participating life insurance scenario.

 

Speaker:

Donatien Hainaut
Donatien Hainaut is a professor of quantitative finance and actuarial sciences at UCLouvain, where he manages the Master's program in Data Science with a statistical orientation. Prior to this, he held several positions as an associate professor at Rennes School of Business and the ENSAE in Paris. He also possesses extensive field experience, having worked as a Risk Officer, Quantitative Analyst, and ALM Officer. He is a Qualified Actuary and holds a PhD in the area of Asset and Liability Management. His current research focuses on contagion mechanisms in stochastic processes, fractional processes, and neural networks.


Moderator:

Lukasz Delong
Lukasz Delong is working at SGH Warsaw School of Economics. He has a PhD in mathematics, a habilitation degree in economics and the professor title in economics and finance. Lukasz is an actuary with license no. 130 issued by the Polish Financial Supervision Authority, the head of the Examination Committee for Actuaries at the Polish Financial Supervision Authority, a board member of the Polish Society of Actuaries and a board member of AFIR-ERM Section of the IAA. He is an editor of ASTIN Bulletin – The Journal of the IAA and an associate editor of European Actuarial Journal. Lukasz is an author of numerous scientific papers on insurance mathematics, financial mathematics and probability. His scientific research includes different areas of actuarial mathematics with emphasis on stochastic modelling of financial risks and machine learning in insurance.


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Important Notes:
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  • To complete your registration, click the "Proceed to check-out" button and lastly click on the "Submit" button to complete your order.
  • Once successfully registered, you will receive an automated response with your individual Zoom link to add to your personal calendar.
  • In addition to the Zoom link, you will also receive a confirmation email from the IAA Secretariat.
  • If you need assistance with your registration; please contact Teboho Mabeba, Section Coordinator

When
6/25/2024 8:00 AM - 9:00 AM