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Practitioners' Challenge

There was a good variety of projects to choose from, each one allowing us to work alongside a well renowned financial institution. It was really interesting working with academic staff at the LSE that had spent some time researching multivariate time series analysis, the topic we had chosen. In this respect we had the opportunity to ask lots of questions and get guidance from people who really knew what they were talking about.

Tom Parry

This project offers me an opportunity to apply what I learned to practice. I really enjoyed the process of thinking and cooperating with my team members and making progress step by step. The time is relatively short so we worked hard from creating ideas to implementing them and finally presenting the results. Overall, I would recommend it to all master students and I hope this project opportunity goes on and gets enlarged so more students could be involved.

Yili Peng

It's a very good access for students to have a view of the finance and insurance industry. In this activity, I did some research on the insurance and reinsurance and applied my statistical knowledge to the newest and hottest topic in insurance which really helps me to decide (my) future direction.

Jiahui Chen

Overview

Each year, we organise the Department of Statistics Practitioners' Challenge for BSc and MSc students. During this event, we collaborate with leading industry partners to initiate competitive projects focusing on real issues faced by companies. Students who take on the challenge use their personal and professional skills developed through their programme at LSE. 

Target audience

All third year undergraduate students and all taught postgraduate students within the Department of Statistics.

Details

During the project, led by Dr Gelly Mitrodima, we collaborate with leading industry partners. In the past we worked with Aviva, JP Morgan, UBS, and QBE. Companies propose a problem, from insurance to data science and students form teams in order to apply their interest for their preferred challenge. The teams are then selected from the companies through an interview process and they start working on their approach to the challenge. 

The students are supervised by Dr Mitrodima and other academic staff. PhD students in the Department of Statistics are offering support to the teams throughout the challenge. This way students don’t only work for well-known institutions, but they also collaborate with academic staff in the Department and get some invaluable guidance. We also organise a communication and presentation skills seminar in collaboration with LSE LIFE. This aims to help students with their actual presentations at the end of the challenge. For support and advice on programming students can refer to the LSE Digital Skills Lab and a dedicated team for the challenge.

The challenge starts during Week One of Lent term and ends after five weeks. In the final stage, our students present their findings to the companies and the Department and submit a technical report. The 2018 and 2019, BSc challenges were funded by the Student Experience Enhancement Fund. The funding is required for the prizes for the teams. 

Impact

Students enjoy the variety of projects to choose from and the chance to gain experience working on solving real issues. They also appreciated the working environment during the challenge, where they were able to reach out and exchange with academics and professional. The experience gave them more insight into their future career which often coincide with the industry they work with. A key aspect is that they learn how to research alternative approaches and develop great skills in effectively working with other people

Previous challenges for UG students

2018

Project 1

Title: Testing various methods of nonlinear principal component analysis (PCA) on financial time series           

Business: AVIVA       

Brief description: Apply traditional and nonlinear PCA on the overnight index swap (OIS) rates provided by the Bank of England. Investigate the forecasting performance of different time series models and select ones that are more robust to different time periods and at the same time maintain a low forecast error overall. 

Project 2

Title: Large loss prediction       

Business: QBE         

Brief description: Predict rare events (large losses, both in terms of frequency and severity), define the relationship between small and large losses, and find ways to rescale model forecasts generated from imbalanced data sets.

2019

Project 1

Title: Bouquet of interest rate models

Business: Hymans Robertson

Brief description: Compare and contrast various stochastic interest rate models given their advantages/limitations, implementation steps etc. 

Project 2

Title: Calibration risk for interest rates

Business: Hymans Robertson

Brief description: Quantify the calibration risk of interest rate models and demonstrate their impact on company’s projections. 

2020

Title: Correlation models for time series

Business: AVIVA       

Brief description: Identify the optimal length and the optimal frequency for the time series to calculate stable correlations. Establish a method to annualise the correlations calculated with daily or monthly observations. Develop a robust model able to forecast reliable future correlations.