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ME317: Statistical Methods for Risk Management

Subject Area: Research Methods, Data Science, and Mathematics

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Course details

  • Department
    Department of Statistics
  • Application code
    SS-ME317
Dates
Session oneNot running in 2024
Session twoNot running in 2024
Session threeOpen - 29 Jul 2024 - 16 Aug 2024

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Applications are open

We are accepting applications. Apply early to avoid disappointment.

Overview

This course is a self-contained introduction to probabilistic and statistical methods used in risk management.

A number of important questions and methods will be examined, including:

  • How is the risk of a portfolio measured?
  • How is the accuracy of a risk management framework accessed?
  • How is a multivariate model constructed and tested?
  • How is the dependence relation between risk factors modelled?
  • How are extreme events modelled and measured?

This course will help students develop rigorous quantitative skills to measure market risks in modern financial institutions. It builds on student’s introductory understanding of Probability and Statistics and focuses on risk management applications. This course will illustrate methodologies using real financial data and a number of computer-based workshops.

Key information

Prerequisites: At least one semester of Calculus, and at least one semester of Probability and Statistics. Some understanding of financial markets.

Level: 300 level. Read more information on levels in our FAQs

Fees: Please see Fees and payments

Lectures: 36 hours

Classes: 18 hours

Assessment: Two examinations: one individual project (25%) and a final examination (75%). 

Typical credit: 3-4 credits (US) 7.5 ECTS points (EU)

Please note: Assessment is optional but may be required for credit by your home institution. Your home institution will be able to advise how you can meet their credit requirements. For more information on exams and credit, read Teaching and assessment

Is this course right for you?

This course is ideal if you are seeking a theoretical and practical understanding in modern statistical methods and risk management fundamentals which help practitioners to navigate uncertainties and make informed choices in dynamic financial institutions. If you are targeting a career in finance, insurance, risk management, and data analytics you should consider this course.

It will also be useful if you are starting an MSc or you are a professional and wish to learn fundamental concepts in the area.

Outcomes

  • Understand how market risks affect financial risk management practices
  • Show how to apply cutting-edge statistical methods for risk management
  • Analyse approaches and challenges affecting risk management
  • Apply statistical methods including Value-at-Risk, Expected shortfall, multivariate normal models, factor models, Copulas, extreme value theory
  • Implement methodologies using real financial data 

Content

Jonathan Tam, Canada

The fundamentals of my course are covered at my home institution, but the summer school course gives me an extra breadth into how the industry works. It’s been a really good experience in diversifying my skill set.

Faculty

The design of this course is guided by LSE faculty, as well as industry experts, who will share their experience and in-depth knowledge with you throughout the course.

Gelly Mitrodima

Dr Gelly Mitrodima

Assistant Professor (Education)

Department

LSE’s Department of Statistics has earned an international reputation for the development of statistical methodology that has grown from its long history and active contributions to research and teaching in statistics for the social sciences.

Students have the opportunity to engage with some of the most rapidly developing topics transforming business and society today, including machine learning, big data forecasting, social media, and text and network analysis. As a result, the department is meeting the rising demand for professionals with the skills to work with new datasets and who can conduct meaningful research. Students can develop these sought-after data science skills which will prepare them for careers in a wide range of sectors including the financial, government, non-profit and public sectors.

Apply

Applications are open

We are accepting applications. Apply early to avoid disappointment.