Analysing mathematical models in investment strategies and risk management within European companies
Sahebi, Emmanuel (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025120332241
https://urn.fi/URN:NBN:fi:amk-2025120332241
Tiivistelmä
Financial institutions operate in increasingly integrated markets and rely on quantitative models to support investment and risk decisions. Yet there is limited comparative evidence on how the Capital Asset Pricing Model, Modern Portfolio Theory, Value at Risk, and Arbitrage Pricing Theory are used side by side in practice. This thesis addresses that gap by examining how three European institutions apply these models in everyday planning, limit setting, and portfolio management.
The study builds on a concise theoretical overview of the four models and uses a qualitative multiple case design. Deutsche Bank, AXA, and OP Financial Group are analysed with a single template that records model use, calibration choices, links to limits and stress tests, and governance arrangements. Evidence is drawn from public reports, market and macroeconomic data, and explanatory materials, organised to allow transparent cross-case comparison.
The findings show that the same models play distinct roles depending on business model, balance sheet structure, and regulatory focus. Across the cases, the models work best when inputs are treated as ranges, when Value at Risk is complemented by stress testing, and when outputs are tied to precise limits, buffers, and decision forums.
The study builds on a concise theoretical overview of the four models and uses a qualitative multiple case design. Deutsche Bank, AXA, and OP Financial Group are analysed with a single template that records model use, calibration choices, links to limits and stress tests, and governance arrangements. Evidence is drawn from public reports, market and macroeconomic data, and explanatory materials, organised to allow transparent cross-case comparison.
The findings show that the same models play distinct roles depending on business model, balance sheet structure, and regulatory focus. Across the cases, the models work best when inputs are treated as ranges, when Value at Risk is complemented by stress testing, and when outputs are tied to precise limits, buffers, and decision forums.