Financial engineering is a multidisciplinary field involving financial theory, methods of engineering, tools of mathematics and the practice of programming. It has also been defined as the application of technical methods, especially from mathematical finance and computational finance, in the practice of finance.
Financial engineering plays a key role in a bank's customer-driven derivatives business
— delivering bespoke OTC-contracts and "exotics", and implementing various structured products —
which encompasses quantitative modelling, quantitative programming and risk managing financial products in compliance with the regulations and Basel capital/liquidity requirements.
An older use of the term "financial engineering" that is less common today is aggressive restructuring of corporate balance sheets.
Mathematical finance is the application of mathematics to finance. Computational finance and mathematical finance are both subfields of financial engineering. Computational finance is a field in computer science and deals with the data and algorithms that arise in financial modeling.
Financial engineering draws on tools from applied mathematics, computer science, statistics and economic theory.
In the broadest sense, anyone who uses technical tools in finance could be called a financial engineer, for example any computer programmer in a bank or any statistician in a government economic bureau. However, most practitioners restrict the term to someone educated in the full range of tools of modern finance and whose work is informed by financial theory. It is sometimes restricted even further, to cover only those originating new financial products and strategies.
Despite its name, financial engineering does not belong to any of the fields in traditional professional engineering even though many financial engineers have studied engineering beforehand and many universities offering a postgraduate degree in this field require applicants to have a background in engineering as well.
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This course provides an introduction to Distributed Ledger Technology (DLT), blockchains and cryptocurrencies, and their applications in finance and banking and draws the analogies between Traditional
The objective of this course is to acquire experience in financial machine learning by solving real-world problems. Different groups of students will work on different industry projects during the sem
Research oriented project in Financial engineering which is carried out during a 25-week internship in the financial industry, and based on which the student writes a master thesis
En finance, l'analyse quantitative est l'utilisation de mathématiques financières, souvent dérivées des probabilités, pour mettre au point et utiliser des modèles permettant aux gestionnaires de fonds et autres spécialistes financiers de s'attaquer à deux problèmes : mieux évaluer la valeur des actifs financiers, et surtout leurs dérivés. Ces dérivés peuvent être des produits comme les warrants, les certificats ou tout autre type de dérivé ou d'option (contrats Futures sur matières premières, indices, etc.
Les mathématiques financières (aussi nommées finance quantitative) sont une branche des mathématiques appliquées ayant pour but la modélisation, la quantification et la compréhension des phénomènes régissant les opérations financières d'une certaine durée (emprunts et placements / investissements) et notamment les marchés financiers. Elles font jouer le facteur temps et utilisent principalement des outils issus de l'actualisation, de la théorie des probabilités, du calcul stochastique, des statistiques et du calcul différentiel.
La modélisation financière consiste à représenter une situation financière grâce à un modèle mathématique, en fonction de différents paramètres. La modélisation financière facilite ainsi la prise de décision, en permettant de simuler divers scénarios et d’aboutir à des recommandations. La modélisation s’applique principalement à deux grands domaines de la finance, la finance d’entreprise et la finance de marché.
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