What quantitative aptitude do Finance and Risk Management require?
@actuarynews @CQFInstitute @GARP_Risk
Mathematics is the Queen of all Natural Sciences.

However, its applications in Social Sciences and its sub-fields such as Economics and Business Studies is growing all the time.

Finance and Risk are broadly categorized as subfields of Microeconomics.
That's my opinion.
The two subjects (Risk and Finance, which I would like to jointly refer to as Risk Finance) share a lot in common with the Pricing Theory, Utility Theory, Portfolio Theory, Risk Pooling, Risk Financing and Risk Sharing Theories, the Moral Hazard Problem,
and lastly, the Auctions Theory is taught as part & parcel of the Pedagogy undertaken at any standard business and/or economics school.
Of course, there is a lot more that can be added to the tentative academic teaching list!
The modern-day problems in Economics are viewed and interpreted as Mathematical problems.

This may lead to Charlatanism.

Another topic for another day.
From a standard Finance Pedagogical Perspective, the role of Maths is ever-growing and most noticeable. Curriculums are fast embracing General Quantitative Methods such as Matrix Algebra, EconoPhysics, Quantum Mechanics,
Advanced Neural Network Models, Statistical and Machine Learning Data Science Models, and certain Industrial Engineering Principles (Operations Research and Decision Science Tools) to identify and optimize Financial Market and Portfolio Management Problems.
Understanding the Basic TYPE 1 and TYPE 2 Errors, the Alpha and Beta Risk, power of the test and size of the test varies with the Law of Large Numbers, Levy Power Rule, Normal Distribution(Gaussian) model, Other Discrete Random variable distributions,
Bayesian Estimation Techniques, Stochastic Calculus - SDEs (Stochastic Differential Equations) and related Stochastic Processes, Arithmetic (ABM) and GBM - Geometric Brownian Motion Models, Markovian and Stationary increments, Ito’s Lemma and Wiener Process,
Martingale, White Noise Deviations- Serial Dependence / Autocorrelations, Random (Drunken) Walks, Auto-Regressions, ARIMA, ARMA, ARCH and GARCH(1,1)
and Monte Carlo Simulations are the Backbone of many Derivative Pricing Concepts, Model Building Methods and Risk Management Hedging Instruments and the correlated strategies.
These Risk and Finance Concepts are at best investigated and validated using certain Mathematical and Statistical Models.

We go a step forward now! Machine Learning and Big Data.
Converting your theory and model into a pattern recognizing predictive tool which can enable you to develop a prescriptive analytics dashboard require a strong understanding of programming and/or coding.
Hence, everything that was earlier taxonomized as a Mathematical and/or Statistical Theorem (with applications in Financial Markets/ Banking), now ends up as a Machine Learning Predictive Analytical Algorithm in the fast-expanding Universe of #Risk #Finance.
The computing and software programming experts specializing in AI - Artificial Intelligence have taken things beyond Coase Theorem and Game Theory, earlier solved by Tenure- track Professors using chalk and blackboard ingenuity!
Time to rephrase the question, please =>

” How Machine Learning and Big Data influences the subject of modern Risk Finance ?”

That will generate some more interesting answers.

Thank you!

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