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Autor Tópico: Quant analysis on Portfolio Optimization  (Lida 74 vezes)

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Quant analysis on Portfolio Optimization
« em: 18 de Maio de 2021, 09:23 »

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 26 lectures (3h 17m) | Size: 1.43 GB
Discover how Mathematical Optimization models can be applied to Financial Portfolio Optimization by building your own

What you'll learn:
Portfolio Optimization
The effect of uncertain asset prices
The effect of deterministic asset prices
Coding in GAMS
Coding in FICO
Stochastic Optimization application in Finance

Requirements
There are no pre-requisites. We start from zero.

Description
We develop Stochastic Optimization models and apply them to Finance. Specifically...

a) We install FICO and GAMS software and learn the fundamental code needed.

b) We define what Stochastic Optimization is, what a scenario is, what uncertainty is, what the levels of uncertainty are,
what a Scenario Tree is , and we look at mathematical formulations for Stochastic Optimization.

c) We apply Stochastic Optimization to Finance. Specifically we build a Portfolio Optimization model. We use FICO and GAMS software tools.

d) We learn the differences between deterministic and stochastic optimization. The code is significantly different in these two versions of optimization.

e) We conduct sensitivity analyses on stochastic optimization problems.

The entire FICO and GAMS code, and complementary material used in this course, are all available to download.

No prerequisites are specified for this course.

We start, literally, from the very beginning and build , step by step, the necessary knowledge.

In case you have difficulties , please feel free to post them in the Q & A section.

Thank you, and see you in the course.

Any questions you may have please feel free to message me or to simply participate in the Q and A section inside the course.

This will allow you to engage with fellow researchers and finance audience

Who this course is for
Quant Researchers
PhD Students and Academics
Investors and Enterpreneurs
Postgraduate students with an interest in finance


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