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19 junio, 2019


Part 1:
Many banks, asset managers, supervisors, and insurers are using more advanced technological approaches for capital
requirements regimes such as stress test infrastructures for regulatory requirements, market risk, credit risk, operational risk, and compliance and fraud monitoring.
Learn how MATLAB is being used by mathematicians, quants, data scientists, and others to perform risk calculations that are faster than spreadsheets. With these tools, you can create models more quickly than in C++, with greater transparency and customization than black box products, and with greater quality and consistency than open source applications.
Part 2:
At the heart of many financial applications are machine learning techniques used for risk classification, economic analysis, credit scoring, time series forecasting, estimating default probabilities, and data mining. Big data represents an opportunity for quantitative analysts and data scientists alike to impact the way organizations make informed business decisions. By building machine learning models that harness big data, a greater level of insight and confidence can be achieved. MATLAB minimizes challenges in the machine learning space by providing you with a number of built-in functions and tools for quick prototyping, integration, and scaling, to take you from initial prototype all the way to business-critical production system.


Part 1: Portfolio Optimization, Risk Management, and more using MATLAB
• Build an optimal portfolio using MATLAB.
• Evaluate and backtest market risk in MATLAB – [Extreme Value Theory] GARCH, Copula & Pareto tail distribution fitting.
• Credit risk analysis using Monte Carlo methods.
• Predicting credit losses for counterparties using Copulas.
• Develop graphical applications in MATLAB and deploy them to your end users.
• Develop interfaces in Excel using MATLAB developed functionality.
• Build and deploy MATLAB Apps to the web.

Part 2: Machine Learning and Big Data Analytics using MATLAB
• Data management and integration with databases, live market data, and big data environments
• Dealing with out-of-memory data (big data) using Parallel Computing techniques
• Using Neural networks, deep learning, supervised and unsupervised machine learning techniques to enhance traditional Financial modeling approaches
• Identifying Alpha or risks stemming from unstructured data
HORAS: 7 1 Sesión

Alex Link



Fecha de inicio 19 / 06 / 2019
Pais México
Duración 7 Horas (1 Clase)
  • JW Marriot Santa Fe
Horarios WORKSHOP 10:00 am a 6:00 pm
* El horario varia según el módulo
Puntos AMIB N / A
Puntos Mexder N / A
Precio MXN $17,400.00