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19 June | 10:00 am - 5:00 pm



Alex is a Computational Finance Application Engineer at MathWorks, an advisor to the financial services industry. Graduated from the Georgia Institute of Technology, in Computer Science, with an emphasis in Modeling and Simulation. Alex has extensive experience working closely with Central Banks in North America, advising them to improve their modeling and performance capabilities through the use of technologies ranging from Machine Learning to Cloud Computing. COURSE DESCRIPTION: 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. TOPICS: 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


PRICE: $15,000.00 Mexican Pesos + Tax (16%)

• Graduated from an economic and/or administrative career. • Preferably working in Financial Institutions. • Participants should bring a laptop.

DURATION: 7 Hours (1 Session) VENUE: JW Marriott Santa Fe Hotel

Avenida Santa Fe 160 Col. La Fe Santa Fe, CDMX.

PAYMENT METHODS: 1. Bank Transfer in US Dollars (Foreign Institutions) BANK: BBVA Bancomer ACCOUNT NUMBER: 0121 8000 11 0583 0066 SWIFT: BCMRMXMM BRANCH NUMBER: 0956 BENEFICIARY: RiskMathics, S.C. 2. Credit Card: VISA, MASTERCARD or AMERICAN EXPRESS. IMPORTANT NOTICE: There will be no reimbursements.

Registration E-mail: Telephone: +52 (55) 5638 0367 y +52 (55) 5669 4729


Profile for RiskMathics Financial Intitute

MATLAB Day Workshop 2019 Ing  

MATLAB Day Workshop 2019 Ing