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Risk Evaluation with Data Analytics

Who Should Attend?

DATA SCIENTIST & DATA ANALYST involve in RISK MANAGEMENT

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Duration: 4 Days

With the rise of computing power and new analytical techniques, banks can now extract deeper and more valuable insights from their ever-growing mountains of data. Risk departments, which have been using data analytics for decades, participates heavily in these trends present unique opportunities to better identify, measure, and mitigate risk. Critically, they can leverage their vast expertise in data and analytics to help leaders shape the strategic agenda of the bank.

Today, risk analytics techniques make it possible to measure, quantify, and even predict risk with more certainty than ever before. That’s a big deal for organizations that have relied heavily on the opinions of leaders at the business unit level to monitor, assess, and report risk. Even for executives with sound intuition, it was virtually impossible to construct an enterprise level view of risk spanning many different parts of the business. Hence, let’s proceed to becoming risk intelligent by joining this course.

  • Perform all common data preparations
  • Build sophisticated predictive models
  • Evaluate model quality with respect to different criteria
  • Deploy analytical predictive models
Part A: Value At Risk Applications for Investment Management
  1. Overview of Portfolio Analytics
    • Overview of the mean-variance portfolio optimization
    • Financial risk management for fund management entities
  2. Value At Risk (VAR) Approaches
    • Variance-Covariance approach
    • Historical Simulation approach
    • Monte-Carlo simulation
  3. Portfolio Risk Decomposition
    • Decomposing risks using VAR approaches – Incremental VAR, Contribution VAR and Target-VAR
    • Merits of VAR computations for fund management
  4. Risk-Based Asset Allocation Strategies
    • Concept of risk-budgeting using VAR methodologies
    • Risk-based asset allocation strategies for portfolios
Part B: Structural Models (Regression Analysis)
  1. Overview of Regression Analysis
    • Single-factor and Multi-factor regression analysis
    • Theoretical background and practical application
    • Qualitative response regression model
  2. Limitations and Biases in Multiple-Regression
    • Issues with multicollinearity, heteroscedasticity, auto-correlation etc
    • Performing diagnostic testing
    • Managing exceptions in results from statistical models
  3. Scenario Analysis
    • Scenario analysis with multiple variables
    • Building what-if scenarios for risk assessment
  4. Principle Component Analysis
    • Application in Regression Analysis
    • Exercise and Summary
Part C: Econometric Models (Time Series Analysis)
  1. Generalized Linear Models
    • Formulate and fit the multiple linear regression model and its variants ▪ Formulate and fit a generalized linear model
  2. Univariate Time Series Models
    • Describe the main components of a time series model ▪ Fit AR, MA and ARMA models to data
    • Define cointegration
    • Perform unit root tests
    • Fit ARIMA models
    • Fit other time series models
  3. Multivariate Time Series Analysis
    • Extend the standard stationary univariate ARMA model to a multivariate VAR model that can model the simultaneous time series properties and interaction between a set of mutually endogenous variables
    • Estimate and interpret the results from a VAR model via two new concepts: the impulse response function and the variance decomposition function
    • Model a system of unit root processes that have a long-running stationary relationship via the VECM model
    • Extract useful information from a large time series dataset into a small number of variables that can be used effectively in multivariate analysis when the full dataset cannot
    • Extend the univariate volatility models to a multivariate setting where dynamic correlations between different assets can be modeled effectively
  4. Extreme Value Theory and Risk Management
    • Derive the distributions of order statistics from a random sample
    • Derive the limit distributions of order statistics
    • Derive the properties of the three main extreme value distributions
    • Estimate the parameters for extreme value distributions
    • Apply Extreme Value Theory to portfolio management
Part D: Risk Modeling
  1. Risk Management Tools
    • Various types of financial risk management tools for financial risks
    • Considerations for market risks
    • Considerations for credit risks
    • Considerations for operational risks
  2. Tools of Measuring Operational Risks
    • Consideration for special characteristics for measuring operational risks
    • Non-parametric models
    • Probability assessment
  3. Monte Carlo Simulation
    • How to build an effective simulation model for risk assessment?
    • Theoretical background and practical application
  4. Stress Testing
    • Key risk indicators
    • Simulation of key risk data

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