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IBM Algorithmics Introduction to Algo Risk Application (G1000G)

  • Overview
  • Who Should Attend
  • Certifications
  • Prerequisites
  • Objectives
  • Content
  • Schedule
Course Overview

Duration: 1 Days

Skill level: Intermediate

The Algo Risk Application (ARA) distributes all portfolio risk information required to facilitate the investment decision making process. The training itself is intended to provide participants with an overview of ARA, with hands-on experience in its functionality.

Who Should Attend

The target audience is the ARA end-user, particularly risk managers/analysts, portfolio managers and traders, investment analysts, and other financial professionals.

Course Certifications

This course is part of the following Certifications:

Prerequisites

A basic knowledge of risk management principles such as Value-at-Risk, scenario analysis, andderivatives is presumed.

Course Objectives

  • "Slice and dice" portfolios in ARA by means of aggregation
  • Build risk management reports for selected portfolios
  • Generate Monte Carlo and Historical VaR reports, with parametric method comparisons
  • Perform portfolio scenario analysis, including stress testing
  • Undertake "what-if" analysis of portfolios
  • Define user-preferences
  • Build virtual portfolios and benchmarks as a basis for tracking and related forms of relative risk analysis
  • Manage your portfolios, benchmarks, and reports

Course Content

This one-day introductory course and is delivered through a number of mediums, including product demonstrations, instructor-led exercises and self-paced hands-on practice.

  • Introduction to ARA and Navigation
  • Understanding how to set up the General Preferences
  • Understanding how ARA develops its analytics
    • Various Aggregation Methods
    • How they are seen in the Results Viewer
    • Absolute vs Relative Risk
    • Concentration Profiles
    • Analyzing the risk through different Trading Strategies
    • Create and Manage Limits
  • Understand how ARA generates Reports

NB: Optimization will not be covered in this course

Course ID: G1000G


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