CFA Level 2: Derivatives Part 2 – Introduction

This is the second tutorial of two covering derivatives for the Level II CFA Exam.  The topics of options, swaps, and credit default swaps can be intimidating for some candidates, particularly given the volume of associated formulas.

One recommended prep approach is to start with the conceptual aspects of derivatives and then apply the formulas.  First, some exam questions are likely to be conceptual or at least require limited math.  By understanding the theory and concepts for options and swaps, candidates have positioned themselves for success on these questions.  Second, attempting to study the formulas before mastering the concepts may hinder the preparation progress if frustration settles in.

A total of two item sets (12 questions) on derivatives is a reasonable expectation.  Some percentage of those questions will come from options, swaps, and credit derivatives (i.e. CDS).  Application of the “Greeks”, from the Black-Scholes-Merton option pricing model is a likely suspect for one or two exam questions.

Derivatives are a middle tier subject in terms of importance.  If you want to pass the exam, derivatives does not need to be your strongest subject, but you cannot afford to leave too many points on the table either.  Take derivatives seriously and adapt a test tactic around your strengths and weaknesses that allows you to maximize points.

This tutorial aligns with Study Session 17 material in the Level II CFA Program Curriculum ©.

Material:

I.         Options

II.        Swaps

III.       Interest Rate Derivatives

IV.       Credit Derivatives

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.