Financial Risk Manager Part 1

Explanation

Independent white noise and normal (Gaussian) white noise are both types of time series data with distinct characteristics:

Independent White Noise:

• Exhibits serial independence (values are not influenced by previous values)
• Has lack of serial correlation (no predictable patterns or trends)
• Does not necessarily follow a normal distribution

Normal (Gaussian) White Noise:

• Has all properties of independent white noise:
  • Serial independence
  • Lack of serial correlation
• Additionally follows a normal distribution
• Characterized by a bell-shaped curve defined by mean and standard deviation

Why Choice A is Correct: It accurately distinguishes that independent white noise has serial independence and lack of serial correlation, while normal white noise has these same properties PLUS normal distribution.

Why Other Choices are Incorrect:

• Choice B: Incorrectly swaps the definitions
• Choice C: Incorrectly focuses on mean-variance equality, which doesn't define these concepts
• Choice D: Incorrectly associates discrete/continuous distinction with these noise types

Both types of white noise can be used in time series analysis, with normal white noise being a more specific case that includes distributional assumptions.