When are Markov Matrices also Martingales?

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Discussion Overview

The discussion centers on the conditions under which a Markov Chain can also be classified as a Martingale. Participants explore theoretical aspects, specific examples, and mathematical formulations related to Markov matrices and their properties.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested, Mathematical reasoning

Main Points Raised

  • One participant notes that the only Random Walk that is a Martingale is the symmetric one, where the probabilities are equal (p = 1/2).
  • Another participant emphasizes the need to clarify what is meant by expected value in the context of an nxn Markov matrix, questioning the applicability of the concept to discrete observations.
  • A participant mentions that risk-neutral probabilities can be martingales even when not equal to 0.5, suggesting that martingales can be created through binomial processes.
  • One participant inquires about income decile transition matrices and hypothesizes that if the matrix stabilizes at the kth iteration, the dominant eigenvalues would be close to the kth root of unity.
  • Two participants pose a related question regarding a specific formula involving a constant matrix and inquire about its correctness, with one asserting that the formula is indeed correct.

Areas of Agreement / Disagreement

Participants express differing views on the conditions for Markov Chains to be Martingales, with no consensus reached on the broader applicability of the concepts discussed. The correctness of the mathematical formula presented is agreed upon by some participants.

Contextual Notes

There are limitations regarding the definitions of expected value and the specific contexts in which the Markov matrices are being discussed, which may affect the interpretations and conclusions drawn by participants.

WWGD
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Are there known conditions under which a Markov Chain is also a Martingale? I know only that the only Random Walk that is a Martingale is the symmetric one, i.e., p= 1-p =1/2.
 
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If you have an nxn Markov matrix the first thing you need to decide is what does expected value mean? You have n discrete observations, are they the numbers 1 through n or something else? If your Markov chain is a random walk on a graph of n vertices the question doesn't even obviously make sense
 
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WWGD said:
TL;DR Summary: I know only that the only Random Walk that is a Martingale is the symmetric one, i.e., p= 1-p =1/2.
Risk neutral probabilities are martingales but <> 0.5

https://en.wikipedia.org/wiki/Risk-neutral_measure

Can arbitrarily create a martingale with any binomial process
 
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Thank you both. Im thinking about income decile transition matrices. If the matrix M tends to stabilize at abouth the kth iteration, I assume the dominant eigenvalues of M would be close to the kth root of unity, right?
 
Related question: Let M be an ## n \times n ## constant matrix, with constant value ##c## and let ##k ## a positive Integer. Is this formular correct : ##M^{k}=n^{k-1} c^{k} J ##, where ##J## is the constant ## n \times n ## matrix with ##c ==1##?
 
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WWGD said:
Related question: Let M be an ## n \times n ## constant matrix, with constant value ##c## and let ##k ## a positive Integer. Is this formular correct : ##M^{k}=n^{k-1} c^{k} J ##, where ##J## is the constant ## n \times n ## matrix with ##c ==1##?
The formula is correct.
 
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