Martingale theory: Collection of examples and counterexamples
The aim of this question is to collect interesting examples and counterexamples in martingale theory. There is a huge variety of such (counter)examples available here on StackExchange but I always have a hard time when I try to locate a specific example/question. I believe that it would be a benefit to make this knowledge easier to access. For this reason I would like to create a (big) list with references to related threads.
Martingale theory is a broad topic, and therefore I suggest to focus on time-discrete martingales $(M_n)_{n in mathbb{N}}$. I am well aware that this is still a quite broad field. To make this list a helpful tool (e.g. for answering questions) please make sure to give a short but concise description of each (counter)example which you list in your answer.
Related literature:
- Jordan M. Stoyanov: Counterexamples in Probability, Dover.
- Joseph P. Romano, Andrew F. Siegel: Counterexamples in probability and statistics, CRC Press.
probability-theory examples-counterexamples martingales big-list
add a comment |
The aim of this question is to collect interesting examples and counterexamples in martingale theory. There is a huge variety of such (counter)examples available here on StackExchange but I always have a hard time when I try to locate a specific example/question. I believe that it would be a benefit to make this knowledge easier to access. For this reason I would like to create a (big) list with references to related threads.
Martingale theory is a broad topic, and therefore I suggest to focus on time-discrete martingales $(M_n)_{n in mathbb{N}}$. I am well aware that this is still a quite broad field. To make this list a helpful tool (e.g. for answering questions) please make sure to give a short but concise description of each (counter)example which you list in your answer.
Related literature:
- Jordan M. Stoyanov: Counterexamples in Probability, Dover.
- Joseph P. Romano, Andrew F. Siegel: Counterexamples in probability and statistics, CRC Press.
probability-theory examples-counterexamples martingales big-list
An example which I like but not found in text books is the following: consider $(0,1)$ with Lebesgue measure and let $X_n(omega)=n$ if $0<omega <frac 1 n$, and $0$ otherwise. This sequence happens to be a martingale which converges almost surely but not in the mean (hence not uniformly integrable).
– Kavi Rama Murthy
Oct 28 at 12:01
add a comment |
The aim of this question is to collect interesting examples and counterexamples in martingale theory. There is a huge variety of such (counter)examples available here on StackExchange but I always have a hard time when I try to locate a specific example/question. I believe that it would be a benefit to make this knowledge easier to access. For this reason I would like to create a (big) list with references to related threads.
Martingale theory is a broad topic, and therefore I suggest to focus on time-discrete martingales $(M_n)_{n in mathbb{N}}$. I am well aware that this is still a quite broad field. To make this list a helpful tool (e.g. for answering questions) please make sure to give a short but concise description of each (counter)example which you list in your answer.
Related literature:
- Jordan M. Stoyanov: Counterexamples in Probability, Dover.
- Joseph P. Romano, Andrew F. Siegel: Counterexamples in probability and statistics, CRC Press.
probability-theory examples-counterexamples martingales big-list
The aim of this question is to collect interesting examples and counterexamples in martingale theory. There is a huge variety of such (counter)examples available here on StackExchange but I always have a hard time when I try to locate a specific example/question. I believe that it would be a benefit to make this knowledge easier to access. For this reason I would like to create a (big) list with references to related threads.
Martingale theory is a broad topic, and therefore I suggest to focus on time-discrete martingales $(M_n)_{n in mathbb{N}}$. I am well aware that this is still a quite broad field. To make this list a helpful tool (e.g. for answering questions) please make sure to give a short but concise description of each (counter)example which you list in your answer.
Related literature:
- Jordan M. Stoyanov: Counterexamples in Probability, Dover.
- Joseph P. Romano, Andrew F. Siegel: Counterexamples in probability and statistics, CRC Press.
probability-theory examples-counterexamples martingales big-list
probability-theory examples-counterexamples martingales big-list
asked Oct 28 at 8:18
saz
77.8k756120
77.8k756120
An example which I like but not found in text books is the following: consider $(0,1)$ with Lebesgue measure and let $X_n(omega)=n$ if $0<omega <frac 1 n$, and $0$ otherwise. This sequence happens to be a martingale which converges almost surely but not in the mean (hence not uniformly integrable).
– Kavi Rama Murthy
Oct 28 at 12:01
add a comment |
An example which I like but not found in text books is the following: consider $(0,1)$ with Lebesgue measure and let $X_n(omega)=n$ if $0<omega <frac 1 n$, and $0$ otherwise. This sequence happens to be a martingale which converges almost surely but not in the mean (hence not uniformly integrable).
– Kavi Rama Murthy
Oct 28 at 12:01
An example which I like but not found in text books is the following: consider $(0,1)$ with Lebesgue measure and let $X_n(omega)=n$ if $0<omega <frac 1 n$, and $0$ otherwise. This sequence happens to be a martingale which converges almost surely but not in the mean (hence not uniformly integrable).
– Kavi Rama Murthy
Oct 28 at 12:01
An example which I like but not found in text books is the following: consider $(0,1)$ with Lebesgue measure and let $X_n(omega)=n$ if $0<omega <frac 1 n$, and $0$ otherwise. This sequence happens to be a martingale which converges almost surely but not in the mean (hence not uniformly integrable).
– Kavi Rama Murthy
Oct 28 at 12:01
add a comment |
1 Answer
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convergence results:
pointwise convergence of martingale $M_n$ does not imply $sup_n mathbb{E}(M_n^+)<infty$ (this means that the converse of the martingal convergence theorem does not hold true)
martingale which converges almost surely but not in $L^1$ (see also here)
martingale $(M_n)_n$ such that $M_n to infty$ almost surely
non-trivial martingale which converges almost surely to $0$ (see also here)
martingale which converges in probability but not almost surely (see also here)
martingale which converges in distribution but not almost surely/in probability (see also Section 2.2 here)
uniform integrability:
martingale $(M_n)_n$ for which $M_{infty} = lim_n M_n$ exists a.s. but $mathbb{E}(M_{infty} mid mathcal{F}_n) neq M_n$ (see also here and here; note that such a martingale cannot be uniformly integrable and cannot converge in $L^1$)
uniformly integrable martingale $(M_n)_n$ such that $mathbb{E}left( sup_{n in mathbb{N}} |M_n| right) = infty$.
sample path behaviour:
oscillating martingale with bounded sample paths
non-trivial martingale which is constant with positive probability
martingale which is non-constant and non-negative
Stopping times (Optional stopping/sampling theorem):
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $mathbb{E}(M_{tau}) neq mathbb{E}(M_0)$
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $M_{n wedge tau} to M_{tau}$ almost surely but not in $L^1$ (see the very first part of the the linked answer)
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $tau<infty$ almost surely and $mathbb{E}(tau)=infty$
Other
stochastic process $(M_n)_n$ which satisfies $mathbb{E}(M_{n+1} mid M_n) = M_n$ for all $n$ but which is not a martingale
martingale which is not bounded in $L^1$
add a comment |
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1 Answer
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oldest
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
convergence results:
pointwise convergence of martingale $M_n$ does not imply $sup_n mathbb{E}(M_n^+)<infty$ (this means that the converse of the martingal convergence theorem does not hold true)
martingale which converges almost surely but not in $L^1$ (see also here)
martingale $(M_n)_n$ such that $M_n to infty$ almost surely
non-trivial martingale which converges almost surely to $0$ (see also here)
martingale which converges in probability but not almost surely (see also here)
martingale which converges in distribution but not almost surely/in probability (see also Section 2.2 here)
uniform integrability:
martingale $(M_n)_n$ for which $M_{infty} = lim_n M_n$ exists a.s. but $mathbb{E}(M_{infty} mid mathcal{F}_n) neq M_n$ (see also here and here; note that such a martingale cannot be uniformly integrable and cannot converge in $L^1$)
uniformly integrable martingale $(M_n)_n$ such that $mathbb{E}left( sup_{n in mathbb{N}} |M_n| right) = infty$.
sample path behaviour:
oscillating martingale with bounded sample paths
non-trivial martingale which is constant with positive probability
martingale which is non-constant and non-negative
Stopping times (Optional stopping/sampling theorem):
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $mathbb{E}(M_{tau}) neq mathbb{E}(M_0)$
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $M_{n wedge tau} to M_{tau}$ almost surely but not in $L^1$ (see the very first part of the the linked answer)
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $tau<infty$ almost surely and $mathbb{E}(tau)=infty$
Other
stochastic process $(M_n)_n$ which satisfies $mathbb{E}(M_{n+1} mid M_n) = M_n$ for all $n$ but which is not a martingale
martingale which is not bounded in $L^1$
add a comment |
convergence results:
pointwise convergence of martingale $M_n$ does not imply $sup_n mathbb{E}(M_n^+)<infty$ (this means that the converse of the martingal convergence theorem does not hold true)
martingale which converges almost surely but not in $L^1$ (see also here)
martingale $(M_n)_n$ such that $M_n to infty$ almost surely
non-trivial martingale which converges almost surely to $0$ (see also here)
martingale which converges in probability but not almost surely (see also here)
martingale which converges in distribution but not almost surely/in probability (see also Section 2.2 here)
uniform integrability:
martingale $(M_n)_n$ for which $M_{infty} = lim_n M_n$ exists a.s. but $mathbb{E}(M_{infty} mid mathcal{F}_n) neq M_n$ (see also here and here; note that such a martingale cannot be uniformly integrable and cannot converge in $L^1$)
uniformly integrable martingale $(M_n)_n$ such that $mathbb{E}left( sup_{n in mathbb{N}} |M_n| right) = infty$.
sample path behaviour:
oscillating martingale with bounded sample paths
non-trivial martingale which is constant with positive probability
martingale which is non-constant and non-negative
Stopping times (Optional stopping/sampling theorem):
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $mathbb{E}(M_{tau}) neq mathbb{E}(M_0)$
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $M_{n wedge tau} to M_{tau}$ almost surely but not in $L^1$ (see the very first part of the the linked answer)
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $tau<infty$ almost surely and $mathbb{E}(tau)=infty$
Other
stochastic process $(M_n)_n$ which satisfies $mathbb{E}(M_{n+1} mid M_n) = M_n$ for all $n$ but which is not a martingale
martingale which is not bounded in $L^1$
add a comment |
convergence results:
pointwise convergence of martingale $M_n$ does not imply $sup_n mathbb{E}(M_n^+)<infty$ (this means that the converse of the martingal convergence theorem does not hold true)
martingale which converges almost surely but not in $L^1$ (see also here)
martingale $(M_n)_n$ such that $M_n to infty$ almost surely
non-trivial martingale which converges almost surely to $0$ (see also here)
martingale which converges in probability but not almost surely (see also here)
martingale which converges in distribution but not almost surely/in probability (see also Section 2.2 here)
uniform integrability:
martingale $(M_n)_n$ for which $M_{infty} = lim_n M_n$ exists a.s. but $mathbb{E}(M_{infty} mid mathcal{F}_n) neq M_n$ (see also here and here; note that such a martingale cannot be uniformly integrable and cannot converge in $L^1$)
uniformly integrable martingale $(M_n)_n$ such that $mathbb{E}left( sup_{n in mathbb{N}} |M_n| right) = infty$.
sample path behaviour:
oscillating martingale with bounded sample paths
non-trivial martingale which is constant with positive probability
martingale which is non-constant and non-negative
Stopping times (Optional stopping/sampling theorem):
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $mathbb{E}(M_{tau}) neq mathbb{E}(M_0)$
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $M_{n wedge tau} to M_{tau}$ almost surely but not in $L^1$ (see the very first part of the the linked answer)
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $tau<infty$ almost surely and $mathbb{E}(tau)=infty$
Other
stochastic process $(M_n)_n$ which satisfies $mathbb{E}(M_{n+1} mid M_n) = M_n$ for all $n$ but which is not a martingale
martingale which is not bounded in $L^1$
convergence results:
pointwise convergence of martingale $M_n$ does not imply $sup_n mathbb{E}(M_n^+)<infty$ (this means that the converse of the martingal convergence theorem does not hold true)
martingale which converges almost surely but not in $L^1$ (see also here)
martingale $(M_n)_n$ such that $M_n to infty$ almost surely
non-trivial martingale which converges almost surely to $0$ (see also here)
martingale which converges in probability but not almost surely (see also here)
martingale which converges in distribution but not almost surely/in probability (see also Section 2.2 here)
uniform integrability:
martingale $(M_n)_n$ for which $M_{infty} = lim_n M_n$ exists a.s. but $mathbb{E}(M_{infty} mid mathcal{F}_n) neq M_n$ (see also here and here; note that such a martingale cannot be uniformly integrable and cannot converge in $L^1$)
uniformly integrable martingale $(M_n)_n$ such that $mathbb{E}left( sup_{n in mathbb{N}} |M_n| right) = infty$.
sample path behaviour:
oscillating martingale with bounded sample paths
non-trivial martingale which is constant with positive probability
martingale which is non-constant and non-negative
Stopping times (Optional stopping/sampling theorem):
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $mathbb{E}(M_{tau}) neq mathbb{E}(M_0)$
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $M_{n wedge tau} to M_{tau}$ almost surely but not in $L^1$ (see the very first part of the the linked answer)
martingale $(M_n)_{n in mathbb{N}}$ and stopping time $tau$ such that $tau<infty$ almost surely and $mathbb{E}(tau)=infty$
Other
stochastic process $(M_n)_n$ which satisfies $mathbb{E}(M_{n+1} mid M_n) = M_n$ for all $n$ but which is not a martingale
martingale which is not bounded in $L^1$
edited Nov 26 at 20:34
answered Oct 28 at 8:20
saz
77.8k756120
77.8k756120
add a comment |
add a comment |
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An example which I like but not found in text books is the following: consider $(0,1)$ with Lebesgue measure and let $X_n(omega)=n$ if $0<omega <frac 1 n$, and $0$ otherwise. This sequence happens to be a martingale which converges almost surely but not in the mean (hence not uniformly integrable).
– Kavi Rama Murthy
Oct 28 at 12:01