Doob's upcrossing estimate #
Given a discrete real-valued submartingale $(f_n)_{n \in \mathbb{N}}$, denoting by $U_N(a, b)$ the number of times $f_n$ crossed from below $a$ to above $b$ before time $N$, Doob's upcrossing estimate (also known as Doob's inequality) states that $$(b - a) \mathbb{E}[U_N(a, b)] \le \mathbb{E}[(f_N - a)^+].$$ Doob's upcrossing estimate is an important inequality and is central in proving the martingale convergence theorems.
Main definitions #
MeasureTheory.upperCrossingTime a b f N n: is the stopping time corresponding tofcrossing abovebthen-th time before timeN(if this does not occur then the value is taken to beN).MeasureTheory.lowerCrossingTime a b f N n: is the stopping time corresponding tofcrossing belowathen-th time before timeN(if this does not occur then the value is taken to beN).MeasureTheory.upcrossingStrat a b f N: is the predictable process which is 1 ifnis between a consecutive pair of lower and upper crossings and is 0 otherwise. Intuitively one might think of theupcrossingStratas the strategy of buying 1 share whenever the process crosses belowafor the first time after selling and selling 1 share whenever the process crosses abovebfor the first time after buying.MeasureTheory.upcrossingsBefore a b f N: is the number of timesfcrosses from belowato abovebbefore timeN.MeasureTheory.upcrossings a b f: is the number of timesfcrosses from belowato aboveb. This takes value inℝ≥0∞and so is allowed to be∞.
Main results #
MeasureTheory.Adapted.isStoppingTime_upperCrossingTime:upperCrossingTimeis a stopping time whenever the process it is associated to is adapted.MeasureTheory.Adapted.isStoppingTime_lowerCrossingTime:lowerCrossingTimeis a stopping time whenever the process it is associated to is adapted.MeasureTheory.Submartingale.mul_integral_upcrossingsBefore_le_integral_pos_part: Doob's upcrossing estimate.MeasureTheory.Submartingale.mul_lintegral_upcrossings_le_lintegral_pos_part: the inequality obtained by taking the supremum on both sides of Doob's upcrossing estimate.
References #
We mostly follow the proof from [Kallenberg, Foundations of modern probability][kallenberg2021]
Proof outline #
In this section, we will denote by $U_N(a, b)$ the number of upcrossings of $(f_n)$ from below $a$ to above $b$ before time $N$.
To define $U_N(a, b)$, we will construct two stopping times corresponding to when $(f_n)$ crosses
below $a$ and above $b$. Namely, we define
$$ \sigma_n := \inf \{n \ge \tau_n \mid f_n \le a\} \wedge N; $$
$$ \tau_{n + 1} := \inf \{n \ge \sigma_n \mid f_n \ge b\} \wedge N. $$
These are lowerCrossingTime and upperCrossingTime in our formalization which are defined
using MeasureTheory.hitting allowing us to specify a starting and ending time.
Then, we may simply define $U_N(a, b) := \sup \{n \mid \tau_n < N\}$.
Fixing $a < b \in \mathbb{R}$, we will first prove the theorem in the special case that
$0 \le f_0$ and $a \le f_N$. In particular, we will show
$$ (b - a) \mathbb{E}[U_N(a, b)] \le \mathbb{E}[f_N]. $$
This is MeasureTheory.integral_mul_upcrossingsBefore_le_integral in our formalization.
To prove this, we use the fact that given a non-negative, bounded, predictable process $(C_n)$ (i.e. $(C_{n + 1})$ is adapted), $(C \bullet f)_n := \sum_{k \le n} C_{k + 1}(f_{k + 1} - f_k)$ is a submartingale if $(f_n)$ is.
Define $C_n := \sum_{k \le n} \mathbf{1}_{[\sigma_k, \tau_{k + 1})}(n)$. It is easy to see that $(1 - C_n)$ is non-negative, bounded and predictable, and hence, given a submartingale $(f_n)$, $(1 - C) \bullet f$ is also a submartingale. Thus, by the submartingale property, $0 \le \mathbb{E}[((1 - C) \bullet f)_0] \le \mathbb{E}[((1 - C) \bullet f)_N]$ implying $$ \mathbb{E}[(C \bullet f)_N] \le \mathbb{E}[(1 \bullet f)_N] = \mathbb{E}[f_N] - \mathbb{E}[f_0]. $$
Furthermore, \begin{align} (C \bullet f)N & = \sum{n \le N} \sum_{k \le N} \mathbf{1}{[\sigma_k, \tau{k + 1})}(n)(f_{n + 1} - f_n)\ & = \sum_{k \le N} \sum_{n \le N} \mathbf{1}{[\sigma_k, \tau{k + 1})}(n)(f_{n + 1} - f_n)\ & = \sum_{k \le N} (f_{\sigma_k + 1} - f_{\sigma_k} + f_{\sigma_k + 2} - f_{\sigma_k + 1} + \cdots + f_{\tau_{k + 1}} - f_{\tau_{k + 1} - 1})\ & = \sum_{k \le N} (f_{\tau_{k + 1}} - f_{\sigma_k}) \ge \sum_{k < U_N(a, b)} (b - a) = (b - a) U_N(a, b) \end{align} where the inequality follows since for all $k < U_N(a, b)$, $f_{\tau_{k + 1}} - f_{\sigma_k} \ge b - a$ while for all $k > U_N(a, b)$, $f_{\tau_{k + 1}} = f_{\sigma_k} = f_N$ and $f_{\tau_{U_N(a, b) + 1}} - f_{\sigma_{U_N(a, b)}} = f_N - a \ge 0$. Hence, we have $$ (b - a) \mathbb{E}[U_N(a, b)] \le \mathbb{E}[(C \bullet f)_N] \le \mathbb{E}[f_N] - \mathbb{E}[f_0] \le \mathbb{E}[f_N], $$ as required.
To obtain the general case, we simply apply the above to $((f_n - a)^+)_n$.
lowerCrossingTimeAux a f c N is the first time f reached below a after time c before
time N.
Equations
- MeasureTheory.lowerCrossingTimeAux a f c N = MeasureTheory.hitting f (Set.Iic a) c N
Instances For
upperCrossingTime a b f N n is the first time before time N, f reaches
above b after f reached below a for the n - 1-th time.
Equations
- One or more equations did not get rendered due to their size.
- MeasureTheory.upperCrossingTime a b f N 0 = ⊥
Instances For
lowerCrossingTime a b f N n is the first time before time N, f reaches
below a after f reached above b for the n-th time.
Equations
- MeasureTheory.lowerCrossingTime a b f N n ω = MeasureTheory.hitting f (Set.Iic a) (MeasureTheory.upperCrossingTime a b f N n ω) N ω
Instances For
upcrossingStrat a b f N n is 1 if n is between a consecutive pair of lower and upper
crossings and is 0 otherwise. upcrossingStrat is shifted by one index so that it is adapted
rather than predictable.
Equations
- One or more equations did not get rendered due to their size.
Instances For
The number of upcrossings (strictly) before time N.
Equations
- MeasureTheory.upcrossingsBefore a b f N ω = sSup {n : ℕ | MeasureTheory.upperCrossingTime a b f N n ω < N}
Instances For
Doob's upcrossing estimate: given a real valued discrete submartingale f and real
values a and b, we have (b - a) * 𝔼[upcrossingsBefore a b f N] ≤ 𝔼[(f N - a)⁺] where
upcrossingsBefore a b f N is the number of times the process f crossed from below a to above
b before the time N.
Variant of the upcrossing estimate #
Now, we would like to prove a variant of the upcrossing estimate obtained by taking the supremum over $N$ of the original upcrossing estimate. Namely, we want the inequality $$ (b - a) \sup_N \mathbb{E}[U_N(a, b)] \le \sup_N \mathbb{E}[f_N]. $$ This inequality is central for the martingale convergence theorem as it provides a uniform bound for the upcrossings.
We note that on top of taking the supremum on both sides of the inequality, we had also used the monotone convergence theorem on the left hand side to take the supremum outside of the integral. To do this, we need to make sure $U_N(a, b)$ is measurable and integrable. Integrability is easy to check as $U_N(a, b) ≤ N$ and so it suffices to show measurability. Indeed, by noting that $$ U_N(a, b) = \sum_{i = 1}^N \mathbf{1}_{\{U_N(a, b) < N\}} $$ $U_N(a, b)$ is measurable as $\{U_N(a, b) < N\}$ is a measurable set since $U_N(a, b)$ is a stopping time.
The number of upcrossings of a realization of a stochastic process (upcrossings takes value
in ℝ≥0∞ and so is allowed to be ∞).
Equations
- MeasureTheory.upcrossings a b f ω = ⨆ (N : ι), ↑(MeasureTheory.upcrossingsBefore a b f N ω)
Instances For
A variant of Doob's upcrossing estimate obtained by taking the supremum on both sides.