Stopped-history Markov future-reward lower bound transfers to the killed record expectation
ProvedAvramDividend.Classical.levy_killed_record_expectation_ge_markov_rewardFor a finite Lévy stopping time, a past-observable survival event, and a measurable nonnegative future reward, suppose the actual killed first-record reward is pointwise at least the past discount times the shifted future reward on that event. Then the expected actual killed weight is at least the past discounted survival expectation times the independent fresh-path reward expectation. This is the expectation-level lower-comparison bridge for the q-scale regenerative step.
Preamble
import Mathlib.MeasureTheory.Integral.Lebesgue.Basic import Theorems.Thm_AvramDividend_Classical_levy_finiteStopping_killedDiscount_futureReward_factor open MeasureTheory ProbabilityTheory Set Classical open scoped NNReal ENNReal open AvramDividend.Classical
Formal statement
theorem AvramDividend.Classical.levy_killed_record_expectation_ge_markov_reward
{Ω : Type*} [mΩ : MeasurableSpace Ω] {P : Measure Ω}
{𝓕 : Filtration ℝ≥0 mΩ}
(X : SpectrallyNegativeLevy P 𝓕)
(τ : Ω → ℝ≥0)
(hτ : ∀ s : ℝ≥0, MeasurableSet[𝓕 s] {ω : Ω | τ ω ≤ s})
(A : Set Ω)
(hA : MeasurableSet[
(isStoppingTime_withTop_of_le_events τ hτ).measurableSpace] A)
(hAfine : MeasurableSet A)
(hτfine : Measurable τ)
(discount : ℝ≥0 → ℝ≥0∞) (hd : Measurable discount)
(reward : (ℝ≥0 → ℝ) → ℝ≥0∞) (hr : Measurable reward)
(killedWeight : Ω → ℝ≥0∞)
(hLower : ∀ ω : Ω,
(if ω ∈ A then discount (τ ω) *
reward (fun t : ℝ≥0 =>
X.X (τ ω + t) ω - X.X (τ ω) ω)
else 0) ≤ killedWeight ω) :
(∫⁻ ω, if ω ∈ A then discount (τ ω) else 0 ∂P) *
(∫⁻ ω, reward (fun t : ℝ≥0 => X.X t ω) ∂P) ≤
(∫⁻ ω, killedWeight ω ∂P) := by sorrySource
Avram, Palmowski and Pistorius (2007), Proposition 1; stopped Lévy strong-Markov reward factorisation.