Geometric upper bound for killed first-record expectations from a one-step renewal inequality
ProvedAvramDividend.Classical.killed_firstRecord_grid_geometric_expected_upper_of_stepavram-dividendexpectationfirst-recordgeometric-boundstopped-renewal
For a spectrally negative Levy process with nonnegative discount and record spacing, let w_n be the actual killed discounted strict first-record payment at level b+n h. If successive expected payments satisfy a one-step multiplicative upper recurrence with nonnegative ratio p, then the expected n-th payment is at most h p^n. The initial bound follows from the proven pointwise payment bound and the probability law; the remaining recurrence is kept explicit for the strong-Markov renewal step.
Preamble
import Mathlib.MeasureTheory.Integral.Lebesgue.Basic import Mathlib.Data.ENNReal.Real import Mathlib.Algebra.Order.GroupWithZero.Defs import Definitions.Def_AvramDividend_Classical_ReflectionBarrier import Theorems.Thm_AvramDividend_Classical_chat08_killed_record_discount_weight_le_cell open MeasureTheory open scoped NNReal ENNReal open AvramDividend.Classical
Formal statement
theorem AvramDividend.Classical.killed_firstRecord_grid_geometric_expected_upper_of_step
{Ω : Type*} [mΩ : MeasurableSpace Ω] {P : Measure Ω}
{𝓕 : Filtration ℝ≥0 mΩ}
(X : SpectrallyNegativeLevy P 𝓕)
(a b q h p : ℝ)
(hq : 0 ≤ q) (hh : 0 ≤ h) (hp : 0 ≤ p)
(w : ℕ → Ω → ℝ≥0∞)
(hw : ∀ n ω, w n ω =
if twoSidedExitUpTime X (b + (n : ℝ) * h) ω <
barrierRuinTime X a ω then
ENNReal.ofReal
(h * Real.exp (-(q * (twoSidedExitUpTime X (b + (n : ℝ) * h) ω).toReal)))
else 0)
(hstep : ∀ n,
(∫⁻ ω, w (n + 1) ω ∂P) ≤
ENNReal.ofReal p * (∫⁻ ω, w n ω ∂P)) :
∀ n, (∫⁻ ω, w n ω ∂P) ≤ ENNReal.ofReal (h * p ^ n) := by sorrySource
Root-linked helper for AvramDividend.Classical.barrierSupValue_stopped_renewal_upper_bound (c6c3b1d0-2c2f-41ce-a5a0-2771efcc3a69): discharges the geometric induction and base expectation using Proved chat08_killed_record_discount_weight_le_cell, while exposing the one-step killed first-record recurrence as the remaining stochastic premise.