PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 18, 2026Risks2 citationsOpen Access

Closed-Form Valuation of Discounted Cash Flows with Finite Poisson Arrivals in a Finite Horizon

View Full Paper
YKYuto KitamuraYKYuta KudoMSMakoto Shimoshimizu

Key Points

  • The aim is to derive a formula for expected discounted cash flows with constraints on time and arrivals.
  • Derivation of a closed-form expression for cash flows under a finite Poisson process
  • Consideration of constraints on both time and arrival counts
  • Numerical illustrations to demonstrate formula behavior across parameters
  • The closed-form expression converges to known infinite-horizon results under specific limits.
  • Numerical examples show varying behavior of the formula based on parameter changes.
  • The formula serves as an analytical tool for evaluating discounted revenues and losses in finite risk contexts.

Abstract

This paper derives a closed-form expression for the expected discounted value of aggregate cash flows when arrival times follow a Poisson process but both the time horizon and the number of arrivals are finite. The result provides a tractable analytical formula for the expected discounted sum under simultaneous constraints on time and arrival counts. We show that the expression converges to the well-known infinite-horizon and infinite-arrival results as limiting cases. Numerical illustrations demonstrate the behavior of the formula under different parameter values. The result can be interpreted as the valuation of a discounted compound Poisson process with finite constraints and may be useful in stochastic modeling and risk-analysis applications. The proposed formula provides a simple analytical tool for evaluating discounted losses or revenues in finite risk portfolios.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Kitamura et al. (2026) studied this question.

synapsesocial.com/papers/69e320fd40886becb65402a7https://doi.org/10.3390/risks14040090
Ask AI
Helpful
Bookmark
Share
View Full Paper