A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Upper bound on withdrawal success for geometric Levy alpha-stable wealth process.
Maximizes probability of completing investment schedules with optimal portfolio weights.
Maximizing withdrawal success in a pooled annuity fund with multiple annuitants.
Optimal withdrawal strategy for DC pension plans maximizes total withdrawals while managing risk.
Withdrawal guarantees ensure the periodical deduction of a constant dollar-amount from a fund investment for a fixed number of periods. If the fund depletes before the last withdrawal, the guarantor has to finance the outstanding withdrawals. We derive a robust hedging strategy which leads to closed form solutions for …
Project forecasts liquidity withdrawal using machine learning models.
This paper presents numerical algorithm and results for pricing a capital protection option offered by many asset managers for investment portfolios to take advantage of market growth and protect savings. Under optimal withdrawal policyholder behaviour the pricing of such a product is an optimal stochastic control prob…
Optimal tontine strategy maximizes withdrawals while minimizing shortfall.
Optimizes retirement spending and asset allocation to maximize withdrawals and shortfall.
Proposes GLWB-LTC for enhanced life care annuities with dynamic withdrawal strategies and stochastic interest rates.
A variable annuity contract with Guaranteed Minimum Withdrawal Benefit (GMWB) promises to return the entire initial investment through cash withdrawals during the policy life plus the remaining account balance at maturity, regardless of the portfolio performance. Under the optimal withdrawal strategy of a policyholder,…
The paper analyzes GMWB annuities in low interest rate environments.
Investigates optimal withdrawal strategies in VA contracts with tax and ratchet mechanisms.
The paper models ATM cash withdrawal chaos and forecasts using deep learning.
Optimizes cash management in ATM networks to reduce costs and increase revenue.
A model explains why 4% is a safe retirement withdrawal rate.
Under the optimal withdrawal strategy of a policyholder, the pricing of variable annuities with Guaranteed Minimum Withdrawal Benefit (GMWB) is an optimal stochastic control problem. The surrender feature available in marketed products allows termination of the contract before maturity, making it also an optimal stoppi…
Develops a new method for pricing GMWBs with jumps and stochastic interest rates.
Existence of stochastic financial equilibria giving rise to semimartingale asset prices is established under a general class of assumptions. These equilibria are expressed in real terms and span complete markets or markets with withdrawal constraints.We deal with random endowment density streams which admit jumps and g…
this is a duplicate submission(original is arXiv:1612.02141). Hence want to withdraw it
The Australian Government uses the means-test as a way of managing the pension budget. Changes in Age Pension policy impose difficulties in retirement modelling due to policy risk, but any major changes tend to be `grandfathered' meaning that current retirees are exempt from the new changes. In 2015, two important chan…
Valuing Guaranteed Lifelong Withdrawal Benefit (GLWB) has attracted significant attention from both the academic field and real world financial markets. As remarked by Forsyth and Vetzal the Black and Scholes framework seems to be inappropriate for such long maturity products. They propose to use a regime switching mod…
There is a technical issue in the analysis that is not easily fixable. We, therefore, withdraw the submission. Sorry for the inconvenience.
Valuing Guaranteed Minimum Withdrawal Benefit (GMWB) has attracted significant attention from both the academic field and real world financial markets. As remarked by Yang and Dai, the Black and Scholes framework seems to be inappropriate for such a long maturity products. Also Chen Vetzal and Forsyth in showed that th…
We generalize the classic Shiller cyclically adjusted price-earnings ratio (CAPE) used for prediction of future total returns of the stock market. We treat earnings growth as exogenous. The difference between log wealth and log earnings is modeled as an autoregression of order 1 with linear trend 4.6% and Gaussian inno…
A variable annuity contract with Guaranteed Minimum Withdrawal Benefit (GMWB) promises to return the entire initial investment through cash withdrawals during the contract plus the remaining account balance at maturity, regardless of the portfolio performance. Under the optimal(dynamic) withdrawal strategy of a policyh…
New method schedules learning rate without stopping time, outperforming existing methods.
This paper proposes a method to select project schedules with the lowest risk.
The guaranteed minimum withdrawal benefit (GMWB) rider, as an add on to a variable annuity (VA), guarantees the return of premiums in the form of peri- odic withdrawals while allowing policyholders to participate fully in any market gains. GMWB riders represent an embedded option on the account value with a fee structu…
Optimizes financial auditor schedules to reduce time and costs.
ScheduleFree+ improves large language model training without schedules or learning rates.
Cosine schedule is optimal for discrete diffusion models.
Optimal learning rate schedules derived for various tasks.
The paper optimizes interpolation schedules in generative models to improve sampling accuracy.
New method converts and optimizes sampling schedules for generative models.
In this paper we explore an identity in distribution of hitting times of a finite variation process (Yor's process) and a diffusion process (geometric Brownian motion with affine drift), which arise from various applications in financial mathematics. As a result, we provide analytical solutions to the fair charge of va…
This paper proposes a system-agnostic policy for dynamic scheduling.
The paper presents a multi-power law for predicting loss curves across different learning rate schedules.
Current clinical practice to monitor patients' health follows either regular or heuristic-based lab test (e.g. blood test) scheduling. Such practice not only gives rise to redundant measurements accruing cost, but may even lead to unnecessary patient discomfort. From the computational perspective, heuristic-based test …
More and more companies have deployed machine learning (ML) clusters, where deep learning (DL) models are trained for providing various AI-driven services. Efficient resource scheduling is essential for maximal utilization of expensive DL clusters. Existing cluster schedulers either are agnostic to ML workload characte…
Optimal learning rate schedules for SGD in changing data distributions.
A large collection of financial contracts offering guaranteed minimum benefits are often posed as control problems, in which at any point in the solution domain, a control is able to take any one of an uncountable number of values from the admissible set. Often, such contracts specify that the holder exert control at a…
Enhances multi-project scheduling with multiple priority rules.
Learning-rate schedules for large models match optimization theory closely, leading to better training.
The study introduces anytime learning schedules for large language models without fixed horizons.
With online calendar services gaining popularity worldwide, calendar data has become one of the richest context sources for understanding human behavior. However, event scheduling is still time-consuming even with the development of online calendars. Although machine learning based event scheduling models have automate…
Annealed importance sampling (AIS) is a common algorithm to estimate partition functions of useful stochastic models. One important problem for obtaining accurate AIS estimates is the selection of an annealing schedule. Conventionally, an annealing schedule is often determined heuristically or is simply set as a linear…
This work uses reinforcement learning to optimize task scheduling and execution in a dynamic multi-agent warehouse environment.