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.
Study financial contracts pricing in markets with nonproportional costs and constraints.
The paper addresses pricing contingent claims by accounting for model uncertainty.
Measures incompleteness of financial markets using asset rank and acceptance set dimension.
Study compares rejection policies to acceptance policies for lead-time and price-sensitive demand.
Study optimizes pricing for new insurance business.
Model uses Preisach hysteresis to predict gig worker acceptance, reducing costs and improving fill rates.
In this paper we present a theoretical framework for determining dynamic ask and bid prices of derivatives using the theory of dynamic coherent acceptability indices in discrete time. We prove a version of the First Fundamental Theorem of Asset Pricing using the dynamic coherent risk measures. We introduce the dynamic …
We consider a trader who wants to direct his portfolio towards a set of acceptable wealths given by a convex risk measure. We propose a black-box algorithm, whose inputs are the joint law of stock prices and the convex risk measure, and whose outputs are the numerical values of initial capital requirement and the funct…
Estimates boundaries for acceptable bilateral gamma risk in financial markets.
This paper studies the utility maximization on the terminal wealth with random endowments and proportional transaction costs. To deal with unbounded random payoffs from some illiquid claims, we propose to work with the acceptable portfolios defined via the consistent price system (CPS) such that the liquidation value p…
The risk of financial positions is measured by the minimum amount of capital to raise and invest in eligible portfolios of traded assets in order to meet a prescribed acceptability constraint. We investigate nondegeneracy, finiteness and continuity properties of these risk measures with respect to multiple eligible ass…
We present an arbitrage free theoretical framework for modeling bid and ask prices of dividend paying securities in a discrete time setup using theory of dynamic acceptability indices. In the first part of the paper we develop the theory of dynamic subscale invariant performance measures, on a general probability space…
Why do a market's prices move up or down? Claims about causes are made without actual information, and accepted or dismissed based upon poor or non-existent evidence. Here we investigate the price movements that ended with Apple stock closing at \$500.00 on January 18, 2013. There is a ready explanation for this price …
Empirical study shows carriers ignore past shippers' behavior, focusing only on current actions.
The paper is aware of the importance of certain figures that are essential to an understanding of Credit Scoring models in credit acceptance process optimization, namely if the power of discrimination measured by Gini value is increased by 5% then the profit of the process can be increased monthly by about 1 500 kPLN (…
A new auction model for zero-marginal cost products.
New method for practical hedging under uncertainty in continuous time models.
The classical discrete time model of proportional transaction costs relies on the assumption that a feasible portfolio process has solvent increments at each step. We extend this setting in two directions, allowing for convex transaction costs and assuming that increments of the portfolio process belong to the sum of a…
Improved pricing method for illiquid assets using Lambert function.
Notwithstanding almost forty years of efforts, the market for paintings still lacks a widely accepted price index. In this paper, we introduce a simple and intuitive metric to construct such index. Our metric is based on the price of a painting divided by its area. This formulation rests on a solid mathematical foundat…
New financial model revises risk measure under NA condition.
Paper solves annuity pricing puzzle by minimizing risk.
The stochastic knapsack has been used as a model in wide ranging applications from dynamic resource allocation to admission control in telecommunication. In recent years, a variation of the model has become a basic tool in studying problems that arise in revenue management and dynamic/flexible pricing; and it is in thi…
The analysis of markets with indivisible goods and fixed exogenous prices has played an important role in economic models, especially in relation to wage rigidity and unemployment. This research report provides a mathematical and computational details associated to the mathematical programming based approaches proposed…
The importance of considering the volumes to analyze stock prices movements can be considered as a well-accepted practice in the financial area. However, when we look at the scientific production in this field, we still cannot find a unified model that includes volume and price variations for stock assessment purposes.…
Doubly fair dynamic pricing ensures equal prices for different groups over time.
The valuation process that economic agents undergo for investments with uncertain payoff typically depends on their statistical views on possible future outcomes, their attitudes toward risk, and, of course, the payoff structure itself. Yields vary across different investment opportunities and their interrelations are …
Develops a robust framework for pricing and hedging in discrete-time markets.
New model prices corporate bonds by accounting for non-hedgeable risk.
In this paper we derive robust super- and subhedging dualities for contingent claims that can depend on several underlying assets. In addition to strict super- and subhedging, we also consider relaxed versions which, instead of eliminating the shortfall risk completely, aim to reduce it to an acceptable level. This yie…
Pricing of high-dimensional options is a deep problem of the Theoretical Financial Mathematics. In this article we present a new class of Lévy driven models of stock markets. In our opinion, any market model should be based on a transparent and intuitively easily acceptable concept. In our case this is a linear system …
Neural networks predict stock prices better than traditional methods.
New star-shaped acceptability indexes generalize existing methods.
While the investors' responses to price changes and their price forecasts are well accepted major factors contributing to large price fluctuations in financial markets, our study shows that investors' heterogeneous and dynamic risk aversion (DRA) preferences may play a more critical role in the dynamics of asset price …
Paper compares two annuity pricing models and finds insurer underestimates value.
We consider a limit order book, where buyers and sellers register to trade a security at specific prices. The largest price buyers on the book are willing to offer is called the market bid price, and the smallest price sellers on the book are willing to accept is called the market ask price. Market ask price is always …
We develop closed-form approximations for European put options under stochastic volatility models.
The paper examines insurance market dynamics and optimal regulation.
Studies acceptable bundles on a partially punctured polydisk.
The paper discusses decades of stock market manipulation and its benefits.
A novel method uses blockchain transaction graphs for Bitcoin price prediction.
Study on acceptable bundles on a punctured disk.
A policy learns optimal truckload prices through reinforcement learning.
We propose the development of a prediction market for forecasting prices for "toxic assets" to be transferred from Irish banks to the National Asset Management Agency (NAMA). Such a market allows market participants to assume a stake in a security whose value is tied to a future event. We propose that securities are cr…
Transformer model predicts stock prices in Bangladesh's stock market.
New method assesses financial risk using model testing.
Simple conditions for comonotonic additive risk measures from acceptance sets.
For classification problems with significant class imbalance, subsampling can reduce computational costs at the price of inflated variance in estimating model parameters. We propose a method for subsampling efficiently for logistic regression by adjusting the class balance locally in feature space via an accept-reject …