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Results for “exercise” · papers 18 · wiki 2
Academic Papers · 18arXiv q-fin live 18 · desk corpus 0
arXiv · arXiv q-fin · 2019

Closed form optimal exercise boundary of the American put option

We present three models of stock price with time-dependent interest rate, dividend yield, and volatility, respectively, that allow for explicit forms of the optimal exercise boundary of the finite maturity American put option. The optimal exercise boundary satisfies the nonlinear integral equation of Volterra type. We choose time-dependent parameters of the model so that the integral equation for the exercise boundar

Yerkin Kitapbayev
arXiv · arXiv q-fin · 2018

Pricing American Options by Exercise Rate Optimization

We present a novel method for the numerical pricing of American options based on Monte Carlo simulation and the optimization of exercise strategies. Previous solutions to this problem either explicitly or implicitly determine so-called optimal exercise regions, which consist of points in time and space at which a given option is exercised. In contrast, our method determines the exercise rates of randomized exercise s

Christian Bayer, Raúl Tempone, Sören Wolfers
arXiv · arXiv q-fin · 2016

Early exercise decision in American options with dividends, stochastic volatility and jumps

Using a fast numerical technique, we investigate a large database of investor suboptimal non-exercise of short maturity American call options on dividend-paying stocks listed on the Dow Jones. The correct modelling of the discrete dividend is essential for a correct calculation of the early exercise boundary as confirmed by theoretical insights. Pricing with stochastic volatility and jumps instead of the Black-Schole

Antonio Cosma, Stefano Galluccio, Paola Pederzoli, Olivier Scaillet
arXiv · arXiv q-fin · 2010

American Options Pricing under Stochastic Volatility: Approximation of the Early Exercise Surface and Monte Carlo Simulations

The aim of this study was to develop methods for evaluating the American-style option prices when the volatility of the underlying asset is described by a stochastic process. As part of this problem were developed techniques for modeling the early exercise surface of the American option. These methods of present work are compared to the complexity of modeling and computation speed. The paper presents the semi-analyti

Yu. A. Kuperin, P. A. Poloskov
arXiv · arXiv q-fin · 2008

Transformation methods for evaluating approximations to the optimal exercise boundary for linear and nonlinear Black-Scholes equations

The purpose of this survey chapter is to present a transformation technique that can be used in analysis and numerical computation of the early exercise boundary for an American style of vanilla options that can be modelled by class of generalized Black-Scholes equations. We analyze qualitatively and quantitatively the early exercise boundary for a linear as well as a class of nonlinear Black-Scholes equations with a

Daniel Sevcovic
arXiv · arXiv q-fin · 2007

An iterative algorithm for evaluating approximations to the optimal exercise boundary for a nonlinear Black-Scholes equation

The purpose of this paper is to analyze and compute the early exercise boundary for a class of nonlinear Black--Scholes equations with a nonlinear volatility which can be a function of the second derivative of the option price itself. A motivation for studying the nonlinear Black--Scholes equation with a nonlinear volatility arises from option pricing models taking into account e.g. nontrivial transaction costs, inve

Daniel Sevcovic
arXiv · arXiv q-fin · 2026

What Happens When Institutional Liquidity Enters Prediction Markets: Identification, Measurement, and a Synthetic Proof of Concept

Prediction markets are starting to look less like crowd polls and more like electronic markets. The central question is therefore no longer only whether these markets forecast well, but what happens when institutional liquidity enters: do spreads tighten, does price discovery improve, and do those gains actually reach the traders who are slowest to react when information arrives? This paper offers a research design f

Shaw Dalen
arXiv · arXiv q-fin · 2026

Benchmarking Deep Time Series Models for Equity Portfolios

Benchmarking forecasting architectures for daily equity portfolios is not just a prediction exercise. It also asks which model remains usable after preferences, costs, and portfolio constraints are imposed. We build a CRSP daily-stock benchmark for 15 deep and statistical time-series architectures over 2018--2024. The protocol combines common-window decile portfolios, stochastic multi-criteria acceptability analysis,

Aoxin Zhang, Yuhan Cheng, Kwanting Leung
arXiv · arXiv q-fin · 2025

Robust Pricing of Equity-Indexed Annuities under Uncertain Volatility and Stochastic Interest Rate

In this paper, we propose a novel methodology for pricing equity-indexed annuities featuring cliquet-style payoff structures and early surrender risk, using advanced financial modeling techniques. Specifically, the market is modeled by an equity index that follows an uncertain volatility framework, while the dynamics of the interest rate are captured by the Hull-White model. Due to the inherent complexity of the mark

Ludovic Goudenège, Andrea Molent, Antonino Zanette
arXiv · arXiv q-fin · 2024

Provisions and Economic Capital for Credit Losses

Based on supermodularity ordering properties, we show that convex risk measures of credit losses are nondecreasing w.r.t. credit-credit and, in a wrong-way risk setup, credit-market, covariances of elliptically distributed latent factors. These results support the use of such setups for computing credit provisions and economic capital or for conducting stress test exercises and risk management analysis.

Dorinel Bastide, Stéphane Crépey
arXiv · arXiv q-fin · 2020

Unconventional Policies Effects on Stock Market Volatility: A MAP Approach

Taking the European Central Bank unconventional policies as a reference, we suggest a class of Multiplicative Error Models (MEM) taylored to analyze the impact such policies have on stock market volatility. The new set of models, called MEM with Asymmetry and Policy effects (MAP), keeps the base volatility dynamics separate from a component reproducing policy effects, with an increase in volatility on announcement da

Demetrio Lacava, Giampiero M. Gallo, Edoardo Otranto
arXiv · arXiv q-fin · 2020

Quantification of Risk in Classical Models of Finance

This paper enhances the pricing of derivatives as well as optimal control problems to a level comprising risk. We employ nested risk measures to quantify risk, investigate the limiting behavior of nested risk measures within the classical models in finance and characterize existence of the risk-averse limit. As a result we demonstrate that the nested limit is unique, irrespective of the initially chosen risk measure.

Alois Pichler, Ruben Schlotter
arXiv · arXiv q-fin · 2017

Learning Agents in Black-Scholes Financial Markets: Consensus Dynamics and Volatility Smiles

Black-Scholes (BS) is the standard mathematical model for option pricing in financial markets. Option prices are calculated using an analytical formula whose main inputs are strike (at which price to exercise) and volatility. The BS framework assumes that volatility remains constant across all strikes, however, in practice it varies. How do traders come to learn these parameters? We introduce natural models of learni

Tushar Vaidya, Carlos Murguia, Georgios Piliouras
arXiv · arXiv q-fin · 2013

An analytic multi-currency model with stochastic volatility and stochastic interest rates

We introduce a tractable multi-currency model with stochastic volatility and correlated stochastic interest rates that takes into account the smile in the FX market and the evolution of yield curves. The pricing of vanilla options on FX rates can be performed effciently through the FFT methodology thanks to the affinity of the model Our framework is also able to describe many non trivial links between FX rates and in

Alessandro Gnoatto, Martino Grasselli
arXiv · arXiv q-fin · 2013

Robust Portfolios and Weak Incentives in Long-Run Investments

When the planning horizon is long, and the safe asset grows indefinitely, isoelastic portfolios are nearly optimal for investors who are close to isoelastic for high wealth, and not too risk averse for low wealth. We prove this result in a general arbitrage-free, frictionless, semimartingale model. As a consequence, optimal portfolios are robust to the perturbations in preferences induced by common option compensatio

Paolo Guasoni, Johannes Muhle-Karbe, Hao Xing
arXiv · arXiv q-fin · 2012

Consumer finance data generator - a new approach to Credit Scoring technique comparison

This paper aims to present a general idea of method comparison of Credit Scoring techniques. Any scorecard can be made in various methods based on variable transformations in the logistic regression model. To make a comparison and come up with the proof that one technique is better than another is a big challenge due to the limited availability of data. The same conclusion cannot be guaranteed when using other data f

Karol Przanowski, Jolanta Mamczarz
arXiv · arXiv q-fin · 2011

From Smile Asymptotics to Market Risk Measures

The left tail of the implied volatility skew, coming from quotes on out-of-the-money put options, can be thought to reflect the market's assessment of the risk of a huge drop in stock prices. We analyze how this market information can be integrated into the theoretical framework of convex monetary measures of risk. In particular, we make use of indifference pricing by dynamic convex risk measures, which are given as

Ronnie Sircar, Stephan Sturm
arXiv · arXiv q-fin · 2011

GPGPUs in computational finance: Massive parallel computing for American style options

The pricing of American style and multiple exercise options is a very challenging problem in mathematical finance. One usually employs a Least-Square Monte Carlo approach (Longstaff-Schwartz method) for the evaluation of conditional expectations which arise in the Backward Dynamic Programming principle for such optimal stopping or stochastic control problems in a Markovian framework. Unfortunately, these Least-Square

Gilles Pagès, Benedikt Wilbertz
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