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Related papers: Pricing Options on Defaultable Stocks

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We consider a discrete-time approximation of paths of an Ornstein--Uhlenbeck process as a mean for estimation of a price of European call option in the model of financial market with stochastic volatility. The Euler--Maruyama approximation…

Computational Finance · Quantitative Finance 2016-01-07 Sergii Kuchuk-Iatsenko , Yuliya Mishura

We present a path integral method to derive closed-form solutions for option prices in a stochastic volatility model. The method is explained in detail for the pricing of a plain vanilla option. The flexibility of our approach is…

Pricing of Securities · Quantitative Finance 2008-12-02 D. Lemmens , M. Wouters , J. Tempere , S. Foulon

The determination of acceptability prices of contingent claims requires the choice of a stochastic model for the underlying asset price dynamics. Given this model, optimal bid and ask prices can be found by stochastic optimization. However,…

Pricing of Securities · Quantitative Finance 2019-01-31 Martin Glanzer , Georg Ch. Pflug , Alois Pichler

We introduce a general decision tree framework to value an option to invest/divest in a project, focusing on the model risk inherent in the assumptions made by standard real option valuation methods. We examine how real option values depend…

General Finance · Quantitative Finance 2018-09-06 Carol Alexander , Xi Chen

This paper presents a framework of imitating the principal investor's behavior for optimal pricing and hedging options. We construct a non-deterministic Markov decision process for modeling stock price change driven by the principal…

Pricing of Securities · Quantitative Finance 2022-01-14 Xin Jin

Risk-averse investors often wish to exclude stocks from their portfolios that bear high credit risk, which is a measure of a firm's likelihood of bankruptcy. This risk is commonly estimated by constructing signals from quarterly accounting…

Computational Finance · Quantitative Finance 2025-03-06 Maksim Papenkov , Beau Robinette

We investigate the pricing of financial options under the 2-hypergeometric stochastic volatility model. This is an analytically tractable model that reproduces the volatility smile and skew effects observed in empirical market data. Using a…

Probability · Mathematics 2017-08-04 Rúben Sousa , Ana Bela Cruzeiro , Manuel Guerra

We study the pricing problem for a European call option when the volatility of the underlying asset is random and follows the exponential Ornstein-Uhlenbeck model. The random diffusion model proposed is a two-dimensional market process that…

Pricing of Securities · Quantitative Finance 2008-12-02 Josep Perello , Ronnie Sircar , Jaume Masoliver

The introduction of transaction costs into the theory of option pricing could lead not only to the change of return for options, but also to the change of the volatility. On the base of assumption of the portfolio analysis, a new equation…

General Physics · Physics 2007-05-23 Alexander Morozovsky

We consider an asset whose risk-neutral dynamics are described by a general class of local-stochastic volatility models and derive a family of asymptotic expansions for European-style option prices and implied volatilities. Our implied…

Computational Finance · Quantitative Finance 2014-12-01 Matthew Lorig , Stefano Pagliarani , Andrea Pascucci

We compare observed corporate cumulative default probabilities to those calculated using a stochastic model based on an extension of the work of Black and Cox and find that corporations default as if via diffusive dynamics. The model, based…

Soft Condensed Matter · Physics 2008-12-02 Ting Lei , Raymond J. Hawkins

We price and replicate a variety of claims written on the log price $X$ and quadratic variation $[X]$ of a risky asset, modeled as a positive semimartingale, subject to stochastic volatility and jumps. The pricing and hedging formulas do…

Mathematical Finance · Quantitative Finance 2021-07-02 Peter Carr , Roger Lee , Matthew Lorig

The measures of roughness of the volatility in the litterature are based on the realized volatility of high frequency data. Some authors show that this leads to a biased estimate, and does not necessarily indicate roughness of the…

Mathematical Finance · Quantitative Finance 2022-08-01 Fabien Le Floc'h

This paper performs the numerical analysis and the computation of a Spread option in a market with imperfect liquidity. The number of shares traded in the stock market has a direct impact on the stock's price. Thus, we consider a…

Pricing of Securities · Quantitative Finance 2016-11-25 Ahmad Reza Yazdanian , T A Pirvu

This paper concerns a local volatility model in which volatility takes two possible values, and the specific value depends on whether the underlying price is above or below a given threshold value. The model is known, and a number of…

Mathematical Finance · Quantitative Finance 2024-05-17 Alexander Gairat , Vadim Shcherbakov

This paper studies equity basket options -- i.e., multi-dimensional derivatives whose payoffs depend on the value of a weighted sum of the underlying stocks -- and develops a new and innovative approach to ensure consistency between options…

Computational Finance · Quantitative Finance 2022-06-22 Lech A. Grzelak , Juliusz Jablecki , Dariusz Gatarek

This paper considers the pricing of long-term options on assets such as housing, where either government intervention or the economic nature of the asset is assumed to limit large falls in prices. The observed asset price is modelled by a…

Pricing of Securities · Quantitative Finance 2023-02-14 R. Guy Thomas

Equity default-swaps pay the holder a fixed amount of money when the underlying spot level touches a (far-down) barrier during the life of the instrument. While most pricing models give reasonable results when the barrier lies within the…

Pricing of Securities · Quantitative Finance 2010-03-30 Alex Langnau

Extracting the risk neutral density (RND) function from option prices is well defined in principle, but is very sensitive to errors in practice. For risk management, knowledge of the entire RND provides more information for Value-at-Risk…

Data Analysis, Statistics and Probability · Physics 2016-09-08 Jerome V. Healy , Maurice Dixon , Brian J. Read , Fang Fang Cai

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being…

Computational Finance · Quantitative Finance 2024-12-20 Shuaiqiang Liu , Cornelis W. Oosterlee , Sander M. Bohte