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This paper extends an option-theoretic approach to estimate liquidity spreads for corporate bonds. Inspired by Longstaff's equity market framework and subsequent work by Koziol and Sauerbier on risk-free zero-coupon bonds, the model views…

Pricing of Securities · Quantitative Finance 2025-01-22 Pietro Rossi , Paolo Spezzati , Riccardo Tedeschi

We derive a backward and forward nonlinear PDEs that govern the implied volatility of a contingent claim whenever the latter is well-defined. This would include at least any contingent claim written on a positive stock price whose payoff at…

Computational Finance · Quantitative Finance 2019-07-18 Peter Carr , Andrey Itkin , Sasha Stoikov

We study pricing and superhedging strategies for game options in an imperfect market with default. We extend the results obtained by Kifer in \cite{Kifer} in the case of a perfect market model to the case of an imperfect market with…

Mathematical Finance · Quantitative Finance 2017-07-04 Roxana Dumitrescu , Marie-Claire Quenez , Agnès Sulem

We study the pricing and the hedging of claim {\psi} which depends on the default times of two firms A and B. In fact, we assume that, in the market, we can not buy or sell any defaultable bond of the firm B but we can only trade…

Pricing of Securities · Quantitative Finance 2012-09-27 Stephane Goutte , Armand Ngoupeyou

In high-dimensional regression modelling, the number of candidate covariates to be included in the predictor is quite large, and variable selection is crucial. In this work, we propose a new penalty able to guarantee both sparse variable…

Methodology · Statistics 2022-12-19 Daniele Cuntrera , Luigi Augugliaro , Vito M. R. Muggeo

A solution to a portfolio optimization problem is always conditioned by constraints on the initial capital and the price of the available market assets. If a risk neutral measure is known, then the price of each asset is the discounted…

Optimization and Control · Mathematics 2025-07-10 Argimiro Arratia , Henryk Gzyl

We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representative of the true likelihood of a prediction can be achieved…

Machine Learning · Computer Science 2021-06-24 Christian Tomani , Sebastian Gruber , Muhammed Ebrar Erdem , Daniel Cremers , Florian Buettner

This paper develops a two-dimensional structural framework for valuing credit default swaps and corporate bonds in the presence of default contagion. Modelling the values of related firms as correlated geometric Brownian motions with…

Pricing of Securities · Quantitative Finance 2008-12-02 Helen Haworth , Christoph Reisinger , William Shaw

In this paper we propose a new nonparametric approach to interacting failing systems (FS), that is systems whose probability of failure is not negligible in a fixed time horizon, a typical example being firms and financial bonds. The main…

Applications · Statistics 2010-10-19 Pasquale Cirillo , Jürg Hüsler , Pietro Muliere

Networked-guarantee loans may cause the systemic risk related concern of the government and banks in China. The prediction of default of enterprise loans is a typical extremely imbalanced prediction problem, and the networked-guarantee make…

Computational Engineering, Finance, and Science · Computer Science 2020-06-09 Dawei Cheng , Zhibin Niu , Yi Tu , Liqing Zhang

We show that, for the purpose of pricing Swaptions, the Swap rate and the corresponding Forward rates can be considered lognormal under a single martingale measure. Swaptions can then be priced as options on a basket of lognormal assets and…

Computational Engineering, Finance, and Science · Computer Science 2007-05-23 Alexandre d'Aspremont

We derive an arbitrage free relationship between recovery swap rates, digital default swap spreads and conventional CDS spreads, and argue that the fair forward recovery rate used in recovery swaps must contain a convexity premium over the…

Pricing of Securities · Quantitative Finance 2010-01-07 Arthur M. Berd

Default risk calculus plays a crucial role in portfolio optimization when the risky asset is under threat of bankruptcy. However, traditional stochastic control techniques are not applicable in this scenario, and additional assumptions are…

Portfolio Management · Quantitative Finance 2023-05-10 José A. Salmerón , Giulia Di Nunno , Bernardo D'Auria

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for…

Machine Learning · Computer Science 2018-05-03 Anahita Namvar , Mohammad Siami , Fethi Rabhi , Mohsen Naderpour

This paper deals with applications of coherent risk measures to pricing in incomplete markets. Namely, we study the No Good Deals pricing technique based on coherent risk. Two forms of this technique are presented: one defines a good deal…

Probability · Mathematics 2008-12-02 Alexander S. Cherny

In this paper we focus on the problem of assigning uncertainties to single-point predictions generated by a deterministic model that outputs a continuous variable. This problem applies to any state-of-the-art physics or engineering models…

Machine Learning · Statistics 2020-03-12 Enrico Camporeale , Algo Carè

This paper introduces a novel stochastic model for credit spreads. The stochastic approach leverages the diffusion of default intensities via a CIR++ model and is formulated within a risk-neutral probability space. Our research primarily…

Risk Management · Quantitative Finance 2026-01-09 Mohamed Ben Alaya , Ahmed Kebaier , Djibril Sarr

This article presents a generic model for pricing financial derivatives subject to counterparty credit risk. Both unilateral and bilateral types of credit risks are considered. Our study shows that credit risk should be modeled as American…

Pricing of Securities · Quantitative Finance 2018-04-09 David Lee

We develop a new approach to multi-label conformal prediction in which we aim to output a precise set of promising prediction candidates with a bounded number of incorrect answers. Standard conformal prediction provides the ability to adapt…

Machine Learning · Computer Science 2022-02-16 Adam Fisch , Tal Schuster , Tommi Jaakkola , Regina Barzilay

Calibration allows predictions to be reliably interpreted as probabilities by decision makers. We propose a decision-theoretic calibration error, the Calibration Decision Loss (CDL), defined as the maximum improvement in decision payoff…

Machine Learning · Computer Science 2024-10-14 Lunjia Hu , Yifan Wu