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In the problem of out-of-distribution (OOD) detection, the usage of auxiliary data as outlier data for fine-tuning has demonstrated encouraging performance. However, previous methods have suffered from a trade-off between classification…

Machine Learning · Computer Science 2023-08-03 Hyunjun Choi , JaeHo Chung , Hawook Jeong , Jin Young Choi

For $n$ assets and discrete-time rebalancing, the probability to complete a given schedule of investments and withdrawals is maximized over progressively measurable portfolio weight functions. Applications consider two assets, namely the…

Portfolio Management · Quantitative Finance 2024-10-22 Hayden Brown

Under the Basel II standards, the Operational Risk (OpRisk) advanced measurement approach allows a provision for reduction of capital as a result of insurance mitigation of up to 20%. This paper studies the behaviour of different insurance…

Risk Management · Quantitative Finance 2010-11-04 Gareth W. Peters , Aaron D. Byrnes , Pavel V. Shevchenko

Regulatory requirements dictate that financial institutions must calculate risk capital (funds that must be retained to cover future losses) at least annually. Procedures for doing this have been well-established for many years, but recent…

Computational Finance · Quantitative Finance 2017-05-22 Peter Mitic

This paper focuses on stochastic orders and its applications : policy limits and deductibles. Further, many applications and some examples are given : comparison of two families of copulas, individual and collective risk model, reinsurance…

Statistics Theory · Mathematics 2015-01-28 Halim Zeghdoudi , Meriem Bouhadjar , Mohamed Riad Remita

This paper develops a risk-adjusted alternative to standard optimal policy learning (OPL) for observational data by importing Roy's (1952) safety-first principle into the treatment assignment problem. We formalize a welfare functional that…

Econometrics · Economics 2025-10-07 Giovanni Cerulli , Francesco Caracciolo

For a mining strategy we define the notion of "profit lag" as the minimum time it takes to be profitable after that moment. We compute closed forms for the profit lag and the revenue ratio for the strategies "selfish mining" and…

Cryptography and Security · Computer Science 2020-10-07 Cyril Grunspan , Ricardo Pérez-Marco

Learning from off-policy data is essential for sample-efficient reinforcement learning. In the present work, we build on the insight that the advantage function can be understood as the causal effect of an action on the return, and show…

Machine Learning · Computer Science 2024-02-21 Hsiao-Ru Pan , Bernhard Schölkopf

In this paper we study data from the yearly reports the four major Swedish non-life insurers have sent to the Swedish Financial Supervisory Authority (FSA). We aim at finding marginal distributions of, and dependence between, losses on the…

Risk Management · Quantitative Finance 2015-01-06 Jonas Alm

Autonomous crypto trading systems often spend most of their design effort on finding entries, while exits are left to fixed rules that are rarely tested in a systematic way. This paper examines whether better stop-loss and take-profit…

Artificial Intelligence · Computer Science 2026-05-01 Nathan Li , Aikins Laryea , Yigit Ihlamur

The Merton investment-consumption problem is fundamental, both in the field of finance, and in stochastic control. An important extension of the problem adds transaction costs, which is highly relevant from a financial perspective but also…

General Economics · Economics 2024-02-14 Martin Herdegen , David Hobson , Alex S. L. Tse

The performance of learning models often deteriorates when deployed in out-of-sample environments. To ensure reliable deployment, we propose a stability evaluation criterion based on distributional perturbations. Conceptually, our stability…

Machine Learning · Statistics 2024-05-07 Jose Blanchet , Peng Cui , Jiajin Li , Jiashuo Liu

The frequent occurrence of natural disasters has posed significant challenges to society, necessitating the urgent development of effective risk management strategies. From the early informal community-based risk sharing mechanisms to…

Risk Management · Quantitative Finance 2025-08-06 Lichen Wang , Shijia Hua , Yuyuan Liu , Zhengyuan Lu , Liang Zhang , Linjie Liu , Attila Szolnoki

We study the problem of optimal long term portfolio selection with a view to beat a benchmark. Two kinds of objectives are considered. One concerns the probability of outperforming the benchmark and seeks either to minimise the decay rate…

Probability · Mathematics 2017-12-04 Anatolii A. Puhalskii

Most of the banks' operational risk internal models are based on loss pooling in risk and business line categories. The parameters and outputs of operational risk models are sensitive to the pooling of the data and the choice of the risk…

Risk Management · Quantitative Finance 2015-05-12 Vivien Brunel

We establish a high-dimensional statistical learning framework for individualized asset allocation. Our proposed methodology addresses continuous-action decision-making with a large number of characteristics. We develop a discretization…

Machine Learning · Statistics 2022-11-09 Yi Ding , Yingying Li , Rui Song

We hypothesize that portfolio sorts based on the V/P ratio generate excess returns and consist of companies that are undervalued for prolonged periods. Results, for the US market show that high V/P portfolios outperform low V/P portfolios…

Econometrics · Economics 2025-06-03 Ahmad Haboub , Aris Kartsaklas , Vasilis Sarafidis

The European insurance sector will soon be faced with the application of Solvency 2 regulation norms. It will create a real change in risk management practices. The ORSA approach of the second pillar makes the capital allocation an…

Risk Management · Quantitative Finance 2015-06-15 Véronique Maume-Deschamps , Didier Rullière , Khalil Said

In recent years, multi-factor strategies have gained increasing popularity in the financial industry, as they allow investors to have a better understanding of the risk drivers underlying their portfolios. Moreover, such strategies promise…

Statistical Finance · Quantitative Finance 2021-11-12 Gabriele D'Acunto , Paolo Bajardi , Francesco Bonchi , Gianmarco De Francisci Morales

Accurate modeling of operational risk is important for a bank and the finance industry as a whole to prepare for potentially catastrophic losses. One approach to modeling operational is the loss distribution approach, which requires a bank…

Risk Management · Quantitative Finance 2021-07-09 Daniel Hadley , Harry Joe , Natalia Nolde