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EDITORIAL: SPECIAL ISSUE ON FINANCIAL MATHEMATICS AND QUANTITATIVE FINANCE [PDF]
The ANZIAM Journal, 2021 The nexus between world financial markets and the discipline of quantitative finance, which is heavily based on mathematics and statistics, has become increasingly clearer as a result of enormously expanded global financial derivative markets over the ...
Song-Ping Zhu, Xin-Jiang He, Xiaoping Lu
semanticscholar +4 more sources
A quantitative and a dual version of the Halmos-Savage theorem with applications to mathematical finance [PDF]
The Annals of Probability, 1996 The celebrated theorem of Halmos and Savage implies that if M is a set of ℙ-absolutely continuous probability measures Q on (Ω ℱ, ℙ) such that each A ∈ ℱ, ℙ(A) > 0 is charged by some Q ∈ M, that is, Q(A) > 0 (where the choice of Q depends on the set A), then - provided M is closed under countable convex combinations - we can find Q ∈ M with full ...
Klein, Irene, Schachermayer, Walter
semanticscholar +5 more sources
Money Mathematics: Examining Ethics Education in Quantitative Finance [PDF]
Journal of Business Ethics Education, 2012 The field of quantitative analysis is often mistaken to be a discipline free from ethical burdens. The quantitative financial analyst or “quant” profession holds a position of significant responsibility as the keeper of mathematical models used in complex derivative security pricing and risk management. Despite this responsibility very few postgraduate
Jason West
semanticscholar +5 more sources
Sig-SDEs model for quantitative finance [PDF]
International Conference on AI in Finance, 2020 Mathematical models, calibrated to data, have become ubiquitous to make key decision processes in modern quantitative finance. In this work, we propose a novel framework for data-driven model selection by integrating a classical quantitative setup with a
Imanol Perez Arribas +2 more
semanticscholar +1 more source
Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction [PDF]
, 2021 The task we consider is portfolio construction in a speculative market, a fundamental problem in modern finance. While various empirical works now exist to explore deep learning in finance, the theory side is almost non-existent. In this work, we focus on developing a theoretical framework for understanding the use of data augmentation for deep ...
arxiv +1 more source
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance [PDF]
ACM International Conference on AI in Finance, 2021, 2021 Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning approaches has a number of limitations. The development of deep reinforcement learning techniques is partially addressing these issues.
arxiv +1 more source
, 2021 This is an informal and sketchy review of six topical, somewhat unrelated subjects in quantitative finance: rough volatility models; random covariance matrix theory; copulas; crowded trades; high-frequency trading & market stability; and "radical complexity" & scenario based (macro)economics.
arxiv +1 more source
QuantCloud: Big Data Infrastructure for Quantitative Finance on the Cloud
IEEE Transactions on Big Data, 2018 In this paper, we present the QuantCloud infrastructure, designed for performing big data analytics in modern quantitative finance. Through analyzing market observations, quantitative finance (QF) utilizes mathematical models to search for subtle ...
Peng Zhang +3 more
semanticscholar +1 more source
Prediction and Portfolio Optimization in Quantitative Trading Using Machine Learning Techniques
International Journal for Research in Applied Science and Engineering Technology, 2023 : Quantitative trading is an automated procedure in which trading techniques and judgments are performed using mathematical models. Quantitative trading involves a vast spectrum of computational methods, such as statistics, physics, or machine learning ...
G. Patil
semanticscholar +1 more source
Mathematical modeling in Quantitative Finance and Computational Economics
, 2021 The first part of my PhD Thesis deals with different Machine Learning techniques mainly applied to solve financial engineering and risk management issues. After a short literary review, every chapter analyzes a particular topic linked to the implementation of these models, showing the most suitable methodologies able to solve it efficiently.
openaire +2 more sources