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Derivatives algorithms, volume 1
Derivatives Algorithms provides a unique expert overview of the abstractions and coding methods which support real-world derivatives trading. Written by an industry professional with extensive experience in large-scale trading operations, it describes the fundamentals of library code structure, and innovative advanced solutions to thorny issues in implementation. For the reader already familiar with C++ and arbitrage-free pricing, the book offers an invaluable glimpse of how they combine on an industrial scale. Topics range from interface design through code generation to the protocols that support ever more complex trades and models.
eBook
Modeling derivatives in C++
This book is the definitive and most comprehensive guide to modeling derivatives in C++ today. Providing readers with not only the theory and math behind the models, as well as the fundamental concepts of financial engineering, but also actual robust object-oriented C++ code, this is a practical introduction to the most important derivative models used in practice today, including equity (standard and exotics including barrier, lookback, and Asian) and fixed income (bonds, caps, swaptions, swaps, credit) derivatives. The book provides complete C++ implementations for many of the most important derivatives and interest rate pricing models used on Wall Street including Hull-White, BDT, CIR, HJM, and LIBOR Market Model. London illustrates the practical and efficient implementations of these models in real-world situations and discusses the mathematical underpinnings and derivation of the models in a detailed yet accessible manner illustrated by many examples with numerical data as well as real market data. A companion CD contains quantitative libraries, tools, applications, and resources that will be of value to those doing quantitative programming and analysis in C++. Filled with practical advice and helpful tools, Modeling Derivatives in C++ will help readers succeed in understanding and implementing C++ when modeling all types of derivatives.
eBook
Derivatives Algorithms - Bones
Key Features:Unique focus on algorithms and implementationConsists of fundamentals as well as very advanced techniquesProvides a large-scale overview of the real work of quants.
Publication
Modeling Derivatives in C++
This book is the definitive and most comprehensive guide to modeling derivatives in C++ today. Providing readers with not only the theory and math behind the models, as well as the fundamental concepts of financial engineering, but also actual robust object-oriented C++ code, this is a practical introduction to the most important derivative models used in practice today, including equity (standard and exotics including barrier, lookback, and Asian) and fixed income (bonds, caps, swaptions, swaps, credit) derivatives. The book provides complete C++ implementations for many of the most important derivatives and interest rate pricing models used on Wall Street including Hull-White, BDT, CIR, HJM, and LIBOR Market Model. London illustrates the practical and efficient implementations of these models in real-world situations and discusses the mathematical underpinnings and derivation of the models in a detailed yet accessible manner illustrated by many examples with numerical data as well as real market data. A companion CD contains quantitative libraries, tools, applications, and resources that will be of value to those doing quantitative programming and analysis in C++. Filled with practical advice and helpful tools, Modeling Derivatives in C++ will help readers succeed in understanding and implementing C++ when modeling all types of derivatives.
Publication
Energy Power Risk
The book describes both mathematical and computational tools for energy and power risk management, deriving from first principles stochastic models for simulating commodity risk and how to design robust C++ to implement these models.
eBook
Eligibility and Safety of Triple Therapy for Hepatitis C: Lessons Learned from the First Experience in a Real World Setting
HCV protease inhibitors (PIs) boceprevir and telaprevir in combination with PEG-Interferon alfa and Ribavirin (P/R) is the new standard of care in the treatment of chronic HCV genotype 1 (GT1) infection. However, not every HCV GT1 infected patient is eligible for P/R/PI therapy. Furthermore phase III studies did not necessarily reflect real world as patients with advanced liver disease or comorbidities were underrepresented. The aim of our study was to analyze the eligibility and safety of P/R/PI treatment in a real world setting of a tertiary referral center.
All consecutive HCV GT1 infected patients who were referred to our hepatitis treatment unit between June and November 2011 were included. Patients were evaluated for P/R/PI according to their individual risk/benefit ratio based on 4 factors: Treatment-associated safety concerns, chance for SVR, treatment urgency and nonmedical patient related reasons. On treatment data were analyzed until week 12.
208 patients were included (F3/F4 64%, mean platelet count 169/nl, 40% treatment-naïve). Treatment was not initiated in 103 patients most frequently due to safety concerns. 19 patients were treated in phase II/III trials or by local centers and a triple therapy concept was initiated at our unit in 86 patients. Hospitalization was required in 16 patients; one patient died due to a gastrointestinal infection possibly related to treatment. A platelet count of <110/nl was associated with hospitalization as well as treatment failure. Overall, 128 patients were either not eligible for therapy or experienced a treatment failure at week 12.
P/R/PI therapies are complex, time-consuming and sometimes dangerous in a real world setting, especially in patients with advanced liver disease. A careful patient selection plays a crucial role to improve safety of PI based therapies. A significant number of patients are not eligible for P/R/PI, emphasizing the need for alternative therapeutic options.
Journal Article
Derivative-free optimization adversarial attacks for graph convolutional networks
In recent years, graph convolutional networks (GCNs) have emerged rapidly due to their excellent performance in graph data processing. However, recent researches show that GCNs are vulnerable to adversarial attacks. An attacker can maliciously modify edges or nodes of the graph to mislead the model’s classification of the target nodes, or even cause a degradation of the model’s overall classification performance. In this paper, we first propose a black-box adversarial attack framework based on derivative-free optimization (DFO) to generate graph adversarial examples without using gradient and apply advanced DFO algorithms conveniently. Second, we implement a direct attack algorithm (DFDA) using the Nevergrad library based on the framework. Additionally, we overcome the problem of large search space by redesigning the perturbation vector using constraint size. Finally, we conducted a series of experiments on different datasets and parameters. The results show that DFDA outperforms Nettack in most cases, and it can achieve an average attack success rate of more than 95% on the Cora dataset when perturbing at most eight edges. This demonstrates that our framework can fully exploit the potential of DFO methods in node classification adversarial attacks.
Journal Article