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Optimization-Based Models for Measuring and Hedging Risk in Fixed Income Markets
The global fixed income market is an enormous financial market whose value by far exceeds that of the public stock markets. The interbank market consists of interest rate derivatives, whose primary purpose is to manage interest rate risk. The credit market primarily consists of the bond market, which links investors to companies, institutions, and governments with borrowing needs. This dissertation takes an optimization perspective upon modeling both these areas of the fixed-income market. Legislators on the national markets require financial actors to value their financial assets in accordance with market prices. Thus, prices of many assets, which are not publicly traded, must be determined...
Decision Making under Uncertainty in Financial Markets
This thesis addresses the topic of decision making under uncertainty, with particular focus on financial markets. The aim of this research is to support improved decisions in practice, and related to this, to advance our understanding of financial markets. Stochastic optimization provides the tools to determine optimal decisions in uncertain environments, and the optimality conditions of these models produce insights into how financial markets work. To be more concrete, a great deal of financial theory is based on optimality conditions derived from stochastic optimization models. Therefore, an important part of the development of financial theory is to study stochastic optimization models th...
Mathematical Reviews
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Finance India
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Skog & Forskning
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Lectures on Stochastic Programming
Optimization problems involving stochastic models occur in almost all areas of science and engineering, such as telecommunications, medicine, and finance. Their existence compels a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. Readers will find coverage of the basic concepts of modeling these problems, including recourse actions and the nonanticipativity principle. The book also includes the theory of two-stage and multistage stochastic programming problems; the current state of the theory on chance (probabilistic) constraints, including the structure of the problems, optimality theory, and duality; and statistical inference in and risk-averse approaches to stochastic programming.
Cloud FinOps
Despite many uncertainties in cloud computing, one truth is evident: costs will always tend to go up unless you’re actively engaged in the process. Whether you’re new to managing cloud spend or a seasoned pro, this book will clarify the often misunderstood workings of cloud billing fundamentals and provide expert strategies on creating a culture of cloud cost management in your organization. Drawing on real-world examples of successes and failures of large-scale cloud spenders, this book outlines a road map for building a culture of FinOps in your organization. Beginning with the fundamental concepts required to understand cloud billing concepts, you’ll learn how to enable an efficient and effective FinOps machine. Learn how the cloud works when it comes to financial management Set up a FinOps team and build a framework for making spend efficiency a priority Examine the anatomy of a cloud bill and learn how to manage it Get operational recipes for maximizing cloud efficiency Understand how to motivate engineering teams to take cost-saving actions Explore the FinOps lifecycle: Inform, Optimize, and Operate Learn the DNA of a highly functional cloud FinOps culture
Introduction to the Theory of Statistical Inference
Based on the authors' lecture notes, this text presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Unlike related textbooks, it combines the theoretical basis of statistical inference with a useful applied toolbox that includes linear models. Suitable for a second semester undergraduate course on statistical inference, the text offers proofs to support the mathematics and does not require any use of measure theory. It illustrates core concepts using cartoons and provides solutions to all examples and problems.
