Quantitative Risk Management Using Python
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Quantitative Risk Management Using Python
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Author : Peng Liu
language : en
Publisher: Springer Nature
Release Date : 2025-09-17
Quantitative Risk Management Using Python written by Peng Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-17 with Computers categories.
Gain an understanding of various financial risks, the benefits of portfolio diversification, and the fundamental trade-off between risk and return. This book takes an in-depth journey into the world of quantitative risk management using Python, focusing on credit and market risk, with an extension to model risk. You'll start by reviewing the different types of financial risk, the benefit of diversification in a portfolio, and the fundamental trade-off between risk and return. The book then offers an in-depth look at managing credit and market risk in today's dynamic markets, all with practical Python implementations. Moving on, you’ll examine common hedging strategies used to manage investment positions, along with practical implementations on evaluating risk-adjusted, as well as downside risk measures. Finally, you’ll be introduced to common risks related to the development and use of machine learning models in finance. Whether you're a finance professional, academic, or student, Quantitative Risk Management Using Python will empower you to make informed decisions in today's complex financial landscape. What You Will Learn Explore techniques to assess and manage the risk of default by borrowers or counterparties. Identify, measure, and mitigate risks arising from fluctuations in market prices. Understand how derivatives can be employed for risk management purposes. Delve into both static and dynamic hedging techniques to protect investment positions, including practical applications for evaluating risk-adjusted and downside risk measures. Identify and address risks associated with the development and deployment of machine learning models in financial contexts. Who This Book Is For Finance professionals, academics, and students seeking to deepen their understanding of Quantitative Risk Management using Python, especially those interested in navigating the intricate domains of credit, market and model risk within the financial sector and beyond.
Quantitative Asset Management Factor Investing And Machine Learning For Institutional Investing
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Author : Michael Robbins
language : en
Publisher: McGraw Hill Professional
Release Date : 2023-06-24
Quantitative Asset Management Factor Investing And Machine Learning For Institutional Investing written by Michael Robbins and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-24 with Business & Economics categories.
Augment your asset allocation strategy with machine learning and factor investing for unprecedented returns and growth Whether you’re managing institutional portfolios or private wealth, Quantitative Asset Management will open your eyes to a new, more successful way of investing—one that harnesses the power of big data and artificial intelligence. This innovative guide walks you through everything you need to know to fully leverage these revolutionary tools. Written from the perspective of a seasoned financial investor making use of technology, it details proven investing methods, striking a rare balance between providing important technical information without burdening you with overly complex investing theory. Quantitative Asset Management is organized into four thematic sections: Part I reveals invaluable lessons for planning and governance of investment decision-making. Part 2 discusses quantitative financial modeling, covering important topics like overfitting, mitigating unrealistic assumptions, managing substitutions, enhancing minority classes, and missing data imputation. Part 3 shows how to develop a strategy into an investment product, including the alpha models, risk models, implementation, backtesting, and cost optimization. Part 4 explains how to measure performance, learn from mistakes, manage risk, and survive financial tragedies. With Quantitative Asset Management, you have everything you need to build your awareness of other markets, ask the right questions and answer them effectively, and drive steady profits even through times of great uncertainty.
Principles Of Computer Security Comptia Security And Beyond Sixth Edition Exam Sy0 601
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Author : Wm. Arthur Conklin
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-07-29
Principles Of Computer Security Comptia Security And Beyond Sixth Edition Exam Sy0 601 written by Wm. Arthur Conklin and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-29 with Computers categories.
Fully updated computer security essentials—mapped to the CompTIA Security+ SY0-601 exam Save 10% on any CompTIA exam voucher! Coupon code inside. Learn IT security fundamentals while getting complete coverage of the objectives for the latest release of CompTIA Security+ certification exam SY0-601. This thoroughly revised, full-color textbook covers how to secure hardware, systems, and software. It addresses new threats and cloud environments, and provides additional coverage of governance, risk, compliance, and much more. Written by a team of highly respected security educators, Principles of Computer Security: CompTIA Security+TM and Beyond, Sixth Edition (Exam SY0-601) will help you become a CompTIA-certified computer security expert while also preparing you for a successful career. Find out how to: Ensure operational, organizational, and physical security Use cryptography and public key infrastructures (PKIs) Secure remote access, wireless networks, and virtual private networks (VPNs) Authenticate users and lock down mobile devices Harden network devices, operating systems, and applications Prevent network attacks, such as denial of service, spoofing, hijacking, and password guessing Combat viruses, worms, Trojan horses, and rootkits Manage e-mail, instant messaging, and web security Explore secure software development requirements Implement disaster recovery and business continuity measures Handle computer forensics and incident response Understand legal, ethical, and privacy issues Online content features: Test engine that provides full-length practice exams and customized quizzes by chapter or exam objective Each chapter includes: Learning objectives Real-world examples Try This! and Cross Check exercises Tech Tips, Notes, and Warnings Exam Tips End-of-chapter quizzes and lab projects
American Book Publishing Record
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Author :
language : en
Publisher:
Release Date : 2005
American Book Publishing Record written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with American literature categories.
Machine Learning For Financial Risk Management With Python
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Author : Abdullah Karasan
language : en
Publisher: O'Reilly Media
Release Date : 2022-01-18
Machine Learning For Financial Risk Management With Python written by Abdullah Karasan and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-18 with categories.
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models. Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models Capture different aspects of liquidity with a Gaussian mixture model Use machine learning models for fraud detection Identify corporate risk using the stock price crash metric Explore a synthetic data generation process to employ in financial risk
Machine Learning For Financial Risk Management With Python
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Author : Abdullah Karasan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-12-07
Machine Learning For Financial Risk Management With Python written by Abdullah Karasan and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Business & Economics categories.
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models. Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models Capture different aspects of liquidity with a Gaussian mixture model Use machine learning models for fraud detection Identify corporate risk using the stock price crash metric Explore a synthetic data generation process to employ in financial risk.
Comptia Security All In One Exam Guide Sixth Edition Exam Sy0 601
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Author : Wm. Arthur Conklin
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-09
Comptia Security All In One Exam Guide Sixth Edition Exam Sy0 601 written by Wm. Arthur Conklin and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-09 with Computers categories.
This fully updated study guide covers every topic on the current version of the CompTIA Security+ exam Get complete coverage of all objectives included on the CompTIA Security+ exam SY0-601 from this comprehensive resource. Written by a team of leading information security experts, this authoritative guide fully addresses the skills required to perform essential security functions and to secure hardware, systems, and software. You’ll find learning objectives at the beginning of each chapter, exam tips, practice exam questions, and in-depth explanations. Designed to help you pass the exam with ease, this definitive volume also serves as an essential on-the-job reference. Covers all exam domains, including: Threats, Attacks, and Vulnerabilities Architecture and Design Implementation Operations and Incident Response Governance, Risk, and Compliance Online content includes: 250 practice exam questions Test engine that provides full-length practice exams and customizable quizzes by chapter or by exam domain
Mike Meyers Comptia Security Certification Guide Third Edition Exam Sy0 601
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Author : Mike Meyers
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-05-07
Mike Meyers Comptia Security Certification Guide Third Edition Exam Sy0 601 written by Mike Meyers and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-07 with Computers categories.
An up-to-date CompTIA Security+ exam guide from training and exam preparation guru Mike Meyers Take the latest version of the CompTIA Security+ exam (exam SY0-601) with confidence using the comprehensive information contained in this highly effective self-study resource. Like the test, the guide goes beyond knowledge application and is designed to ensure that security personnel anticipate security risks and guard against them. In Mike Meyers’ CompTIA Security+ Certification Guide, Third Edition (Exam SY0-601), the bestselling author and leading authority on CompTIA A+ certification brings his proven methodology to IT security. Mike covers all exam objectives in small, digestible modules that allow you to focus on individual skills as you move through a broad and complex set of skills and concepts. The book features hundreds of accurate practice questions as well as a toolbox of the author’s favorite network security related freeware/shareware. Provides complete coverage of every objective for exam SY0-601 Online content includes 20+ lab simulations, video training, a PDF glossary, and 180 practice questions Written by computer security and certification experts Mike Meyers and Scott Jernigan
Probability Models And Risk Management For Actuaries With Python
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Author : Grant Richman
language : en
Publisher: Independently Published
Release Date : 2025-09-06
Probability Models And Risk Management For Actuaries With Python written by Grant Richman and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-06 with Business & Economics categories.
Build actuarial-grade probability models and risk management workflows-end to end, in Python Turn deep actuarial theory into real, working models. This comprehensive, code-driven reference takes you from probability foundations through solvency capital, with a laser focus on practical implementation. Each of the 33 dense chapters follows the same high-impact flow: rigorous theory → exam-style multiple-choice questions → complete, runnable Python demonstrations for real insurance problems. Whether you price risks, set reserves, allocate capital, or build internal models, this book shows you exactly how to do it-step by step, with reproducible code and clear actuarial reasoning. Why you'll love it Tight, no-fluff structure: theory you can trust, checks for understanding, and full Python implementations in every chapter Designed for working actuaries and advanced students: life, P&C, and ERM applications throughout Built for production: methods scale from classroom to capital planning, with robust diagnostics and validation What you'll master Probability and statistical foundations: transforms, convergence, asymptotics, change of measure Insurance severity and frequency modeling: Pareto/GB2/Weibull, Poisson/NB/zero-inflation, GLMs, Tweedie, GLMMs Dependence and tail risk: copulas (elliptical/Archimedean/vine), common-shock, multivariate EVT, GEV/GPD Aggregate risk and computation: compound models, Panjer recursion, De Pril, FFT, saddlepoint, importance sampling Bayesian and credibility methods: hierarchical models, MCMC, empirical Bayes, experience rating Time series and processes: NHPP, renewal, Hawkes, INAR/INGARCH, volatility modeling Reserving and development: chain ladder, Mack, GLM reserving, bootstrap, IFRS 17 measurement Life contingencies and survival: hazards, frailty, multiple decrement, Thiele equations Capital and solvency: VaR/TVaR/expectiles, Euler allocation, Solvency II/RBC, ORSA, model risk, stress testing ALM and markets: stochastic interest and inflation, ESGs, reinsurance optimization, ruin theory Code you can run Clean, commented Python that implements estimation, simulation, and validation Practical toolchain with NumPy, SciPy, pandas, statsmodels, and visualization Reproducible workflows for pricing, reserving, capital, and ERM analytics Perfect for Practicing actuaries building pricing, reserving, or capital models ERM and risk professionals responsible for aggregation and allocation Quantitative analysts and data scientists entering insurance Graduate-level actuarial and risk management courses Upgrade your actuarial toolkit with reproducible, regulator-ready methods
The Internet Investor
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Author : J. Timothy Maude
language : en
Publisher: Jist Publishing
Release Date : 1999
The Internet Investor written by J. Timothy Maude and has been published by Jist Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Business & Economics categories.
Tim Maude knows investing, and he knows the Internet. He has compiled a list of trustworthy Internet sites for the investor who goes online. The guts of the book is the listings and reviews of thousands of Interact investing sites, designed to help the cyber investor gather trustworthy information.