Products related to Complexity:
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Market Risk Analysis, Quantitative Methods in Finance
Written by leading market risk academic, Professor Carol Alexander, Quantitative Methods in Finance forms part one of the Market Risk Analysis four volume set.Starting from the basics, this book helps readers to take the first step towards becoming a properly qualified financial risk manager and asset manager, roles that are currently in huge demand.Accessible to intelligent readers with a moderate understanding of mathematics at high school level or to anyone with a university degree in mathematics, physics or engineering, no prior knowledge of finance is necessary.Instead the emphasis is on understanding ideas rather than on mathematical rigour, meaning that this book offers a fast-track introduction to financial analysis for readers with some quantitative background, highlighting those areas of mathematics that are particularly relevant to solving problems in financial risk management and asset management.Unique to this book is a focus on both continuous and discrete time finance so that Quantitative Methods in Finance is not only about the application of mathematics to finance; it also explains, in very pedagogical terms, how the continuous time and discrete time finance disciplines meet, providing a comprehensive, highly accessible guide which will provide readers with the tools to start applying their knowledge immediately. All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study.Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the accompanying CD-ROM.Empirical examples and case studies specific to this volume include: Principal component analysis of European equity indices;Calibration of Student t distribution by maximum likelihood;Orthogonal regression and estimation of equity factor models;Simulations of geometric Brownian motion, and of correlated Student t variables;Pricing European and American options with binomial trees, and European options with the Black-Scholes-Merton formula;Cubic spline fitting of yields curves and implied volatilities;Solution of Markowitz problem with no short sales and other constraints;Calculation of risk adjusted performance metrics including generalised Sharpe ratio, omega and kappa indices.
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A Complete Guide to the Futures Market : Technical Analysis, Trading Systems, Fundamental Analysis, Options, Spreads, and Trading Principles
The essential futures market reference guide A Complete Guide to the Futures Market is the comprehensive resource for futures traders and analysts.Spanning everything from technical analysis, trading systems, and fundamental analysis to options, spreads, and practical trading principles, A Complete Guide is required reading for any trader or investor who wants to successfully navigate the futures market. Clear, concise, and to the point, this fully revised and updated second edition provides a solid foundation in futures market basics, details key analysis and forecasting techniques, explores advanced trading concepts, and illustrates the practical application of these ideas with hundreds of market examples.A Complete Guide to the Futures Market: Details different trading and analytical approaches, including chart analysis, technical indicators and trading systems, regression analysis, and fundamental market models. Separates misleading market myths from reality. Gives step-by-step instruction for developing and testing original trading ideas and systems. Illustrates a wide range of option strategies, and explains the trading implications of each. Details a wealth of practical trading guidelines and market insights from a recognized trading authority. Trading futures without a firm grasp of this market’s realities and nuances is a recipe for losing money.A Complete Guide to the Futures Market offers serious traders and investors the tools to keep themselves on the right side of the ledger.
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Flood Risk Change : A Complexity Perspective
Flood Risk Change: A Complexity Perspective focuses on the dynamic nature of flood risks and follows a systemic approach - including environmental, socioeconomic and socio-technical factors for modeling and managing flood risk change.Readers will gain a more complete picture of the topic for understanding the complexity of flood risk change, both from human and natural causes of flooding.The book includes a mix of theory (introduction to complex system science from the flood risk management perspective) and case studies.It features maps and figures focusing on the system components as well as on the dynamic interactions between the drivers of change. Researchers studying flood risk, environmental engineering, disaster risk reduction, and land use, as well as those in industry and responsible for policy, will find this an invaluable resource.
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Options Trading : 7 Golden Beginners Strategies to Start Trading Options Like a PRO! Perfect Guide to Learn Basics & Tactics for Investing in Stocks, Futures, Binary & Bonds. Create Passive Income Fas
There are a lot of different investment opportunities that you can choose from. Some will entail more risk than others, but they can also entail higher profit potentials as well. But one option that many investors may not consider when they first get started in this market is options trading. This guidebook is going to take some time to explore options trading and how even a beginner can get started making money if they choose the right strategy. Some of the topics that we will discuss about options trading in this guidebook include: What is options trading?,Working with the bull put spread strategy,Working with the bear call spread strategy,The importance of the butterfly and condor strategies. ,Working with both the long straddle and the long strangle. ,The bear put spread strategy,Working with the bull call spread strategy,The ratio spreads and how they work as a strategy. ,The best ways to reduce your risks when you are working with options trading. , Options trading is a great choice when it comes to investing your money. You will be able to earn unlimited profits without actually having to own the security outright. And this type of investment can work no matter what kind of market conditions are present with a stock. When you are ready to get started with options trading, make sure to check out this guidebook to help you out!
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What is options trading?
Options trading is a type of investing strategy that involves buying and selling options contracts on the stock market. An options contract gives the holder the right, but not the obligation, to buy or sell a specific asset at a predetermined price within a set timeframe. Options trading allows investors to speculate on the direction of a stock's price movement without actually owning the stock itself. It can be a high-risk, high-reward strategy that requires a good understanding of the market and careful risk management.
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Can complexity be objectively measured?
Complexity can be objectively measured to some extent, especially in the context of information theory and algorithmic complexity. In information theory, complexity can be measured using metrics such as entropy and Kolmogorov complexity, which provide objective measures of the amount of information or computational resources required to describe a system. However, when it comes to measuring the complexity of real-world systems or phenomena, there is often a subjective element involved, as different observers may prioritize different aspects of complexity. Therefore, while certain aspects of complexity can be objectively measured, the overall assessment of complexity may still involve some degree of subjectivity.
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What is the complexity of Mergesort?
The time complexity of Mergesort is O(n log n) in the worst-case scenario, where n is the number of elements in the array. This complexity arises from the fact that Mergesort divides the array into halves recursively and then merges them back together in sorted order. The space complexity of Mergesort is O(n) due to the need for additional space to store the divided subarrays during the sorting process. Overall, Mergesort is an efficient sorting algorithm that performs well on large datasets.
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How can one get rid of complexity?
One can get rid of complexity by breaking down the problem or situation into smaller, more manageable parts. This can help to identify the root causes of the complexity and address them individually. Additionally, simplifying processes, communication, and decision-making can help reduce complexity. It is also important to prioritize and focus on the most important aspects, while letting go of unnecessary details. Finally, seeking input and collaboration from others can provide fresh perspectives and help to streamline complex situations.
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The Rehabilitation of Sexual Offenders : Complexity, Risk and Desistance
Sexual offenders – arguably the most hated and feared of all offenders – commit their crimes in our communities and are then hidden from public view as they serve long prison sentences.However, despite the public’s understandable anxiety, our criminal justice systems hold to the premise that almost all offenders have the right to hope for rehabilitation, even redemption.Therefore the majority of sexual offenders return to live in our communities, closely monitored by criminal justice agencies and subject to rigorous controls. This book provides an authoritative guide to working with sexual offenders, with a focus on managing those who are reintegrating into the community.It includes those with the most striking histories of trauma and psychological difficulty, and those who have previously failed in their attempts at resettlement.It covers helpful theoretical ideas, such as attachment theory and models of desistance, as well as the latest evidence base for good quality risk assessment. The book supports practitioners on the front line of this work by providing them with evidence-based guidance.It presents a multitude of case examples and practice tips that can support effective decision making and achieve safe outcomes, as well as help such offenders build worthwhile community lives.
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Think Complexity : Complexity Science and Computational Modeling
Complexity science uses computation to explore the physical and social sciences.In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations.In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.
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Simplified Complexity
Thanks to the growth of computational power and the development of new productiontechnologies, NURBS modeling has become the standard in many fields:Industrial Design, Architecture and, more recently, Engineering.Simplified Complexity is a method for learning NURBS modeling with Rhinoceros (R).Born as the synthesis of twenty years of professional experience and teaching,Simplified Complexity consists of a structured knowledge system allowing deepunderstanding of the software.With this method the user can take advantage of Rhinoceros (R) full modeling potential.The idea behind Simplified Complexity is that even if the software has a clear andintuitive interface, NURBS geometry remains quite complex.In order to become aprofessional user, it is necessary to start from basic geometry knowledge: this willallow to foresee and avoid complexity or, if this is not possible, at least reduce it andoptimize it.
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Ecological Complexity
Complexity has received substantial attention from scientists and philosophers alike.There are numerous, often conflicting, accounts of how complexity should be defined and how it should be measured.Much less attention has been paid to the epistemic implications of complexity, especially in Ecology.How does the complex nature of ecological systems affect ecologists' ability to study them?This Element argues that ecological systems are complex in a rather special way: they are causally heterogeneous.Not only are they made up of many interacting parts, but their behaviour is variable across space or time.Causal heterogeneity is responsible for many of the epistemic difficulties that ecologists face, especially when making generalisations and predictions.Luckily, ecologists have the tools to overcome these difficulties, though these tools have historically been considered suspect by philosophers of science.The author presents an updated philosophical account with an optimistic outlook of the methods and status of ecological research.
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What is the complexity of composing two functions?
Composing two functions has a complexity of O(1), as it involves simply applying one function to the output of the other. The time complexity does not depend on the size of the input, as the functions are applied sequentially. Therefore, the complexity of composing two functions is constant and does not increase with the size of the input.
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What are the Landau symbols for the time complexity?
The Landau symbols for time complexity are commonly used to describe the upper and lower bounds of an algorithm's running time. The most commonly used Landau symbols for time complexity are O (big O) for upper bound, Ω (big omega) for lower bound, and Θ (big theta) for both upper and lower bounds. These symbols are used to express the growth rate of an algorithm's running time in terms of the input size. For example, if an algorithm has a time complexity of O(n^2), it means that the running time of the algorithm grows no faster than n^2 as the input size increases.
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What are the Big O notations for time complexity?
The Big O notations for time complexity are used to describe the upper bound on the growth rate of an algorithm's running time as the input size increases. Some common Big O notations include O(1) for constant time complexity, O(log n) for logarithmic time complexity, O(n) for linear time complexity, O(n^2) for quadratic time complexity, and O(2^n) for exponential time complexity. These notations help in analyzing and comparing the efficiency of different algorithms.
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How do you determine the complexity of a function?
The complexity of a function can be determined by analyzing its time and space requirements. This can be done by examining the number of operations the function performs and the amount of memory it uses. Additionally, the complexity can be influenced by the size of the input data and the efficiency of the algorithm used in the function. By considering these factors, one can determine the complexity of a function, which is often expressed using Big O notation.
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