rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Multi-Asset Risk Modeling
    sub title: Techniques for a Global Economy in an Electronic and Algorithmic Trading Era
  name:
    name part: Glantz, Morton.
    role:
      role term: creator
  name:
    name part: Kissell, Robert.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: vtu
    place:
      place term: Burlington
    publisher: Elsevier Science
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (545 pages)
  abstract: Multi-Asset Risk Modeling describes, in a single volume, the latest and most advanced risk modeling techniques for equities, debt, fixed income, futures and derivatives, commodities, and foreign exchange, as well as advanced algorithmic and electronic risk management. Beginning with the fundamentals of risk mathematics and quantitative risk analysis, the book moves on to discuss the laws in standard models that contributed to the 2008 financial crisis and talks about current and future banking regulation. Importantly, it also explores algorithmic trading, which currently receives sparse attent.
  table of contents: Front Cover; Multi-Asset Risk Modeling; Copyright Page; Contents; Preface; About The Authors; Acknowledgements; 1 Introduction to Multi-Asset Risk Modeling-Lessons from the Debt Crisis; Types of Risk; Credit Risk; Interest Rate and Market Risk; Liquidity Risk; Price Risk; Foreign Exchange Risk; Transaction Risk; Compliance Risk; Strategic Risk; Reputation Risk; Faulted Risk Models; Financial Models Breaking Down in the Equity Markets; Covariance Models; Correlation Modeling; Algorithm Trading Issues; Risk Models Breaking Down; Deterministic Optimization; Simplistic Investment Models.
  table of contents: Deterministic or Static ForecastingReferences; 2 A Primer on Risk Mathematics; Introduction; Regression Analysis; Linear Regression; Model Evaluation Metrics; Model Assumptions; Regression Analysis Statistics; T-Test; F-Test; R2 Goodness of Fit; Unbiased Estimators; Matrix Algebra Techniques; Estimate Parameters; Linear Regression: Graphic Example; Log-Linear Regression Model; Log-Transformation: Graphic Example; Non-Linear Regression Model; Probability Models; Model Formulation; Mean and Variance; Logit Model; Probit Model; Comparison of Logit and Probit Models; Probability Distributions.
  table of contents: Extreme Value FunctionsDescriptive Statistics; Probability Distribution Functions; Continuous Distribution Functions; Normal Distribution; Standard Normal Distribution; Student's T-Distribution; Student's T-Distribution: Interesting Notes; Log-Normal Distribution; Uniform Distribution; Exponential Distribution; Beta Distribution; Gamma Distribution; Chi Square Distribution; Logistic Distribution; Cauchy Distribution; Triangular Distribution; Extreme Value Functions; Gumbel Distribution; Frechet Distribution; Weibull Distribution; Discrete Distributions; Binomial Distribution.
  table of contents: Poisson DistributionGeometric Distribution; Hypergeometric Distribution; Endnotes; References; 3 A Primer on Quantitative Risk Analysis; A Brief History of Risk: What exactly is Risk?; The Basics of Risk; The Nature of Risk and Return; Uncertainty Versus Risk; Risk Simulation Applications; Running a Monte Carlo Simulation; Start a New Simulation Profile; Define Input Assumptions; Define Output Forecasts; Run Simulation; Interpret the Forecast Results; Using Forecast Charts and Confidence Intervals; TIPS; Correlations and Precision Control; The Basics of Correlations.
  table of contents: Applying Correlations in Risk SimulatorThe Effects of Correlations in Monte Carlo Simulation; Precision and Error Control; Exercise 1: Basic Simulation Model; Model Background; Running a Monte Carlo Risk Simulation; Using Forecast Charts; Interpreting the Risk Statistics; Optional Exercises; Preferences; Options; Controls; Setting Seed Values; Running Super Speed Simulation; Setting Run Preferences (Simulation Properties); Extracting Simulation Data; Optional Exercises; Creating a Simulation Report and Forecast Statistics Table; Creating Forecast Statistics Using the RS Functions.
  table of contents: Saving a Simulation Run's Forecast Charts.
  subject:
    topic: Investments
  subject:
    topic: Portfolio management
  subject:
    topic: Risk management
    topic: Mathematical models
  subject:
    topic: Investments
  subject:
    topic: Investissements
  subject:
    topic: Gestion de portefeuille
  subject:
    topic: Gestion du risque
    topic: Modèles mathématiques
  subject:
    topic: BUSINESS & ECONOMICS
    topic: Finance
  subject:
    topic: Investments
  subject:
    topic: Portfolio management
  subject:
    topic: Risk management
    topic: Mathematical models
  classification: HG4651
  classification: 332.63 332.63/23 332.6323
  related item:
    title info:
      title: Multi-Asset Risk Modeling : Techniques for a Global Economy in an Electronic and Algorithmic Trading Era
    name:
      name part: Glantz, Morton.
    origin info:
      publisher: Burlington : Elsevier Science, ©2013
  identifier: 9780124016941
  identifier: 0124016944
  identifier: 1306189934
  identifier: 9781306189934
  identifier: 0124016901
  identifier: 9780124016903
  identifier: 550244 MIL
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: AU@
    record creation date: 140330
    record change date: 20260326200923.6
    record identifier: in00005754525
    language of cataloging:
      language term: eng