Applied Analytics - Quantitative Research Methods: Applying Monte Carlo Risk Simulation, Strategic Real Options, Stochastic Forecasting, Portfolio Opt
Synopsis "Applied Analytics - Quantitative Research Methods: Applying Monte Carlo Risk Simulation, Strategic Real Options, Stochastic Forecasting, Portfolio Opt"
THIRD EDITION (2022) The Applied CQRM Book Series showcases how the advanced analytics covered in the Certified in Quantitative Risk Management (CQRM) certification program can be applied to real-life business problems. In Volume I, we show how Risk Simulator and ROV BizStats can be used to perform quantitative analysis in graduate and postgraduate research. Pragmatic applications are emphasized in order to demystify the many elements inherent in quantitative analysis. A statistical black box will remain a black box if no one can understand the concepts despite its power and applicability. It is only when the black box methods become transparent, so that researchers can understand, apply, and convince others of their results, value-add, and applicability, that the approaches will receive widespread attention. This transparency is achieved through step-by-step applications of quantitative modeling as well as presenting multiple cases and discussing real-life applications. This book is targeted at those individuals who have completed the CQRM certification program but can also be used by anyone familiar with basic quantitative research methods--there is some-thing for everyone. It is also applicable for use as a second-year MBA/MS-level or introductory PhD textbook. The examples in the book assume some prior knowledge of the subject matter. Additional information on the CQRM program can be obtained at: www.iiper.org www.realoptionsvaluation.com THE BASICSCentral Tendency, Spread, Skew, KurtosisProbability, Bayes' Theorem, Trees, Combination, PermutationClassical, Standard, P-Value, CICentral Limit TheoremType I-IV Errors, Sampling BiasesData Types & Collection Design ANALYTICAL METHODST-Tests: Equal/Unequal/Paired Variance, F-Test, Z-TestANOVA, Blocked, Two-Way, ANCOVA, MANOVALinear/Nonlinear CorrelationNormality & Distributional Fitting: Kolmogorov-Smirnov, Chi-Square, Akaike Information Criterion, Anderson-Darling, Kuiper's, Schwarz/Bayes, Box-CoxNonparametrics: Runs, Wilcoxon, Mann-Whitney, Lilliefors, Q-Q, D'Agostino-Pearson, Shapiro-Wilk-Royston, Kruskal-Wallis, Mood's, Cochran's Q, Friedman'sInter/Intra-Rater Reliability, Consistency, Diversity, Internal/External Validity, PredictabilityCohen's Kappa, Cronbach's Alpha, Guttman's Lambda, Inter-Class Correlation, Kendall's W, Shannon-Brillouin-Simpson Diversity, Homogeneity, Grubbs Outlier, Mahalanobis, Linear & Quadratic Discriminant, Hannan-Quinn, Diebold-Mariano, Pesaran-Timmermann, Precision, Error ControlLinear/Nonlinear Multivariate RegressionMulticollinearity, HeteroskedasticityStructural Equation Modeling (SEM), Partial Least Squares (PLS)Endogeneity, Simultaneous Equations Methods, Two-Stage Least SquaresGranger Causality, Engle-GrangerAdvanced Regressions: Poisson, Deming, Ordinal Logistic, Ridge, Weighted, Bootstrap ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (DATA SCIENCE)Bagging Linear BootstrapBagging Nonlinear BootstrapClassification and Regression Trees CARTCustom FitDimension Reduction Principal Component AnalysisDimension Reduction Factor AnalysisEnsemble Common FitEnsemble Complex FitEnsemble Time-SeriesGaussian Mix & K-Means SegmentationK-Nearest NeighborsLinear Fit ModelMultivariate Discriminant Analysis (Linear)Multivariate Discriminant Analysis (Quadratic)Neural Network (Cosine, Tangent, Hyperbolic)Logistic Binary ClassificationNormit-Probit Binary ClassificationPhylogenetic Trees & Hierarchical ClusteringRandom ForestSegmentation ClusteringSupport Vector Machines SVM
