Johnathan Mun

Course slides for Quantitative Financial Analysis

M TODOS QUANTITATIVOS DE PESQUISA EM POUCAS PALAVRASESTAT STICAS DESCRITIVAS E MOMENTOS DE UMA DISTRIBUI OTEORIA DAS PROBABILIDADES E DISTRIBUI O DE PROBABILIDADETESTES DE HIP TESESM TODOS ESTAT STICOS PARA UMA VARI VELM TODOS ESTAT STICOS PARA DUAS OU MAIS VARI VEISSIMULA O, MODELAGEM PREDITIVA E OTIMIZA OMOMENTOS DAS DISTRIBUI ESMEDINDO O CENTRO, DISPERS O, ASSIMETRIA, EVENTOS EXTREMOS EM UMA DISTRIBUI OTESTE DE HIP TESESETAPAS PARA ESTABELECER UM TESTE DE HIP TESESTEOREMA DO LIMITE CENTRALERROS TIPO I, TIPO II, TIPO III E TIPO IV E VIESES NA AMOSTRAGEM DE DADOS ANAL TICOS MAIS COMUNSTESTE-T DE DUAS AMOSTRAS COM IGUAL VARI NCIATESTE-T DE DUAS AMOSTRAS COM VARI NCIA IRREGULARTESTE-T DE DUAS AMOSTRAS E M DIA DEPENDENTESTESTE-F DE VARI NCIAS DE AMOSTRAS INDEPENDENTESTESTE-Z DE PROPOR ESZ-TESTE DE PROPOR ES E M DIASANOVA SIMPLES COM M LTIPLOS TRATAMENTOSANOVA COM TESTE DE BLOCO RANDOMIZADOANOVA DOIS FATORES, ANCOVA, MANOVA E MANOVA DOIS FATORESTESTES QUI-QUADRADOCORRELA ES LINEARES E N O LINEARESTESTES DE NORMALIDADE E AJUSTE DE DISTRIBUI OTESTES N O-PARAM TRICOSCONFIABILIDADE, CONFIABILIDADE ENTRE E INTRA AVALIADORES, CONSIST NCIA, CREDIBILIDADE, DIVERSIDADE, VALIDADE INTERNA, VALIDADE EXTERNA E PREVISIBILIDADEREGRESS O MULTIVARIADA LINEAR E N O LINEARREGRESS O BIVARIADATESTES PARA MULTICOLINEARIDADE E HETEROCEDASTICIDADEM TODOS AVAN ADOS DE REGRESS O, M TODOS RELACIONADOS DE REGRESS O E SUAS VARIA ESAL M DA REGRESS O M LTIPLA: MODELAGEM DE EQUA ES ESTRUTURAIS (MEE) COM M NIMOS QUADRADOS PARCIAIS (PLS) NA ESTIMATIVA DO CAMINHOAL M DA REGRESS O M LTIPLA: M TODOS DE ENDOGENEIDADE E EQUA ES SIMULT NEAS E M NIMOS QUADRADOS DE DOIS EST GIOSAL M DA REGRESS O M LTIPLA: CAUSALIDADE GRANGER E M TODOS ENGLE-GRANGERAL M DA REGRESS O M LTIPLA: REGRESS O DE POISSON, REGRESS O DEMING, REGRESS O LOG STICA ORDINAL, REGRESS O DO RIDGE E REGRESS O PONDERADA INTELIG NCIA ARTIFICIAL & APRENDIZADO DE M QUINAAPRENDIZADO DE M QUINA DE IA: AJUSTE LINEAR POR AGREGA O DE BOOTSTRAP (SUPERVISIONADO)APRENDIZADO DE M QUINA DE IA: AJUSTE N O LINEAR EMPACOTAMENTO BOOTSTRAP (SUPERVISIONADOAPRENDIZADO DE M QUINA DE IA: CART - RVORES DE CLASSIFICA O DE E REGRESS O (SUPERVISIONADO)APRENIZADO DE M QUINA DE IA: CLASSIFICA O COM SEGMENTA O MIX GAUSSIANO & K-MEANS (N O SUPERVISIONADA)APRENDIZADO DE M QUINA DE IA: CLASSIFICA O COM K-NEAREST NEIGHBORS (SUPERVISIONADO)APRENDIZADO DE M QUINA DE IA: CLASSIFICA O COM RVORES FILOGEN TICAS E AGRUPAMENTO HIER RQUICO (N O SUPERVISIONADO)APRENDIZADO DE M QUINA DE IA: CLASSIFICA O COM M QUINAS VETORIAIS DE SUPORTE (SUPERVISIONADAS)APRENDIZADO DE M QUINA DE IA: MODELO DE AJUSTE PERSONALIZADO(SUPERVISIONADO)APRENDIZADO DE M QUINA DE IA: AN LISE COMPONENTES PRINCIPAIS - REDU O DE DIMENS ES (N O SUPERVISIONADOS)APRENDIZADO DE M QUINA DE IA: AN LISE DO FATOR DEREDU O DE DIMENS ES (N O SUPERVISIONADO)APRENDIZADO DE M QUINA DE IA: CONJUNTO COMMON FIT (NONLINEAR) (SUPERVISIONADO)APRENDIZADO DE M QUINA DE IA: AJUSTE CONJUNTO COMPLEXO (N O LINEAR) (SUPERVISIONADO)APRENDIZADO DE M QUINA DE IA: S RIE-TEMPO DO CONJUNTO (SUPERVISIONADO)APRENDIZADO DE M QUINA DE IA: MODELO DE AJUSTE LINEAR (SUPERVISIONADO)APRENDIZADO DE M QUINA DE IA: AN LISE DISCRIMINANTE MULTIVARIADA (LINEAR) (SUPERVISIONADA)APRENDIZADO DE M QUINA DE IA: AN LISE DISCRIMINANTE MULTIVARIADA (QUADR TICA) (SUPERVISIONADO)APRENDIZADO DE M QUINA DE IA: REDE NEURAL (SUPERVISIONADA)APRENDIZADO DE M QUINA DE IA: CLASSIFICA O BIN RIA LOG STICA (SUPERVISIONADA)APRENDIZADO DE M QUINA DE IA: CLASSIFICA O BIN RIA NORMIT PROBIT (SUPERVISIONADA)APRENDIZADO DE M QUINA DE IA

A S rie de Livros CQRM Aplicada mostra como as an lises avan adas abordadas no programa de Certifica o em Gerenciamento de Riscos Quantitativos (CQRM) podem ser aplicadas a problemas de neg cios da vida real. No Volume VI, mostramos como o m dulo ROV-PEAT/ERM (Gest o de Risco Empresarial) utilizado para avaliar e gerir e divulgar conjunto de riscos classificados como Riscos Empresariais. Este livro. Este livro n o s direcionado para aqueles que completaram o programa de certifica o CQRM, mas tamb m pode ser adotado em cadeiras de gest o de projetos em cursos de engenharia industrial (gradua o e p s-gradua o) para fixar as t cnicas e estrat gias de gest o de riscos em projetos pode ser usado por qualquer pessoa familiarizada com m todos b sicos de pesquisa quantitativa - h sempre alguma coisa til para todos. Tamb m aplic vel como um livro did tico em cursos de n vel MBA/MS, no segundo ano ou introdut rio. Nos exemplos do livro, pressupomos algum conhecimento pr vio sobre o assunto. Informa es adicionais sobre o programa CQRM podem ser obtidas em: www.iiper.org o www.realoptionsvaluation.com

A S rie de Livros CQRM aplicada mostra como as an lises avan adas abordadas no programa de Certifica o em Gerenciamento de Riscos Quantitativos (CQRM) podem ser aplicadas a problemas de neg cios da vida real. No Volume III, mostramos como o m dulo ROV-PEAT/FCD-E pode ser utilizado para realizar an lise econ mico-financeira de projetos (Avalia o Quantitativa Estoc stica de Projetos), seja em cadeiras de p s-gradua o ou trabalhos profissionais de consultoria, Valuation e M&A . As aplica es pragm ticas s o enfatizadas para desmistificar os muitos elementos inerentes an lise quantitativa em ambiente de incerteza, alta volatilidade e uso de Op es Reais Estrat gicas e Otimiza o de Portf lio de Projetos ou Ativos. Uma caixa preta de m todos de apoio decis o permanecer uma caixa preta se ningu m conseguir entender os conceitos, apesar de seu poder e aplicabilidade. Somente quando os m todos da caixa preta se tornam transparentes para os pesquisadores e analistas de neg cio, ser o capazes de aplicar as t cnicas aqui explicadas e convencer os seus colegas e clientes sobre os seus resultados, valor agregado e aplicabilidade, de forma a obter ampla aceita o. Essa transpar ncia alcan ada atrav s de aplica es passo-a-passo da modelagem proposta no PEAT/FCD-E, bem como apresenta o de m ltiplos casos e discuss o de aplica es reais. Este livro n o s direcionado para aqueles que completaram o programa de certifica o CQRM, mas tamb m pode ser usado por qualquer pessoa familiarizada com m todos b sicos de pesquisa quantitativa - h sempre alguma coisa til para todos. Tamb m aplic vel para ser adotado como um livro did tico em cursos de n vel MBA/MS, no segundo ano ou introdut rio. Nos exemplos do livro, pressupomos algum conhecimento pr vio sobre o assunto. Informa es adicionais sobre o programa CQRM podem ser obtidas em: www.iiper.org www.realoptionsvaluation.com

La Serie de Libros sobre el CQRM Aplicado expone c mo la anal tica avanzada que figura en el programa de Certificaci n en Gesti n Cuantitativa de Riesgos (CQRM), se puede aplicar a los problemas de negocios en la vida real. Se hace un nfasis en las aplicaciones pragm ticas con el fin de desmitificar los elementos inherentes al an lisis de riesgos. Una caja negra continuar siendo una caja negra si nadie puede entender los conceptos a pesar de su poder y su aplicabilidad. S lo hasta cuando los m todos de la caja negra se vuelven transparentes, para que los investigadores puedan entender, aplicar y convencer a otros de sus resultados, su valor agregado y la aplicabilidad, es que los enfoques recibir n una amplia atenci n. Esta transparencia se logra a trav s de las aplicaciones paso-a-paso de la modelaci n cuantitativa as como de la presentaci n de m ltiples casos y de la discusi n de las aplica-ciones en la vida real.El presente libro va dirigido a aquellas personas que han com-pletado el programa de certificaci n CQRM; pero tambi n lo pueden usar quienes est n familiarizados con los m todos b sicos cuantitativos de investigaci n- hay algo para todos. Es un texto igualmente aplicable a nivel de segundo a o de un MBA/MS o a nivel introductorio de un PhD. Los ejemplos que aparecen en el libro requieren de un conocimiento previo sobre el tema.Para obtener informaci n adicional sobre el programa CQRM, dir jase a los siguientes sitios Web: www.iiper.org www.realoptionsvaluation.com

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 V, we show how Credit, Market, Operational, and Liquidity (CMOL) Risks can be modeled using the CMOL software, Risk Simulator, and Modeling Toolkit. Pragmatic applications are emphasized in order to demystify the many elements inherent in risk analysis. A 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 through 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 something 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

La Serie de Libros sobre el CQRM Aplicado expone c mo la anal tica avanzada que figura en el programa de Certificaci n en Gesti n Cuantitativa de Riesgos (CQRM), se puede aplicar a los problemas de negocios en la vida real. En el Volumen IV, demostramos c mo se pueden aplicar estas anal ticas en el contexto de la Gesti n de Riesgos Empresariales, utilizando los registros cualitativos de riesgos y ampliando los resultados por medio de los m todos anal ticos cuantitativos.Se hace un nfasis en las aplicaciones pragm ticas con el fin de desmitificar los elementos inherentes al an lisis de riesgos. Una caja negra continuar siendo una caja negra si nadie puede entender los conceptos a pesar de su poder y su aplicabilidad. S lo hasta cuando los m todos de la caja negra se vuelven transparentes, para que los investigadores puedan entender, aplicar y convencer a otros de sus resultados, su valor agregado y la aplicabilidad, es que los enfoques recibir n una amplia atenci n. Esta transparencia se logra a trav s de las aplicaciones paso-a-paso de la modelaci n cuantitativa as como de la presentaci n de m ltiples casos y de la discusi n de las aplicaciones en la vida real.El presente libro va dirigido a aquellas personas que han completado el programa de certificaci n CQRM; pero tambi n lo pueden usar quienes est n familiarizados con los m todos b sicos cuantitativos de investigaci n- hay algo para todos. Es un texto igualmente aplicable a nivel de segundo a o de un MBA/MS o a nivel introductorio de un PhD. Los ejemplos que aparecen en el libro requieren de un conocimiento previo sobre el tema.Para obtener informaci n adicional sobre el programa CQRM, dir jase a los siguientes sitios Web: www.iiper.org www.realoptionsvaluation.com

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

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 VI, we show how to model and risk simulate complex projects to obtain schedule and cost risks.Pragmatic applications are emphasized in order to demystify the many elements inherent in risk analysis. A 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 through 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 something 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 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 IV, we show how these analytics can be applied in the context of Enterprise Risk Management, using both qualitative risk registers and extending the results using quantitative analytical methods.Pragmatic applications are emphasized in order to demystify the many elements inherent in risk analysis. A 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 something 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 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 III, we show how ROV's PEAT software can be used to evaluate a project's economics, simulate its uncertainties, run sensitivity analysis, and use their analytical properties for making strategic decisions. Pragmatic applications are emphasized in order to demystify the many elements inherent in risk analysis. A 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 something 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 Applied CQRM Book Series showcases how the advanced analytics covered in the Certified in Quantitative Risk Management (CQRM) certification program can be applied in real-life business problems. In Volume II, we show how Risk Simulator can be used to generate various probability distributions as well as how to interpret the results and use their analytical properties for making strategic decisions.Pragmatic applications are emphasized in order to demystify the many elements inherent in probability analysis. A 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 something 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

FOURTH EDITION (2021). COURSE SLIDES RESEARCH BASICSResearch Philosophy, Ontology, EpistemologyTheory, Constructs, Propositions, Logic, Attributes of a Good Theory, Theory BuildingQualitative Research: Case Study, Phenomenology, Field Research, Ethnographic Research, Grounded TheoryProbabilistic & Nonprobabilistic Statistical SamplingReliability & Threats to ValidityTrue & Quasi Experimental DesignsResearch Layout DESCRIPTIVE STATISTICS & MOMENTSFirst (Central Tendency), Second (Spread), Third (Skew), & Fourth (Kurtosis) BASIC PROBABILITYBayes' Theorem, Trees, Combinations & Permutations DISCRETE PROBABILITY DISTRIBUTIONSPDF, CDF, ICDF, Binomial, Hypergeometric, Poisson, Bernoulli, Discrete Uniform, Geometric, Negative Binomial, Pascal CONTINUOUS PROBABILITY DISTRIBUTIONSArcsine, Beta, Cauchy Lorentzian, Breit Wigner, Chi-Square, Cosine, Double Log, Erlang, Exponential, Extreme Value Gumbel, F Fisher Snedecor, Gamma Erlang, Laplace, Logistic, Lognormal, Normal, Parabolic, Pareto, Pearson V, Pearson VI, PERT, Power, Student's T, Triangular, Uniform, Weibull Rayleigh HYPOTHESIS TESTINGClassical, Standard, P-Value, & Confidence IntervalCentral Limit TheoremType I IV Errors, Data Sampling BiasesData Types & Collection Design STATISTICAL METHODST-Tests: Equal Variance, Unequal Variance, Dependent Means, F-Test, Z-TestANOVA Multiple Treatments, ANOVA Randomized BlockTwo-Way ANOVA, ANCOVA, MANOVALinear & Nonlinear CorrelationsNormality Tests & Distributional Fitting: Kolmogorov Smirnov, Chi Square, Akaike Information Criterion, Anderson Darling, Kuiper's Statistic, Schwarz Bayes Criterion, Box Cox TransformationNonparametric Tests: Runs, Wilcoxon Signed-Rank, Mann Whitney, Lilliefors, Q-Q, D'Agostino Pearson, Shapiro Wilk Royston Tests, Kruskal Wallis, Mood's Multivariate Medians, Cochran's Q, Friedman'sInter-Rater & Intra-Rater Reliability, Consistency, Diversity, Internal & External Validity, PredictabilityCohen's Kappa, Cronbach's Alpha, Guttman's Lambda, Inter-Class Correlation (ICC), Kendall's W Concordance, Data Diversity, Shannon Brillouin Simpson Diversity, Homogeneity, Grubbs Outlier, Mahalanobis, Linear & Quadratic Discriminant Analysis, Hannan Quinn, Diebold Mariano, Pesaran Timmermann, Precision, Error ControlLinear & Nonlinear Multivariate RegressionMulticollinearity & HeteroskedasticityStructural Equation Modeling (SEM), Partial Least Squares (PLS), Path EstimationEndogeneity, Simultaneous Equations Methods, Two-Stage Least SquaresGranger Causality, Engle Granger MethodsPoisson Regression, Deming Regression, Ordinal Logistic Regression, Ridge Regression, Weighted Regression FORECASTING AND PREDICTIVE MODELINGForecasting TechniquesTime-Series AnalysisStepwise RegressionStochastic ForecastingNonlinear ExtrapolationBox Jenkins ARIMAJ-Curve, S-CurveGARCH VolatilityMarkov ChainGLM & MLE: Logit, Probit, TobitCubic Spline Interpolation and ExtrapolationNeural Network, Combinatorial Fuzzy LogicTrendlines, RMSE, MSE, MAD, MAPE, Theil's UOutliers, Nonlinearity, Multicollinearity, Heteroskedasticity, Autocorrelation, Structural BreaksFunctional FormsDiagnostic & Statistical Analysis ToolForecast Intervals, Ordinary Least Squares, Detecting and Fixing Autocorrelation MONTE CARLO SIMULATIONConfidence IntervalsCorrelations & PrecisionTornado & SensitivityFitting Multiple VariablesPercentile Distributional Fitting ToolBootstrap SimulationData Extraction, Saving SimulationDistributional AnalysisScenario AnalysisSegmentation ClusteringStructural BreakDetrending DeseasonalizingPrincipal Component & Factor Analysis OPTIMIZATIONOptimization AlgorithmsContinuous & Discrete OptimizationEfficient Frontier & Sto

FIFTH EDITION (2022)INTRODUCTIONResearch Philosophy, Ontology, EpistemologyTheory, Constructs, Propositions, Logic, Attributes of a Good Theory, Theory BuildingQualitative Research: Case Study, Phenomenology, Field Research, Ethnographic Research, Grounded TheoryProbabilistic & Nonprobabilistic SamplingReliability & Threats to ValidityTrue/Quasi Experimental Design THE BASICSCentral Tendency, Spread, Skew, KurtosisProbability, Bayes' Theorem, Trees, Combination, Permutation PDF, CDF, ICDF, Binomial, Hypergeometric, Poisson, Bernoulli, Discrete Uniform, Geometric, Negative Binomial, Pascal, Arcsine, Beta, Cauchy Lorentzian, Breit Wigner, Chi-Square, Cosine, Double Log, Erlang, Exponential, Extreme Value Gumbel, F Fisher Snedecor, Gamma Erlang, Laplace, Logistic, Lognormal, Normal, Parabolic, Pareto, Pearson V, Pearson VI, PERT, Power, Student's T, Triangular, Uniform, Weibull/Rayleigh Classical, 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 FORECASTING AND PREDICTIVE MODELINGForecasting TechniquesTime-Series AnalysisStepwise RegressionStochastic ForecastingNonlinear ExtrapolationBox Jenkins ARIMAJ-Curve, S-CurveGARCHMarkov ChainGLM/MLE: Logit, Probit, TobitCubic Spline, Neural Network, Combinatorial Fuzzy LogicTrendlines, RMSE, MSE, MAD, MAPE, Theil's UOutliers, Nonlinearity, Multicollinearity, Heteroskedasticity, Autocorrelation, Structural BreaksFunctional FormsForecast Intervals, OLS, Detect/Fix Autocorrelation MONTE CARLO SIMULATIONConfidence Intervals, Correlations, Precision, Tornado, Sensitivity, Fitting, Percentile Fit, Bootstrapping, Distributional Analysis, Scenarios, Structural Break, Detrending, Deseasonalizing OPTIMIZATIONAlgorithms: Continuous & Discrete OptimizationEfficient Frontier & Stochastic Op

Risk analysis, in all its various forms, has become a science that is essential to all types of businesses that must cope with business uncertainties and unanticipated risk. This third edition provides up-to-date coverage of risk analysis as it is applied to a variety of business risks and situations in a way that is useful to a wide audience of business professionals.

ROV Modeling Toolkit software models and functions are described in this book. Applications include Monte Carlo risk simulation, stochastic forecasting, advanced analytics, exotic options, and many others. Risk Simulator and Modeling Toolkit software applications are required to use these models.

Modelaci n de Riesgos (Tercera Edici n, Volumen 1 de 2). Aplicaci n de la Simulaci n de Monte Carlo, An lisis de Opciones Reales, Pron stico Estoc stico, Optimizaci n de Portafolio, An lisis de Datos, Inteligencia de Negocios, y Modelaci n de Decisiones.