book

Machine Learning for Financial Risk Management with Python

by Abdullah Karasan

December 2021

Intermediate to advanced

331 pages

8h 28m

English

O'Reilly Media, Inc.

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Conventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgements
RiskReturnRisk ManagementMain Financial RisksBig Financial CollapseInformation Asymmetry in Financial Risk ManagementAdverse SelectionMoral HazardConclusionReferences
Time Series ComponentsTrendSeasonalityCyclicalityResidualTime Series ModelsWhite NoiseMoving Average ModelAutoregressive ModelAutoregressive Integrated Moving Average ModelConclusionReferences
Recurrent Neural NetworksLong-Short Term MemoryConclusionReferences
ARCH ModelGARCH ModelGJR-GARCHEGARCHSupport Vector Regression: GARCHNeural NetworksThe Bayesian ApproachMarkov Chain Monte CarloMetropolis–HastingsConclusionReferences
Value at Risk (VaR)Variance-Covariance MethodThe Historical Simulation MethodThe Monte Carlo Simulation VaRDenoisingExpected ShortfallLiquidity-Augmented Expected ShortfallEffective CostConclusionReferences
Estimating the Credit RiskRisk BucketingProbability of Default Estimation with Logistic RegressionProbability of Default Estimation with the Bayesian ModelProbability of Default Estimation with Support Vector MachinesProbability of Default Estimation with Random ForestProbability of Default Estimation with Neural NetworkProbability of Default Estimation with Deep LearningConclusionReferences
Liquidity MeasuresVolume-Based Liquidity MeasuresTransaction Cost–Based Liquidity MeasuresPrice Impact–Based Liquidity MeasuresMarket Impact-Based Liquidity MeasuresGaussian Mixture ModelGaussian Mixture Copula ModelConclusionReferences

Getting Familiar with Fraud DataSupervised Learning Modeling for Fraud ExaminationCost-Based Fraud ExaminationSaving ScoreCost-Sensitive ModelingBayesian Minimum RiskUnsupervised Learning Modeling for Fraud ExaminationSelf-Organizing MapAutoencodersConclusionReferences
Stock Price Crash MeasuresMinimum Covariance DeterminantApplication of Minimum Covariance DeterminantLogistic Panel ApplicationConclusionReferences
Synthetic Data GenerationEvaluation of the Synthetic DataGenerating Synthetic DataA Brief Introduction to the Hidden Markov ModelFama-French Three-Factor Model Versus HMMConclusionReferences

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About the Author

Abdullah Karasan was born in Berlin, Germany. After studying economics and business administration, he obtained his master’s degree in applied economics from the University of Michigan, Ann Arbor, and his PhD in financial mathematics from the Middle East Technical University, Ankara. He is a former Treasury employee of Turkey and currently works as a principal data scientist at Magnimind and as a lecturer at the University of Maryland, Baltimore. He has also published several papers in the field of financial data science.