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.

Read now

Unlock full access

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

Content preview from Machine Learning for Financial Risk Management with Python

Chapter 7. Liquidity Modeling

When the music stops, in terms of liquidity, things will be complicated. But as long as the music is playing, you’ve got to get up and dance. We’re still dancing.

Chuck Prince (2007)

Liquidity is another important source of financial risk. However, it has been long neglected, and the finance industry has paid a huge price for modeling risk without considering liquidity.

The causes of liquidity risk are departures from the complete markets and symmetric information paradigm, which can lead to moral hazard and adverse selection. To the extent that such conditions persist, liquidity risk is endemic in the financial system and can cause a vicious link between funding and market liquidity, prompting systemic liquidity risk (Nikolaou 2009).

Tapping into the time lag between a changing value of the variable and its impact on the real market turns out to be a success criterion in modeling. For instance, interest rates, to some extent, diverge from real market dynamics from time to time, and it takes some time to settle. Together with this, uncertainty is the solely source of risk in traditional asset pricing models; however, it is far from reality. To fill the gap between financial models and real-market dynamics, the liquidity dimension stands out. A model with liquidity can better adjust itself to developments in the financial markets in that liquidity affects both the required returns of assets and also the level of uncertainty. Thus, liquidity is quite ...