Liquidity Dynamics and Risk Modeling: Navigating Trading and Investment Portfolios Frontiers with Machine Learning Algorithms
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| Format: | Book |
| Language: | English |
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Cham
palgrave macmillan
[2024]
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| Edition: | 1st ed. 2024 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxxv, 643 Seiten Illustrationen |
| ISBN: | 9783031715020 |
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Contents 1 Beyond the Surface: In-Depth Perspectives on Liquidity and Risk Frontiers 1 Introduction 2 Understanding Liquidity Dimensions and Regulatory Responses 3 Understanding Market and Funding Liquidity Risk—A Comprehensive Overview Understanding Funding Liquidity Risk in Financial Markets Navigating Market-Liquidity Risk—Insights and Strategies 4 Challenges in Evaluating and Managing Liquidity The Complexity of Liquidity Risk Assessment and Its Historical Neglect 5 Proprietary Trading and the Interplay of Liquidity and Market Risk Frontiers Proprietary Trading—The Practice Within Financial Markets The Intersection of Market and Liquidity Risk Dynamics 6 Liquidity Issues, Liquidity Risk, and Market Risk in the Context of Artificial Intelligence (AI) and Machine Learning (ML) Liquidity Matters in the Age of AI and ML 1 1 5 8 8 11 15 . 15 17 18 19 23 25 xiii
xiv CONTENTS Market Risk in the Age ofAI and ML 7 Concluding Remarks Appendix 1: Liquidity Dilemmas—Challenges and Contemporary Risk Management Strategies in Investment and Investable Portfolios Introduction Liquidity in Finance—Foundations and Concepts Contemporary Liquidity Issues in Financial Markets and Institutions Liquidity Risk Management in Financial Markets and Institutions Mitigation of Liquidity Risk in Financial Markets and Institutions Liquidity Issues in Financial Engineering and Markets Liquidity Challenges in Financial Institutions and Investment Portfolios Liquidity Matters in Investable Portfolios Liquidity Risk Management in Investable Portfolios—Effective Strategies and Controls Market Risk Management—Insights for Financial Markets and Institutions Strategies for Effective Market Risk Assessment and Management in Investment Portfolios Strategies for Mitigating Potential Losses Through Effective Market Risk Management Control Mechanisms for Market Risk Conclusion References 31 35 39 39 39 41 43 47 48 50 54 56 58 59 61 62 64 71
CONTENTS 2 Unlocking the Microstructure of Liquidity Risk: Understanding Interactions with Other Financial Risks and Best Practices in Oversight and Governance 1 Introduction and Overview Distinctive Traits of Emerging Markets Charting Market Structures—Prudential Regulations and Trading Risk Management in Emerging Markets 4 Executing Financial Operations—Securities Trading and Risk Management Trading Risk Management Units—Essential Goals and Tasks Enforcing Internal Regulations and Controls—Implementation Strategies 5 Insights into Trading Risk Management and Proprietary Market Operations—A Comprehensive Overview Modeling Trading Risks—Incorporating the Impact of Adverse Events Defining VaR Boundary Limits—Maximizing Risk Parameters 6 Applying Parametric Variance-Covariance Algorithms—A Framework for Value-at-Risk Measurement What is Value-at-Risk (VaR)? What Purposes VaR Can Serve and Who Should Use It? Implementing VaR for Multi-Asset Portfolios—Leveraging Parametric Variance-Covariance Machine Learning Algorithms Incorporating Asset Illiquidity—A Foundational Model for Trading Risk Mitigating Incomplete and Omitted Statistics to Refine Risk Analysis Key Limitations of the Parametric Variance-Covariance VaR Methodology 2 3 XV 79 79 87 89 91 98 105 107 109 110 111 112 113 117 121 124 125
xvi CONTENTS Insight into Stress Testing and Scenario Analysis Methods 7 An Overview of RAROC (Risk-Adjusted-Return-On-Capital) Technique 8 Synthesis, Recommendations, and Concluding Remarks Appendix 1: Advancements in Liquidity and Market Risks Management: An Examination of VaR, ES, and L-VaR with Insights from Artificial Intelligence (AI) and Machine Learning (ML) Introduction Liquidity Risk Management Contemporary Issues Contemporary Issues and Literature Review Emerging Trends in Liquidity Risk Management Market Risk Management Contemporary Issues and Literature Review Emerging Trends in Market Risk Management Contemporary Issues with VaR and L-VaR Conclusion References 3 Crises to Opportunities: Derivatives Trading, Liquidity Management, and Risk Mitigation Strategies in Emerging Markets 1 Introduction and Overview 2 Key Characteristics of Emerging Markets and Criteria for Derivatives Products Trading 3 Constructing Robust Derivatives Products in Emerging Markets—Challenges, Constraints, and Regulatory Strategies Regulatory Challenges in Emerging Markets and Contemporary Risk Management Proposals to Overcome Regulatory Challenges for Reliable Derivatives Products Operations in Emerging Markets Strategies for Combating Financial Market Misconduct—Responses and Potential Proposals Regulatory Hurdles in Derivatives Products Operations—Evaluating Embedded Risks and the Significance of Data and Statistical Tools 127 128 132 141 141 141 142 143 145 148 148 151 154 156 161 169 169 172 177 177 179 183 185
CONTENTS Responses to Efficient Regulatory Challenges—Principles and Proposals Including Relevant Basel Capital Adequacy Accords Issues on Standard and Internal Models 4 Synthesis, Concluding Remarks, and Final Thoughts Appendix (1): A Comprehensive Overview of Basel Capital Adequacy Regulations for Financial Institutions Conclusion Appendix (2): Liquidity Risk in Basel III—A Comprehensive Overview Definition and Categories of Liquidity Risk Framework in Basel III Liquidity Risk Management and Supervision Stress Testing and Scenario Analysis Regulatory Reporting and Disclosure Challenges and Criticisms Conclusion Appendix (3): Effective Liquidity Risk Management in Derivatives Products What Is Liquidity Risk in Derivatives Products? Causes of Liquidity Risk in Derivatives Liquidity Risk in Different Types of Derivatives Specific Issues in Derivatives Liquidity Risk Managing Liquidity Risk in Derivatives Advanced Strategies for Managing Derivatives Liquidity Risk Regulatory Frameworks and Their Impact Future Trends and Challenges in Derivatives Liquidity Risk Conclusion References 4 Insights into Liquidity Dynamics: Optimizing Asset Allocation and Portfolio Risk Management with Machine Learning Algorithms I Introduction Fundamental Principles in Current Research and Comprehensive Literature Review Literature Review on Optimum Portfolio Selection Using Value-at-Risk (VaR) Framework Literature Review on Liquidity-Adjusted Value-at-Risk (L-VaR) Modeling in Financial Research XVÜ 192 202 211 215 215 215 217 217 217 217 218 218 219 220 221 222 223 224 225 227 228 252 257 237 260
262 264
xviii CONTENTS Core Principles and Objectives of the Present Research Initiative 266 2 How Liquidity Risk Affects Asset Allocation—Theoretical Foundations and Algorithms of Modified Liquidity Model 268 Application of Parametric Machine Learning Algorithms in Trading Risk Management: Focus on Liquidity-Adjusted Value-at-Risk (L-VaR) and Glosed-Form Solutions 268 Incorporating Asset Liquidity Risk into L-VaR Modeling Techniques 272 3 Optimization Algorithms for Addressing Theoretical and Practical Challenges of Liquidity Risk and Dynamic Asset Allocation 279 4 Conclusion 285 Appendix (1): Understanding Economic Capital: A Pillar of Effective Risk Management 287 Introduction 287 What Is Economic Capital? 287 Role of Economic Capital in Risk Management 287 Determining Economic Capital 288 Challenges and Considerations 289 Conclusion 290 Appendix (2): Unraveling Investment Portfolios: Constructing Paths to Investable Portfolios 290 Introduction 290 What Is an Investment Portfolio? 291 Understanding Investable Portfolios 292 Key Features of Investable Portfolios 293 Strategies for Building Investable Portfolios 294 Conclusion 296 References 299 5 Navigating Liquidity Waves: Practical Applications of Liquidity Risk Management and Investable Portfolio Optimization in Financial Markets 305 1 2 Introduction Risk Management in Gulf Cooperation Council (GCC) Emerging Financial Markets Within Basel Capital Adequacy Accords Framework 305 307
CONTENTS Foundational Principles and Objectives of the Current Research Study 3 Unlocking Liquidity Risk—Innovative Strategies and Applications in Financial Markets Unveiling Market Dynamics—A Statistical Exploration of Conditional Volatility and Assessing Asymmetric Returns Empirical Exploration of Dependence Measures in L- VaR Modeling Algorithms—A Comparative Analysis Enhancing Risk Assessment—Computational Insights . into Economic Capital Using L-VaR Modeling Algorithms 4 Managing Investable Portfolios—Applications to Financial Markets Using Modified Machine Learning Liquidity Models Balancing Risk and Reward—Empirical Constrained Optimization of Economic Capital Portfolios in Long and Short-Sales Equity Trading Assets Balancing Risk and Reward—Empirical Constrained Optimization of Economic Capital Portfolios in Long-Only Equity Trading Assets 5 Conclusion Appendix: Navigating the Complexities of Proprietary Trading Risk Management and Liquidity Control in Financial Trading Desks Introduction Key Components of Proprietary Trading Risk Management Understanding Proprietary Trading Risk Management Market Risk Management Credit Risk Management Operational Risk Management Liquidity Risk Management Conclusion References xix 310 313 316 318 321 326 328 334 338 342 342 343 345 345 346 347 348 351 355
XX 6 CONTENTS Leveraging Liquidity Models in Commodity Markets: Customizing Risk-Appetite Trading Limits for Robust Investment Portfolios 1 Introduction 2 Theoretical Underpinnings—Exploring L-VaR Models for Advanced Portfolio Management Analytics Precision in Risk—Closed-Form Parametric VaR Modeling Algorithms for Effective Portfolio Risk Management Enhancing Portfolio Risk Modeling—Integrating Liquidity Risk Parameters into the L-VaR Algorithm 3 Unlocking Potential—Advanced Optimization Algorithms and Investment Analytics for Large-Scale Commodity Portfolios using Reinforcement Machine Learning Techniques Refining Efficiency—Optimization Parameters for Investable and Coherent Commodity Portfolios Reinforcement Machine Learning, L-VaR Optimization Algorithms, and Their Operational Dynamics 4 Strategic Management of Investable and Coherent Commodity Portfolios—An Empirical Analysis using L-VaR Modeling Techniques Investable and Coherent Portfolios—Enhancing Efficiency through Optimization Inputs and Parameters—A Case Study of Commodity Long and Short-Sales Positions Investable and Coherent Portfolios—Enhancing Efficiency through Optimization Inputs and Parameters—A Case Study of Commodity Long-Only Positions 5 Calibrating Risk Frontiers: Optimizing Maximum L-VaR and Risk-Appetite Limits in Commodity Trading Management—Strategic Approaches and Implementation Insights 6 Concluding Remarks and Charting Future Directions Appendix 1: Understanding the Dynamics of Commodities—Risk Management, Challenges, and Innovations in Artificial Intelligence (AI) and Machine Learning (ML)
359 359 367 368 370 374 375 377 383 392 403 414 422 438
CONTENTS Introduction Understanding Commodities Risk Challenges in Commodities Risk Management Contemporary Issues in Commodities Risk Management Conclusion References 7 Liquidity Spectrum: Unraveling Theoretical Frameworks and Leveraging Modeling Algorithms for In-Depth Insights into Overall and Relative Liquidity Risk Dynamics 1 2 Introduction and Motivation Literature Review and Chapter Framework Literature Review on Liquidity-Adjusted Value-at-Risk (L-VaR) Modeling Foundations and Specific Objectives of the Current Research Study 3 Incorporating Asset Liquidity Risk into Parametric Trading Risk Modeling Frameworks Statistical Foundations for a Coherent L-VaR Approach Modeling Asset Liquidity Trading Risk Within the L-VaR Context 4 A Comparative Examination and Testing of L-VaR Models’ Performance—Empirical Analysis of Emerging Markets Conditional Volatility Estimation using GARCH-M (1,1) Technique Statistical Analysis and Uon-normality Testing XXÎ 438 438 439 440 441 447 453 453 457 457 464 468 468 473 485 486 489
xxii CONTENTS Comparative Analysis of Alternative L-VaR Modeling Strategies for Liquidity Trading Risk Management 5 Summary and Concluding Remarks Appendix 1: Understanding the AJ (1) Asset Liquidity Model—Mathematical Derivation and Lmplementation of Machine Learning Modeling Algorithms Understanding the A] (2) Asset Liquidity Model—Mathematical Derivation and Lmplementation of Machine Learning Modeling Algorithms Appendix 2: Illiquid Markets Risk Management: Innovations in VaR Prediction through Advanced Modeling, Machine Learning (ML), and Artificial Intelligence (AI) Solutions Introduction Understanding Value-at-Risk (VaR) Challenges in Predicting VaR under illiquid Market Conditions Alternative Modeling Strategies for VaR Prediction Evaluation of Modeling Strategies Contemporary Issues in AI and ML for Financial Risk Management Conclusion References 8 491 504 510 515 522 522 522 523 524 525 526 $27 535 Beyond Boundaries: Navigating Liquidity Frontiers with Advanced L-VaR Optimization Algorithms and Strategic Integration of Bid-Ask Modeling Spreads 539 1 Introduction and Overview 539 2 Literature Review and Research Motivation 547 3 Modeling Uncertainty Using Robust Machine Learning Processes 560 Machine Learning for Uncertainty Modeling with Closed-Form Parametric VaR Algorithms 561 Leveraging Machine Learning for Modeling Adverse Price Impact with the Al Janabi Model 569 Machine Learning Process for Transactions Cost Measurement 581
CONTENTS Leveraging Machine Learning for Computing Overall Risk Exposure 4 Practical Applications for Contemporary Portfolio Optimization Selection, and Risk Management 5 Concluding Remarks and Future Directions Appendix (1): Exploring Liquidity Horizons—Bridging Frontiers with L-VaR Algorithms, Bid-Ask Modeling, Machine Learning (ML), and Artificial Intelligence (AI) Integration Introduction Introduction to Liquidity Management Understanding Liquidity-Adjusted Value-at-Risk (L-VaR) Optimization Algorithms Strategic Integration of Bid-Ask Modeling Spreads Contemporary Issues of Artificial Intelligence (AI) and Machine Learning (ML) Key Issues and Challenges with the Adoption of AI and ML Future Directions andConsiderations Conclusion References Index xxiii 590 600 609 618 618 618 619 620 621 621 622 624 634 639 |
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| edition | 1st ed. 2024 |
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| spelling | Al Janabi, Mazin A. M. Verfasser (DE-588)140797629 aut Liquidity Dynamics and Risk Modeling Navigating Trading and Investment Portfolios Frontiers with Machine Learning Algorithms Mazin A. M. Al Janabi Cham palgrave macmillan [2024] xxxv, 643 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Risk Management Corporate Finance Financial Engineering Capital Markets Financial risk management Business enterprises / Finance Financial engineering Capital market Erscheint auch als Online-Ausgabe 978-3-031-71503-7 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035468519&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Al Janabi, Mazin A. M. Liquidity Dynamics and Risk Modeling Navigating Trading and Investment Portfolios Frontiers with Machine Learning Algorithms Risk Management Corporate Finance Financial Engineering Capital Markets Financial risk management Business enterprises / Finance Financial engineering Capital market |
| title | Liquidity Dynamics and Risk Modeling Navigating Trading and Investment Portfolios Frontiers with Machine Learning Algorithms |
| title_auth | Liquidity Dynamics and Risk Modeling Navigating Trading and Investment Portfolios Frontiers with Machine Learning Algorithms |
| title_exact_search | Liquidity Dynamics and Risk Modeling Navigating Trading and Investment Portfolios Frontiers with Machine Learning Algorithms |
| title_full | Liquidity Dynamics and Risk Modeling Navigating Trading and Investment Portfolios Frontiers with Machine Learning Algorithms Mazin A. M. Al Janabi |
| title_fullStr | Liquidity Dynamics and Risk Modeling Navigating Trading and Investment Portfolios Frontiers with Machine Learning Algorithms Mazin A. M. Al Janabi |
| title_full_unstemmed | Liquidity Dynamics and Risk Modeling Navigating Trading and Investment Portfolios Frontiers with Machine Learning Algorithms Mazin A. M. Al Janabi |
| title_short | Liquidity Dynamics and Risk Modeling |
| title_sort | liquidity dynamics and risk modeling navigating trading and investment portfolios frontiers with machine learning algorithms |
| title_sub | Navigating Trading and Investment Portfolios Frontiers with Machine Learning Algorithms |
| topic | Risk Management Corporate Finance Financial Engineering Capital Markets Financial risk management Business enterprises / Finance Financial engineering Capital market |
| topic_facet | Risk Management Corporate Finance Financial Engineering Capital Markets Financial risk management Business enterprises / Finance Financial engineering Capital market |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035468519&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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