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Liquidity dynamics and risk modeling = navigating trading and investment portfolios frontiers with machine learning algorithms /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Liquidity dynamics and risk modeling/ by Mazin A. M. Al Janabi.
其他題名:	navigating trading and investment portfolios frontiers with machine learning algorithms /
作者:	Al Janabi, Mazin A. M.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xxxv, 643 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter 1: Beyond the Surface: In-depth Perspectives on Liquidity and Risk Frontiers -- Chapter 2: Unlocking the Microstructure of Liquidity Risk: Understanding Interactions with other Financial Risks and Best Practices in Oversight and Governance -- Chapter 3: Crises to Opportunities: Derivatives Trading, Liquidity Management, and Risk Mitigation Strategies in Emerging Markets -- Chapter 4: Insights into Liquidity Dynamics: Optimizing Asset Allocation and Portfolio Risk Management with Machine Learning Algorithms -- Chapter 5: Navigating Liquidity Waves: Practical Applications of Liquidity Risk Management and Investable Portfolio Optimization in Financial Markets -- Chapter 6: Leveraging Liquidity Models in Commodity Markets: Customizing Risk-Appetite Trading Limits for Robust Investment Portfolios -- Chapter 7: Liquidity Spectrum: Unraveling Theoretical Frameworks and Leveraging Modeling Algorithms for In-Depth Insights into Overall and Relative Liquidity Risk Dynamics -- Chapter 8: Beyond Boundaries: Navigating Liquidity Frontiers with Advanced L-VaR Optimization Algorithms and Strategic Integration of Bid-Ask Modeling Spreads.
Contained By:	Springer Nature eBook
標題:	Liquidity (Economics) -
電子資源:	https://doi.org/10.1007/978-3-031-71503-7
ISBN:	9783031715037

Liquidity dynamics and risk modeling = navigating trading and investment portfolios frontiers with machine learning algorithms /
Al Janabi, Mazin A. M.

Liquidity dynamics and risk modelingnavigating trading and investment portfolios frontiers with machine learning algorithms /[electronic resource] :by Mazin A. M. Al Janabi. - Cham :Springer Nature Switzerland :2024. - xxxv, 643 p. :ill. (some col.), digital ;24 cm.

Chapter 1: Beyond the Surface: In-depth Perspectives on Liquidity and Risk Frontiers -- Chapter 2: Unlocking the Microstructure of Liquidity Risk: Understanding Interactions with other Financial Risks and Best Practices in Oversight and Governance -- Chapter 3: Crises to Opportunities: Derivatives Trading, Liquidity Management, and Risk Mitigation Strategies in Emerging Markets -- Chapter 4: Insights into Liquidity Dynamics: Optimizing Asset Allocation and Portfolio Risk Management with Machine Learning Algorithms -- Chapter 5: Navigating Liquidity Waves: Practical Applications of Liquidity Risk Management and Investable Portfolio Optimization in Financial Markets -- Chapter 6: Leveraging Liquidity Models in Commodity Markets: Customizing Risk-Appetite Trading Limits for Robust Investment Portfolios -- Chapter 7: Liquidity Spectrum: Unraveling Theoretical Frameworks and Leveraging Modeling Algorithms for In-Depth Insights into Overall and Relative Liquidity Risk Dynamics -- Chapter 8: Beyond Boundaries: Navigating Liquidity Frontiers with Advanced L-VaR Optimization Algorithms and Strategic Integration of Bid-Ask Modeling Spreads.

This book presents a high-quality contribution to the applications of modern financial algorithms for liquidity risk management and its practical uses and applications to investable portfolios and mutual funds. It brings together the latest thinking on the emerging topic of contemporary liquidity risk estimations and management and includes principles, reviews, examples, and concrete financial markets applications to trading and investment portfolios. Furthermore, it explores research directions of liquidity risk management using modified Liquidity-Adjusted Value-at-Risk (L-VaR) models with the application of machine learning optimization algorithms. The book presents specific self-contained use-cases throughout, showing practical applications of the concepts discussed and providing further directions for researchers and financial markets participants. The book draws practical insights from personal experiences and applies specific examples (with the use of real-world case studies and analysis) about how the modeling techniques and machine learning optimization algorithms could address specific theoretical and practical issues of liquidity risk management and coherent asset allocation in trading and investment portfolios. It will be of interest to researchers, students, and practitioners of risk management, portfolio management, and machine learning. Mazin A. M. Al Janabi has more than three decades of multifaceted experience across science, technology, finance, and academia. Holding a PhD in Nuclear Engineering from the University of London, UK, Prof. Al Janabi's journey has been marked by roles within top-tier international financial institutions, including ING-Barings and BBVA, where he held senior management positions like Director of Global Market Risk Management, Head of Trading Risk Management, Head of Derivative Products, and Head of Fixed Income Research. He also served as a Full Research Professor at institutions such as EGADE Business School, Tecnologico de Monterrey in Mexico, United Arab Emirates University (UAEU) in Abu Dhabi, and Al Akhawayn University in Ifrane (AUI), Morocco. A research fellow at the Economic Research Forum (ERF), Prof. Al Janabi's scholarly pursuits delve into the dynamics of emerging and developed economies.

ISBN: 9783031715037

Standard No.: 10.1007/978-3-031-71503-7doiSubjects--Topical Terms:

650042
Liquidity (Economics)

LC Class. No.: HG178

Dewey Class. No.: 658.15244

Liquidity dynamics and risk modeling = navigating trading and investment portfolios frontiers with machine learning algorithms /
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520 $a This book presents a high-quality contribution to the applications of modern financial algorithms for liquidity risk management and its practical uses and applications to investable portfolios and mutual funds. It brings together the latest thinking on the emerging topic of contemporary liquidity risk estimations and management and includes principles, reviews, examples, and concrete financial markets applications to trading and investment portfolios. Furthermore, it explores research directions of liquidity risk management using modified Liquidity-Adjusted Value-at-Risk (L-VaR) models with the application of machine learning optimization algorithms. The book presents specific self-contained use-cases throughout, showing practical applications of the concepts discussed and providing further directions for researchers and financial markets participants. The book draws practical insights from personal experiences and applies specific examples (with the use of real-world case studies and analysis) about how the modeling techniques and machine learning optimization algorithms could address specific theoretical and practical issues of liquidity risk management and coherent asset allocation in trading and investment portfolios. It will be of interest to researchers, students, and practitioners of risk management, portfolio management, and machine learning. Mazin A. M. Al Janabi has more than three decades of multifaceted experience across science, technology, finance, and academia. Holding a PhD in Nuclear Engineering from the University of London, UK, Prof. Al Janabi's journey has been marked by roles within top-tier international financial institutions, including ING-Barings and BBVA, where he held senior management positions like Director of Global Market Risk Management, Head of Trading Risk Management, Head of Derivative Products, and Head of Fixed Income Research. He also served as a Full Research Professor at institutions such as EGADE Business School, Tecnologico de Monterrey in Mexico, United Arab Emirates University (UAEU) in Abu Dhabi, and Al Akhawayn University in Ifrane (AUI), Morocco. A research fellow at the Economic Research Forum (ERF), Prof. Al Janabi's scholarly pursuits delve into the dynamics of emerging and developed economies.
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