This volume contains six chapters which cover several modern topics of quantitative finance and reflect the most significant trends currently shaping this field. The chapters discuss in detail and make original contributions to stochastic/fractional volatility models and their asymptotic solutions (Chapter 1); equity trading, optimal portfolios and related problems (Chapters 2, 5, 6); machine learning and NLP (Chapters 2, 3); and economic scenario generation (Chapter 4), and are written by the leading experts in the field. This book is useful for both researchers and practitioners.
Contents:
- About the Editor
- About the Contributors
- Introduction
- Multivariate Stochastic Volatility Models and Large Deviation Principles (Archil Gulisashvili)
- Phases of MANES: Multi-Asset Non-Equilibrium Skew Model of a Strongly Nonlinear Market with Phase Transitions (Igor Halperin)
- Mathematics of Embeddings: Spillover of Polarities over Financial Texts (Mengda Li and Charles-Albert Lehalle)
- Optimal ESG Portfolios: Which ESG Ratings to Use? (Anatoly Schmidt and Xu Zhang)
- Centrality of the Supply Chain Network (Liuren Wu)
- Are E-mini S&P 500 Futures Prices Random? (Valerii Salov)
- Index
Readership: Undergraduates, graduates and researchers specializing in quantitative finance, and practitioners in the field.
Key Features:
- Topics of the series are broad enough to cover relevant aspects from both traditional quantitative disciplines such as Mathematics, Stochastics, Statistics, Engineering, Computer Science, Economics, Econophysics, Risk Management, Investments, Insurance, and more recent areas such as Fintech (digital lending and credit, mobile banking, mobile payments, cryptocurrency&blockchain), Machine Learning (deep learning, reinforcement learning, etc.) or other quantitative disciplines
This volume contains six chapters which cover several modern topics of quantitative finance and reflect the most significant trends currently shaping this field. The chapters discuss in detail and make original contributions to stochastic/fractional volatility models and their asymptotic solutions (Chapter 1); equity trading, optimal portfolios and related problems (Chapters 2, 5, 6); machine learning and NLP (Chapters 2, 3); and economic scenario generation (Chapter 4), and are written by the leading experts in the field. This book is useful for both researchers and practitioners.
Contents:
- About the Editor
- About the Contributors
- Introduction
- Multivariate Stochastic Volatility Models and Large Deviation Principles (Archil Gulisashvili)
- Phases of MANES: Multi-Asset Non-Equilibrium Skew Model of a Strongly Nonlinear Market with Phase Transitions (Igor Halperin)
- Mathematics of Embeddings: Spillover of Polarities over Financial Texts (Mengda Li and Charles-Albert Lehalle)
- Optimal ESG Portfolios: Which ESG Ratings to Use? (Anatoly Schmidt and Xu Zhang)
- Centrality of the Supply Chain Network (Liuren Wu)
- Are E-mini S&P 500 Futures Prices Random? (Valerii Salov)
- Index
Readership: Undergraduates, graduates and researchers specializing in quantitative finance, and practitioners in the field.
Key Features:
- Topics of the series are broad enough to cover relevant aspects from both traditional quantitative disciplines such as Mathematics, Stochastics, Statistics, Engineering, Computer Science, Economics, Econophysics, Risk Management, Investments, Insurance, and more recent areas such as Fintech (digital lending and credit, mobile banking, mobile payments, cryptocurrency&blockchain), Machine Learning (deep learning, reinforcement learning, etc.) or other quantitative disciplines
REVIEWS IN MODERN QUANTITATIVE FINANCE
400
REVIEWS IN MODERN QUANTITATIVE FINANCE
400
Product Details
| ISBN-13: | 9789811281754 |
|---|---|
| Publisher: | WSPC |
| Publication date: | 03/12/2024 |
| Series: | Annual Reviews in Modern Quantitative Finance: Including Current Aspects of Fintech, Risk and Investments , #1 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 400 |
| File size: | 103 MB |
| Note: | This product may take a few minutes to download. |