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Full Description
The growth of financial complexity, technology, and big data is transforming and integrating computational statistics and data science; in their wake, it's also changing financial engineering. This first volume introduces elements of computational statistics and data algorithms and considers conventional financial models using statistical models. Such a method provides a more transparent approach to data-science methods when applied to financial data.
This book focuses on financial data including time series, default models, and their increasing complexity in a technological and global financial world. It outlines elements of computational statistics and features applications, including problems and models of credit risks and time series applied to various financial problems. Based on multiple sources, academic research, and applications drawn from various domains and adapted to financial data, this book will be of interest to financial engineering researchers, students, and practitioners.
Contents
Chapter 1. Finance and Data.- Chapter 2. Data Everywhere.- Chapter 3. Data and Statistical Models.- Chapter 4. Computational Statistics and Regressions.- Chapter 5. Algorithms, Glm and Data Reduction.- Chapter 6. Statistical and Data Reduction.- Chapter 7. Multivariate Statistical Distributions.- Chapter 8. Data Information and Entropy.- Chapter 9. Graphs and Networks.- Chapter 10. Modeling Memory and Learning.- Chapter 11. Bayesian Learning.- Chapter 12: Bayesian Networks.- Chapter 13. Bayesian Models and Kalman's Filter.
