Financial Econometrics and Empirical Market Microstructure

ISBN-13: 9783319352077 / Angielski / Miękka / 2016 / 284 str.

Anil K. Bera; Sergey Ivliev; Fabrizio Lillo

Opis

In the era of Big Data our society is given the unique opportunity to understand the inner dynamics and behavior of complex socio-economic systems. Advances in the availability of very large databases, in capabilities for massive data mining, as well as progress in complex systems theory, multi-agent simulation and computational social science open the possibility of modeling phenomena never before successfully achieved. This contributed volume from the Perm Winter School address the problems of the mechanisms and statistics of the socio-economics system evolution with a focus on financial markets powered by the high-frequency data analysis. ​

Spis treści

Mathematical Models of Price Impact and Optimal Portfolio Management in Illiquid Markets.- Evidence of Microstructure Variables' Nonlinear Dynamics from Noised High-Frequency Data.- Revisiting of Empirical Zero Intelligence Models.- Construction and Backtesting of a Multi-Factor Stress-Scenario for the Stock Market.- Modeling Financial Market Using Percolation Theory.- How Tick Size Affects the High Frequency Scaling of Stock Return Distributions.- Market Shocks: Review of Studies.- The Synergy of Rating Agencies' Efforts: Russian Experience.- Spread Modelling Under Asymmetric Information.- On the Modeling of Financial Time Series.- Adaptive Stress Testing: Amplifying Network Intelligence by Integrating Outlier Information.- On Some Approaches to Managing Market Risk Using Var Limits: A Note.- Simulating the Synchronizing Behavior of High-Frequency Trading in Multiple Markets.- Raising Issues About Impact of High Frequency Trading on Market Liquidity.- Application of Copula Models for Modeling One-Dimensional Time Series.- Modeling Demand for Mortgage Loans Using Loan-Level Data.- Sample Selection Bias in Mortgage Market Credit Risk Modeling.- Global Risk Factor Theory and Risk Scenario Generation Based on the Rogov-Causality Test of Time Series Time-Warped Longest Common Subsequence.- Stress-Testing Model for Corporate Borrower Portfolios.

O autorze

Anil K. Bera, Ph.D., Professor of Economics at University of Illinois. Since 1989, Dr. Bera has been awarded eight degrees from Calcutta University, the Indian Statistical Institute, and the Australian National University for his excellence in teaching as well as professional and postgraduate training. He is the author of the popular Jarque–Bera goodness-of-fit test, as well as numerous publications in econometrics and economic statistics.

Sergey Ivliev, Ph.D. in economics, Associate Professor at Perm State University, Chief Research Officer at PROGNOZ, Member of Steering committee of PRMIA Russia. He is a Principal coordinator of Perm Winter School and editor of the Market Risk and Financial Markets Modeling book published by Springer.

Fabrizio Lillo, Professor of Quantitative Finance at the Scuola Normale Superiore di Pisa (Italy), Assistant Professor of Physics at Palermo University (Italy) and External Professor at the Santa Fe Institute (USA). He is author of more than 60 referred scientific papers. The ISI papers have received more than 800 citations and his h-index is 16. His research is focused on the application of methods and tools of statistical physics to economic, financial, and biological complex systems.