Financial Statistics and Data Analytics

doi:10.3390/books978-3-03943-976-8

Download Url(s)

https://mdpi.com/books/pdfview/book/3444

Contributor(s)

Liu, Shuangzhe (editor)

Sathye, Milind (editor)

Language

English

Show full item record

Abstract

Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.

URI

https://directory.doabooks.org/handle/20.500.12854/68428

Keywords

Index parameter; estimation; wrapped stable; Hill estimator; characteristic function-based estimator; asymptotic; efficiency; GARCH model; HARCH model; PHARCH model; Griddy-Gibs; Euro-Dollar; safe-haven assets; gold price; Swiss Franc exchange rate; oil price; generalized Birnbaum–Saunders distributions; ACD models; Box-Cox transformation; high-frequency financial data; goodness-of-fit; banking competition; credit risk; NPLs; Theil index; convergence analysis; interest rates; yeld curve; no-arbitrage; bonds; B-splines; time series; multifractal processes; fractal scaling; heavy tails; long range dependence; financial models; Bitcoin; capital asset pricing model; estimation of systematic risk; tests of mean-variance efficiency; t-distribution; generalized method of moments; multifactor asset pricing model; Lerner index; stochastic frontiers; shrinkage estimator; seemingly unrelated regression model; multicollinearity; ridge regression; financial incentives; public service motivation; job performance; job satisfaction; intention to leave