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Financial Statistics and Data Analytics
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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.
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Keywords
- ACD models
- Antiques & collectables
- asymptotic
- B-splines
- banking competition
- Bitcoin
- Bonds
- Box-Cox transformation
- capital asset pricing model
- characteristic function-based estimator
- convergence analysis
- credit risk
- Efficiency
- estimation
- estimation of systematic risk
- Euro-Dollar
- financial incentives
- financial models
- fractal scaling
- GARCH model
- generalized Birnbaum–Saunders distributions
- generalized method of moments
- gold price
- goodness-of-fit
- Griddy-Gibs
- HARCH model
- heavy tails
- high-frequency financial data
- Hill estimator
- Index parameter
- intention to leave
- Interest rates
- job performance
- Job satisfaction
- Lerner index
- Lifestyle, sport & leisure
- long range dependence
- multicollinearity
- multifactor asset pricing model
- multifractal processes
- no-arbitrage
- NPLs
- oil price
- PHARCH model
- public service motivation
- ridge regression
- safe-haven assets
- seemingly unrelated regression model
- shrinkage estimator
- stochastic frontiers
- Swiss Franc exchange rate
- t-distribution
- tests of mean-variance efficiency
- Theil index
- time series
- wrapped stable
- yeld curve
Links
DOI: 10.3390/books978-3-03943-976-8Editions