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Financial Statistics and Data Analytics

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 ha...

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Format: Online
Sprog:engelsk
Udgivet: MDPI - Multidisciplinary Digital Publishing Institute 2021
Fag:
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
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Summary: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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