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For his excellent monograph, David Ardia won the Chorafas prize 2008 at the University of Fribourg Switzerland. This book presents methodologies for the Bayesian estimation of GARCH models and their application to financial risk management. The study of these models from a Bayesian viewpoint is relatively recent and can be considered very promising due to the advantages of the Bayesian approach, in particular the possibility of obtaining small-sample results and integrating these results in a formal decision model. The first two chapters introduce the work and give an overview of the Bayesian paradigm for inference. The next three chapters describe the estimation of the GARCH model with Normal innovations and the linear regression models with conditionally Normal and Student-t-GJR errors. The sixth chapter shows how agents facing different risk perspectives can select their optimal Value at Risk Bayesian point estimate and documents that the differences between individuals can be substantial in terms of regulatory capital. The last chapter proposes the estimation of a Markov-switching GJR model.

Bayesian statistical decision theory. --- Risk management --- GARCH model. --- Mathematical models. --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Generalized ARCH model --- Generalized Autoregressive Conditional Heteroskedasticity model --- Derivative securities --- Stochastic models --- Mathematical models --- Econometrics. --- Macroeconomics. --- Statistics. --- Finance. --- Macroeconomics/Monetary Economics//Financial Economics. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Quantitative Finance. --- Funding --- Funds --- Economics --- Currency question --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Economics, Mathematical --- Statistics --- Statistics . --- Economics, Mathematical . --- Mathematical economics --- Methodology --- Social sciences --- Macroeconomics and Monetary Economics. --- Statistics in Business, Management, Economics, Finance, Insurance. --- Mathematics in Business, Economics and Finance. --- Mathematics.

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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.

Coins, banknotes, medals, seals (numismatics) --- 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