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This paper deals with the potential positive effect of financial development on economic growth, also known as the Finance-Growth Nexus. Much uncertainty and contradiction surround the subject matter. Based on the uncertain problem set at hand, the purpose of this study is to reduce ambiguity and provide a basis for future, more detailed research. The paper aims to add value by further refining the theoretical definition of financial development, as well as further developing empirical measurement. Furthermore, a large set of financial development variables and different estimation techniques are systematically tested against a harmonized data set, in order to increase the neutrality and comparability of results. However, results of various regression iterations were neither stable, nor always fully comparable due to changes in sample composition. Ergo, the high degree of uncertainty remains, and the author cannot reject the Null Hypothesis that there is no stable correlation between financial development and economic growth. There are many different potential explanations, but all lead to the same conclusion. Perhaps the mechanisms, interrelations and potential effects of financial development on economic growth cannot be measured from a macro perspective. Instead, focusing on a micro approach in future research could potentially add value.
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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
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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.
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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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 --- 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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Written by one of the leading experts in the field, this book focuses on the interplay between model specification, data collection, and econometric testing of dynamic asset pricing models. The first several chapters provide an in-depth treatment of the econometric methods used in analyzing financial time-series models. The remainder explores the goodness-of-fit of preference-based and no-arbitrage models of equity returns and the term structure of interest rates; equity and fixed-income derivatives prices; and the prices of defaultable securities. Singleton addresses the restrictions on t
Capital assets pricing model. --- Pricing --- Econometric models. --- Arbitrage. --- Asymptotic distribution. --- Autocorrelation. --- Autocovariance. --- Autoregressive conditional heteroskedasticity. --- Bayesian inference. --- Bayesian probability. --- Bond Yield. --- Capital asset pricing model. --- Central limit theorem. --- Collateral Value. --- Conditional expectation. --- Conditional probability distribution. --- Conditional variance. --- Consistent estimator. --- Correlation and dependence. --- Covariance function. --- Covariance matrix. --- Credit risk. --- Credit spread (options). --- Discount function. --- Discrete time and continuous time. --- Doubly stochastic model. --- Dynamic pricing. --- Econometric model. --- Economic equilibrium. --- Economics. --- Equity premium puzzle. --- Ergodic process. --- Estimation theory. --- Estimation. --- Estimator. --- Expectations hypothesis. --- Expected value. --- Forecasting. --- Forward price. --- Forward rate. --- General equilibrium theory. --- Generalized method of moments. --- High-yield debt. --- Inference. --- Interest rate risk. --- Interest rate. --- Investment Horizon. --- Investment strategy. --- Investor. --- Joint probability distribution. --- LIBOR market model. --- Leverage (finance). --- Likelihood function. --- Liquidity premium. --- Liquidity risk. --- Margin (finance). --- Marginal rate of substitution. --- Marginal utility. --- Market Risk Premium. --- Market capitalization. --- Market liquidity. --- Market portfolio. --- Market price. --- Market value. --- Markov model. --- Markov process. --- Mathematical finance. --- Monetary policy. --- Objective Probability. --- Option (finance). --- Parameter. --- Partial equilibrium. --- Portfolio insurance. --- Precautionary savings. --- Predictability. --- Preference (economics). --- Present value. --- Price index. --- Pricing. --- Principal component analysis. --- Probability. --- Real interest rate. --- Repurchase agreement. --- Revaluation of fixed assets. --- Risk aversion. --- Risk management. --- Risk premium. --- Skewness. --- Special case. --- Standard deviation. --- State variable. --- Statistic. --- Stochastic differential equation. --- Stochastic volatility. --- Supply (economics). --- Time series. --- Underlying Security. --- Utility maximization problem. --- Utility. --- Variable (mathematics). --- Vector autoregression. --- Yield curve. --- Yield spread.
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