VOLATILITY MODELLING AND TIME SERIES ANALYSIS
Theoretical and Empirical Investigation
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The entire financial system is based on interaction between risk and return. Financial researchers and analysts express the risk as the standard deviation of returns which is referred as volatility. Since Engle (1982) impressed the financial community by introducing the ARCH model, there have been a lot of extensions of the basic ARCH process. These kind of nonlinear time series processes consider the volatility as time varying and estimate it based on historical data. Based on the univariate and multivariate representation of those models, we can explain crucial financial phenomena such as th...
The entire financial system is based on interaction between risk and return. Financial researchers and analysts express the risk as the standard deviation of returns which is referred as volatility. Since Engle (1982) impressed the financial community by introducing the ARCH model, there have been a lot of extensions of the basic ARCH process. These kind of nonlinear time series processes consider the volatility as time varying and estimate it based on historical data. Based on the univariate and multivariate representation of those models, we can explain crucial financial phenomena such as the leverage effect, contagion and the interaction between the global stock markets. This book presents the most applied univariate and multivariate time series processes and it is identical for portfolio managers, investors and financial researchers, who are interesting in return and volatility modelling. They can find all the basic tools that they need in order to make research and analyze stock markets, financial crises, multiple assets for portfolio optimization, contagion and spillover effects.
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