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This handbook presents emerging research exploring the theoretical
and practical aspects of econometric techniques for the financial
sector and their applications in economics. By doing so, it offers
invaluable tools for predicting and weighing the risks of multiple
investments by incorporating data analysis. Throughout the book the
authors address a broad range of topics such as predictive
analysis, monetary policy, economic growth, systemic risk and
investment behavior. This book is a must-read for researchers,
scholars and practitioners in the field of economics who are
interested in a better understanding of current research on the
application of econometric methods to financial sector data.
This handbook presents emerging research exploring the theoretical
and practical aspects of econometric techniques for the financial
sector and their applications in economics. By doing so, it offers
invaluable tools for predicting and weighing the risks of multiple
investments by incorporating data analysis. Throughout the book the
authors address a broad range of topics such as predictive
analysis, monetary policy, economic growth, systemic risk and
investment behavior. This book is a must-read for researchers,
scholars and practitioners in the field of economics who are
interested in a better understanding of current research on the
application of econometric methods to financial sector data.
This book explains the theoretical structure of particle swarm
optimization (PSO) and focuses on the application of PSO to
portfolio optimization problems. The general goal of portfolio
optimization is to find a solution that provides the highest
expected return at each level of portfolio risk. According to H.
Markowitz's portfolio selection theory, as new assets are added to
an investment portfolio, the total risk of the portfolio's
decreases depending on the correlations of asset returns, while the
expected return on the portfolio represents the weighted average of
the expected returns for each asset. The book explains PSO in
detail and demonstrates how to implement Markowitz's portfolio
optimization approach using PSO. In addition, it expands on the
Markowitz model and seeks to improve the solution-finding process
with the aid of various algorithms. In short, the book provides
researchers, teachers, engineers, managers and practitioners with
many tools they need to apply the PSO technique to portfolio
optimization.
This book explains the theoretical structure of particle swarm
optimization (PSO) and focuses on the application of PSO to
portfolio optimization problems. The general goal of portfolio
optimization is to find a solution that provides the highest
expected return at each level of portfolio risk. According to H.
Markowitz's portfolio selection theory, as new assets are added to
an investment portfolio, the total risk of the portfolio's
decreases depending on the correlations of asset returns, while the
expected return on the portfolio represents the weighted average of
the expected returns for each asset. The book explains PSO in
detail and demonstrates how to implement Markowitz's portfolio
optimization approach using PSO. In addition, it expands on the
Markowitz model and seeks to improve the solution-finding process
with the aid of various algorithms. In short, the book provides
researchers, teachers, engineers, managers and practitioners with
many tools they need to apply the PSO technique to portfolio
optimization.
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