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Investigating dependence structures of stocks that are related to one another should be an important consideration in managing a stock portfolio, among other investment strategies. To capture various dependence features, we employ copula. Financial time series data is typically characterized by volatility clustering of returns that influences an estimate of a stock’s future price. To deal with the volatility and dependence of stock returns, this book provides procedures of combining a copula with a GARCH model. Using the copula-GARCH approach that describes the tail dependences of stock returns, we carry out Monte Carlo simulations to predict a company’s movements in the stock market. The procedures are illustrated in two technology stocks, Apple and Samsung.
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Seung-Hwan Lee - Associate Professor. Department of Mathematics. Illinois Wesleyan University, Bloomington.
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Forecasting Stock Returns using a Copula-GARCH model
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LAP Lambert Academic Publishing Nov 2017, 2017
ISBN 10: 3659233579
ISBN 13: 9783659233579
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Investigating dependence structures of stocks that are related to one another should be an important consideration in managing a stock portfolio, among other investment strategies. To capture various dependence features, we employ copula. Financial time series data is typically characterized by volatility clustering of returns that influences an estimate of a stock's future price. To deal with the volatility and dependence of stock returns, this book provides procedures of combining a copula with a GARCH model. Using the copula-GARCH approach that describes the tail dependences of stock returns, we carry out Monte Carlo simulations to predict a company's movements in the stock market. The procedures are illustrated in two technology stocks, Apple and Samsung. 60 pp. Englisch. Seller Inventory # 9783659233579
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Forecasting Stock Returns using a Copula-GARCH model
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LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659233579
ISBN 13: 9783659233579
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Lee Seung-HwanSeung-Hwan Lee - Associate Professor. Department of Mathematics. Illinois Wesleyan University, Bloomington.Investigating dependence structures of stocks that are related to one another should be an important conside. Seller Inventory # 385766304
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Forecasting Stock Returns using a Copula-GARCH model
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LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3659233579
ISBN 13: 9783659233579
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Paperback. Condition: Brand New. 60 pages. 8.66x5.91x0.14 inches. In Stock. Seller Inventory # 3659233579
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Forecasting Stock Returns using a Copula-GARCH model
Published by
LAP Lambert Academic Publishing Nov 2017, 2017
ISBN 10: 3659233579
ISBN 13: 9783659233579
New
Taschenbuch
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Taschenbuch. Condition: Neu. Neuware -Investigating dependence structures of stocks that are related to one another should be an important consideration in managing a stock portfolio, among other investment strategies. To capture various dependence features, we employ copula. Financial time series data is typically characterized by volatility clustering of returns that influences an estimate of a stock¿s future price. To deal with the volatility and dependence of stock returns, this book provides procedures of combining a copula with a GARCH model. Using the copula-GARCH approach that describes the tail dependences of stock returns, we carry out Monte Carlo simulations to predict a company¿s movements in the stock market. The procedures are illustrated in two technology stocks, Apple and Samsung.Books on Demand GmbH, Überseering 33, 22297 Hamburg 60 pp. Englisch. Seller Inventory # 9783659233579
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Forecasting Stock Returns using a Copula-GARCH model
Published by
LAP Lambert Academic Publishing, 2017
ISBN 10: 3659233579
ISBN 13: 9783659233579
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Investigating dependence structures of stocks that are related to one another should be an important consideration in managing a stock portfolio, among other investment strategies. To capture various dependence features, we employ copula. Financial time series data is typically characterized by volatility clustering of returns that influences an estimate of a stock's future price. To deal with the volatility and dependence of stock returns, this book provides procedures of combining a copula with a GARCH model. Using the copula-GARCH approach that describes the tail dependences of stock returns, we carry out Monte Carlo simulations to predict a company's movements in the stock market. The procedures are illustrated in two technology stocks, Apple and Samsung. Seller Inventory # 9783659233579
Quantity: 1 available