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Sinopsis
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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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.
Biografía del autor
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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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 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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Condición: 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. Nº de ref. del artículo: 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. Condición: Brand New. 60 pages. 8.66x5.91x0.14 inches. In Stock. Nº de ref. del artículo: 3659233579
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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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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783659233579
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Forecasting Stock Returns using a Copula-GARCH model
Publicado por
LAP Lambert Academic Publishing, 2017
ISBN 10: 3659233579
ISBN 13: 9783659233579
Nuevo
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783659233579
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