16,99 €
inkl. MwSt.
Versandfertig in 6-10 Tagen
8 °P sammeln
Andere Kunden interessierten sich auch für
![A Semi-Parametric Copula Estimation under Censoring]( "A Semi-Parametric Copula Estimation under Censoring")
A Semi-Parametric Copula Estimation under Censoring44,99 €![Short-term Rainfall Forecasting using ANNs and ANFIS Models]( "Short-term Rainfall Forecasting using ANNs and ANFIS Models")
Short-term Rainfall Forecasting using ANNs and ANFIS Models32,99 €![Bayesian Approach For Forecasting Model]( "Bayesian Approach For Forecasting Model")
Bayesian Approach For Forecasting Model36,99 €![A STUDY ON CLASS OF INVENTORY MODELS WITH TWO LEVELS OF STORAGE]( "A STUDY ON CLASS OF INVENTORY MODELS WITH TWO LEVELS OF STORAGE")
A STUDY ON CLASS OF INVENTORY MODELS WITH TWO LEVELS OF STORAGE29,99 €![Interaction Network Inference and Stock Market Modelling]( "Interaction Network Inference and Stock Market Modelling")
Interaction Network Inference and Stock Market Modelling27,99 €![High Frequency Time Series Forecasting]( "High Frequency Time Series Forecasting")
High Frequency Time Series Forecasting46,99 €![The mathematical theory of stock market]( "The mathematical theory of stock market")
The mathematical theory of stock market32,99 €
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.