Stochastic time series model

Part 2 of ebook "Essentials of time series for financial applications" provides readers with contents including: Chapter 6 - Multivariate GARCH and conditional correlation models; Chapter 7 - Multifactor heteroskedastic models, stochastic volatility; Chapter 8 - Models with breaks, recurrent regime switching, and nonlinearities; Chapter 9 - Markov switching models; Chapter 10 - Realized volatility and covariance;...

189p daonhiennhien 03-07-2024 1 1   Download

Part 1 of ebook "Introductory time series with R" provides readers with contents including: Chapter 1 - Time series data; Chapter 2 - Correlation; Chapter 3 - Forecasting strategies; Chapter 4 - Basic stochastic models; Chapter 5 - Regression;...

131p daonhiennhien 03-07-2024 3 1   Download

An evaluation was conducted to develop a stochastic time series model, capable of prediction of rainfall and runoff in Karso watershed. The Karso Watershed selected for hydrological studies is one of the sub watershed of the Damoder dam catchment of upper Damodar Valley, comprising a cover the area of 27.41 km2 . The hydrologic sequences data of watershed collected from Soil Conservation Deptt., Damodar Valley Corporation, Hazaribagh, Jharkhand State were analysed. The watershed be capable of be divided into three main landscapes.

7p kequaidan4 05-05-2020 13 0   Download

The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term); thus the model is in the form of a stochastic differential equation. The present study was conducted with the main objective to develop a stochastic time series model for prediction of rainfall of Allahabad district, which lies between 250 47’ N latitude, 810 21’E longitude and elevation of 104 m from the mean sea level.

7p kethamoi4 18-04-2020 14 1   Download

Present investigation was an attempt to study the trend of jute production in West Bengal for the period starting from 1950 to 2016. For stochastic trend estimation, a number of time series parametric regression models viz. Linear model, Quadratic model, Exponential model, Logarithmic model, Power model and Auto Regressive Integrated Moving Average (ARIMA) were employed and compared for finding out an appropriate econometric model to capture the trend of jute production of the country. Based on the performance of several goodness of fit criteria viz.

12p trinhthamhodang3 17-02-2020 27 0   Download

This paper investigates high frequency time-series features of stock returns and volatility on China's stock markets. The empirically observed probability distributions of log-returns are almost symmetric, highly leptokurtic, and characterized by a non-Gaussian profile for small index changes. Thus, the China's stock markets cannot be described by a random walk. We suggest that the correlation dynamics and stochastic changes of stock prices of China's stock markets are investigated by the Lorentz stable distribution.

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This chapter’s objectives are to: Explain how stochastic difference equations can be used for forecasting and illustrate how such equations can arise from familiar economic models, explain what it means to solve a difference equation, demonstrate how to find the solution to a stochastic difference equation using the iterative method,...

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This chapter’s objectives are to: Formalize simple models of variables with a time-dependent mean, compare models with deterministic versus stochastic trends, show that the so-called unit root problem arises in standard regression and in timesseries models,...

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This chapter’s objectives are to: Describe the theory of stochastic linear difference equations, develop the tools used in estimating ARMA models, consider the time-series properties of stationary and nonstationary models,...

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This chapter’s objectives are to: Introduce the basic concept of cointegration and show that it applies in a variety of economic models, show that cointegration necessitates that the stochastic trends of nonstationary variables be linked

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Chapter 6 - Univariate time series modelling and forecasting. In this chapter, you will learn how to: Explain the defining characteristics of various types of stochastic processes, identify the appropriate time series model for a given data series, produce forecasts for ARMA and exponential smoothing models, evaluate the accuracy of predictions using various metrics, estimate time series models and produce forecasts from them in EViews.

62p estupendo3 18-08-2016 57 5   Download

Chapter 2 Time series 2.1. Two workhorses This chapter describes two tractable models of time series: Markov chains and first-order stochastic linear difference equations. These models are organizing devices that put particular restrictions on a sequence of random vectors.

56p summerflora 28-10-2010 110 6   Download