Forecasting model

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  • The goals of this chapter are: To explain why exchange rate forecasting is needed, to illustrate forecasting techniques, to present empirical evidence on forecasting models, to explain how forecasters are evaluated, to demonstrate how technical analysis is used to generate buy and sell signals.

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  • Tham khảo sách 'financial modeling with crystal ball and excel', công nghệ thông tin, tin học văn phòng phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • The focus in this book is on the study of market risk from a quantitative point of view. The emphasis is on presenting commonly used state-of-the-art quantitative techniques used in finance for the management of market risk and demonstrate their use employing the principal two mathematical programming languages, R and Matlab. All the code in the book can be downloaded from the book’s website at www.financialrisk forecasting.com

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  • Modeling Hydrologic Change: Statistical Methods is about modeling systems where change has affected data that will be used to calibrate and test models of the systems and where models will be used to forecast system responses after change occurs. The focus is not on the hydrology. Instead, hydrology serves as the discipline from which the applications are drawn to illustrate the principles of modeling and the detection of change. All four elements of the modeling process are discussed: conceptualization, formulation, calibration, and verification.

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  • This book reports initial efforts in providing some useful extensions in financial modeling; further work is necessary to complete the research agenda. The demonstrated extensions in this book in the computation and modeling of optimal control in finance have shown the need and potential for further areas of study in financial modeling. Potentials are in both the mathematical structure and computational aspects of dynamic optimization. There are needs for more organized and coordinated computational approaches.

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  • Weather natural hazards, the environment and climate change are of concern to all of us. Especially, it is essential to understand how human activities might impact the nature. Hence, monitoring, research, and forecasting is of the outmost importance. Furthermore, climate change and pollution of the environment do not obey national borders; so, international collaboration on these issues is indeed extremely important.

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  • This study briefly summarizes the thunderstorm activities in Vietnam. To predict thunderstorms in the Noi Bai Airport region, the thunderstorm indices are calculated for 64 grid points nearby Noi Bai region from the predicted meteorological fields with RAMS (Regional Atmospheric Modeling System) model. The forecast procedure for thunderstorm is built for this region with four prediction factors, such as CAPEmax, Kimax, SI min, Vtmax in the forecast threshold of 0.6. As a result, the occurrence of thunderstorms reaches 80% for the duration of 36 hours.

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  • The leading feature may be considered as a description model and as a method in formgiving with demands on documentation and clarification of the methodical approach. Some of the students have previously completed a project 'Experienced architectural quality' where they were introduces to the leading feature as a method for selecting a sustaining idea, a structural principle (such as a building consisting of serial plans of increasing - decreasing patterns) and a content (atelier + primitive housing expressed by its space program).

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  • Our results suggest that stronger competition implies significantly lower interest rate spreads for most loan market products, as we expected. This result implies that bank interest rates are lower and that the pass-through of market rates is stronger, the heavier competition is. We find evidence of the latter in our error correction model of bank interest rates. Furthermore, when loan market competition is stronger, we observe larger bank spreads (that is, lower bank interest rates) on current account and time deposits.

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  • Supporters of credit scoring note that credit scores have statistical validity, and are predictive of repayment behavior for large populations. However, this does not mean that credit data are error free, nor that credit scoring models are perfect predictors of individual creditworthiness; it only means that they work on average. While the systems do present an accurate risk profile of a large numbers of consumers, data users who manage large numbers of accounts priced by credit risk have a greater tolerance for errors in credit scoring systems than consumers do.

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  • Convergence problem of an economic variable represents an underlying forecast of neoclassical economic growth model. This paper aims to analyze the convergence of provincial per capita GDP stability in Vietnam over the period of 1991-2007.

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  • When you complete this chapter you should be able to: Understand the three time horizons and which models apply for each; explain when to use each of the four qualitative models; apply the naive, moving-average, exponential smoothing, and trend methods; compute three measures of forecast accuracy; develop seasonal indices; conduct a regression and correlation analysis; use a tracking signal.

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  • We describe our initial investigations into generating textual summaries of spatiotemporal data with the help of a prototype Natural Language Generation (NLG) system that produces pollen forecasts for Scotland. forecasts were written. An example of a pollen forecast text is shown in Figure 1, its corresponding data is shown in table 1. A pollen forecast in the map form is shown in Figure 2. ‘Monday looks set to bring another day of relatively high pollen counts, with values up to a very high eight in the Central Belt. ...

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  • Toe erosion, especially in stormy conditions, is one of the common mechanism causing the failure and instability of the sea dikes and revetments. The erosion intensity becomes more serious at the beaches which is under the impacts of typhoons. Reliable forecasts about the intensity of toe erosion of sea dikes in stormy conditions have important ecnomomic and technical meaning in the design and construction of sea dikes.

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  • Time Series Forecasting – Part I presents about What is a Time Series? Components of Time Series, Evaluation Methods of Forecast, Smoothing Methods of Time Series, Time series models, Components of a time series, Trend component.

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  • Time Series Forecasting (Part II) povides about Stationary and nonstationary processes, Autocorrelation function, Autoregressive models AR, Moving Average models MA, ARMA models, Estimating and checking ARIMA models(Box-Jenkins Methodology).

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  • Lecture notes in Macroeconomic and financial forecasting include all of the following: Elementary statistics, trends and seasons, forecasting, time series analysis, overview of macroeconomic forecasting, business cycle facts, data quality, survey data and indicators, using financial data in macroeconomic forecasting, macroeconomic models,...

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  • Expected asset return measures are needed to construct portfolios, plan for retirement, value equities and options, and forecast corporate cash flows. In this lecture, students will: Compute historical returns for stocks and market indexes; estimate expected asset returns using the Capital Asset Pricing Model (CAPM), Global CAPM (GCAPM), the Fama-French 3 Factor Model (FF3F), and fundamental analysis; use excel styles and formats; create modules and write simple VBA programs.

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  • Expected asset risk measures are needed to construct optimal portfolios, plan for retirement, value equities and options, and forecast corporate cash flow distributions. In this lecture, students will: Compute asset return variance and standard deviation, scale standard deviations across time, compute moving average volatility, compute volatility using EWMA models, compute implied volatility using the black-scholes option pricing model.

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  • Topic 15 - Building stochastic free cash flow and DCF models using excel and @Risk. After completing this unit, you should be able to: Forecast and simulate free cash flows, value common stock using discounted cash flow, use other distributional assumptions in @Risk to create stochastic DCF models.

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