report on the time-series analysis of continuously compounded returns for Ford and GM for the periods January 2002 till April 2007 using monthly stock prices. This analysis is aimed at estimating the ARIMA model that provides the best forecast for the series. This paper will be divided into 2 sections; the first section showing the Ford analysis and the second the GM analysis. Section 1: Ford Figure 1: Time series plot for raw Ford data. Figure 1 shows a time series plot of the
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Secondary Research Time Series Analysis VARIABLE FACTOR THAT INCREASING MALAYSIA GDP Prepared by: Dina Maya Avinati Wery Astuti Faculty of Business UNIVERSITAS SISWA BANGSA INTERNATIONAL Mulia Business Park‚ JL. MT. Haryono Kav. 58-60 Pancoran- South Jakarta Page | 1 CONTENT I. Introduction 1.1 Back Ground of Study 1.2 Problem 1.3 Research Problem 1.4 Research Objective 1.5 Scope and Limitation 1.6 Significant of Study II. Literature Review
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.2.3 Time series models Time series is an ordered sequence of values of a variable at equally spaced time intervals. Time series occur frequently when looking at industrial data. The essential difference between modeling data via time series methods and the other methods is that Time series analysis accounts for the fact that data points taken over time may have an internal structure such as autocorrelation‚ trend or seasonal variation that should be accounted for. A Time-series model explains
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E cient neighbor searching in nonlinear time series analysis Thomas Schreiber Department of Theoretical Physics‚ University of Wuppertal‚ D{42097 Wuppertal July 18‚ 1996 We want to encourage the use of fast algorithms to nd nearest neighbors in k{dimensional space. We review methods which are particularly useful for the study of time series data from chaotic systems. As an example‚ a simple box{assisted method and possible re nements are described in some detail. The e ciency of the method is compared
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part you thoroughly motivate your interest in the time series you are about to analyze. You should argue why it is of interest and importance to model your data series. You also briefly report what you do in your project and what results and conclusions you reach. 3. Data. In this section you describe where and how you got the data. Carefully describe all data characteristics‚ length of your time series‚ and frequency. Make a graph of your data series; you could also make a table with summary statistics
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TIME SERIES ANALYSIS Introduction Economic and business time series analysis is a major field of research and application. This analysis method has been used for economic forecasting‚ sales forecasting‚ stock market analysis and company internal control. In this paper‚ we will talk about time series and review techniques that are useful for analyzing time series data. Definition of Time Series and Time Series Analysis Time series is an ordered sequence of values of a variable at equally spaced
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TIME SERIES ANALYSIS Chapter Three Univariate Time Series Models Chapter Three Univariate time series models c WISE 1 3.1 Preliminaries We denote the univariate time series of interest as yt. • yt is observed for t = 1‚ 2‚ . . . ‚ T ; • y0‚ y−1‚ . . . ‚ y1−p are available; • Ωt−1 the history or information set at time t − 1. Call such a sequence of random variables a time series. Chapter Three Univariate time series models c WISE 2 Martingales Let {yt} denote
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and Google Inc. (GOOG) stock is currently trading on the Nasdaq at a price of $1‚105.00 US dollars. Using the information provided below‚ please answer the following questions: (Note: ’Last’ means the last traded price of the put or call option. Use this number for your calculations). Call options: Put options: a) Based on the current stock price‚ which one of the two options is in the money? by how much? (1 marks) b) Assume an investor would like to gain exposure to 1000 shares of Google
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Analysis of Financial Time Series Third Edition RUEY S. TSAY The University of Chicago Booth School of Business Chicago‚ IL A JOHN WILEY & SONS‚ INC.‚ PUBLICATION Analysis of Financial Time Series WILEY SERIES IN PROBABILITY AND STATISTICS Established by WALTER A. SHEWHART and SAMUEL S. WILKS Editors: David J. Balding‚ Noel A. C. Cressie‚ Garrett M. Fitzmaurice‚ Iain M. Johnstone‚ Geert Molenberghs‚ David W. Scott‚ Adrian F. M. Smith‚ Ruey S. Tsay‚ Sanford Weisberg Editors Emeriti:
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Course Outline for Spring 2012‚ Statistics 153: Introduction to Time Series January 16‚ 2012 • Instructor: Aditya Guntuboyina (aditya@stat.berkeley.edu) • Lectures: 12:30 pm to 2 pm on Tuesdays and Thursdays at 160 Dwinelle Hall. • Office Hours: 10 am to 11 am on Tuesdays and Thursdays at 423 Evans Hall. • GSI: Brianna Heggeseth (bhirst@stat.berkeley.edu) • GSI Lab Section: 10 am to 12 pm OR 12 pm to 2 pm on Fridays at 334 Evans Hall (The first section will include a short Introduction
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