An Introduction to univariate financial time series analysis 1 Introduction: what is a time-series? Time-series is a sequence {x1‚ x2‚ ...‚ xT } or {xt} ‚ t = 1‚ ...‚ T‚ where t is an index denoting the period in time in which x occurs. We shall treat xt as a random variable; hence‚ a time-series is a sequence of random variables ordered in time. Such a sequence is known as a stochastic process. The probability structure of a sequence of random variables is determined by the joint distribution
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Univariate Time Series Models (M.Sc. Finance - Exercise 4) Walter Distaso Imperial College Business School w.distaso@imperial.ac.uk Question 1 Consider the following three models that a researcher suggests might be reasonable models of stock market prices. yt yt yt = yt−1 + ut = 0.5yt−1 + ut = 0.8yt−1 + ut (a) What classes of models are these examples of? (b) What would the autocorrelation function for each of these processes look like? (not exactly‚ just the shape) (c) Which model is
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Forecast of Remittance in Bangladesh A Time Series Forecast 8/11/2012 North South University Prepared by: Athena Rahmetullah Leonora Adhikari Nudrat Faria Shreya Sumaita Maisha Tajkia Mahmud I. INTRODUCTION Remittances are funds transferred from migrants to their home country. They are the private savings of workers and families that are spent in the home country for food‚ clothing and other expenditures‚ and which drive the home economy. Remittance inflows in the economy of Bangladesh
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(2003) 307 – 319 www.elsevier.com/locate/neucom Financial time series forecasting using support vector machines Kyoung-jae Kim∗ Department of Information Systems‚ College of Business Administration‚ Dongguk University‚ 3-26‚ Pil-dong‚ Chung-gu‚ Seoul 100715‚ South Korea Received 28 February 2002; accepted 13 March 2003 Abstract Support vector machines (SVMs) are promising methods for the prediction of ÿnancial timeseries because they use a risk function consisting of the empirical error and a
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data set comprising the put‚ call and strike prices of a stock belonging to a company listed on a known index. 2. To use the BSM Model to which provides a mathematical science for the pricing and hedging of European Call and Put options as the American Options market 3. We wanted to analyze the data for Google option prices from the S&P index over the past and present time periods in order to be able to forecast the future. Literature Review 1. Put call parity In financial mathematics
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Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number‚ set of numbers‚ or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself‚
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Time Series Models for Forecasting New One-Family Houses Sold in the United States Introduction The economic recession felt in the United States since the collapse of the housing market in 2007 can be seen by various trends in the housing market. This collapse claimed some of the largest financial institutions in the U.S. such as Bear Sterns and Lehman Brothers‚ as they held over-leveraged positions in the mortgage backed securities market. Credit became widely available to unqualified borrowers
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The ABC series of Complementary Medicine gives insight to who uses complementary medicine‚ and why someone turns to complementary medicine. As well as the risks and concerns that comes with practicing a less researched‚ less practiced form of treatment. According to ABC series of Complementary Medicine “nutrition” and “science” “has always been a part in conventional medicine‚ however traditional practitioners are not taught‚ and therefore do not practice much in the way of nutritional therapeutics
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Determinants of Gross Domestic Saving in Ethiopia: a time series analysis Kidane Badeg Contact: Kidane Badeg MoFED P.o.Box 1905‚ Addis Ababa ‚ Ethiopia Email: kbadeg@mofed.gov.et Abstract The study conduct a time series analysis of the determinants of gross domestic saving in Ethiopia using co-integration and error correction econometric modeling(ECM)‚ and employed data for the period of 1971-2009 collected from National bank of Ethiopia (NBE)‚ MOFED‚ CSA and world bank on annual base. The
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SECTION A (You should attempt all 10 questions) A1. Regression analysis is ____________________________________. A) describes the strength of this linear relationship. B) describes the mathematical relationship between two variables. C) describes the pattern of the data. D) describes the characteristic of independent variable. A2. __________________ is used to illustrate any relationship between two variables. A) Histogram B) Pie chart C) Scatter diagram D) Frequency
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