and some of its bidding areas [3]. A. Purpose The aim of this paper is to study the Elspot price dynamics in Sweden and its interactions with relative variables (wind power, hydropower, gas and crude oil). A seasonal autoregressive integrated moving average model with exogenous variables, SARIMAX model, shall be developed to describe meaningful characteristics of price dynamics and predict the next-day price based on historical
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UNIT 11 AUTOREGRESSIVE AND DISTRIBUTED LAG MODELS - - - Structure 1 1.0 Objectives 1 1.1 Introduction 1 1.2 Models With Lags 1 1.2.1 1 1.2.2 1 1.2.3 1 1.2.4 1 1.2.5 Distributed Lag Models The Koyck Model Dynamic or Autoregressive Models A More General Dynamic Model Jorgenson's Rational Lag Model 1 1.3 Economic Theory and Models with Lags 1 1.3.1 The Partial Adjustment Model 1 1.3.2 The Adaptive Expectations Model 1 1.4 Interpretation of Coefficients 1 1.5 Estimation and Inference
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International Journal of Banking and Finance Volume 9 | Issue 1 Article 3 6-5-2012 Modelling and forecasting volatility in the gold market Stefan Trück Macquarie University, stefan.trueck@mq.edu.au Kevin Liang Macquarie University Follow this and additional works at: http://epublications.bond.edu.au/ijbf Recommended Citation Trück, Stefan and Liang, Kevin (2012) "Modelling and forecasting volatility in the gold market," International Journal of Banking and Finance: Vol. 9: Iss. 1
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Information Sharing in Supply Chains: An Empirical and Theoretical Valuation Ruomeng Cui, Gad Allon, Achal Bassamboo, Jan A. Van Mieghem* Kellogg School of Management, Northwestern University, Evanston, IL April 10, 2013 We provide an empirical and theoretical assessment of the value of information sharing in a two-stage supply chain. The value of downstream sales information to the upstream firm stems from improving upstream order fulfillment forecast accuracy. Such improvement can lead to lower
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Hedge Fund Creation and Management Project Technical Strategy on Momentum Stocks Introduction to Fund Philosophy Carhart’s Four Factor Model Our fund’s main strategy was based on exploiting value from momentum that can be uncovered in securities with positive momentum. We used the Cahart four factor model, based on the fama french model that assumes efficient market theories, to find out if an underlying securities carries the power of momentum. In the fama-french model, the return
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LECTURE 7 Forecasting with ARMA Models Minimum Mean-Square Error Prediction Imagine that y(t) is a stationary stochastic process with E{y(t)} = 0. We may be interested in predicting values of this process several periods into the future on the basis of its observed history. This history is contained in the so-called information set. In practice, the latter is always a finite set {yt , yt−1 , . . . , yt−p } representing the recent past. Nevertheless, in developing the theory of prediction, it
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ARCH An ARCH (autoregressive conditionally heteroscedastic) model is a model for the variance of a time series. Given the apparent lack of any structural dynamic economic theory explaining the variation in higher order moments, particularly instrumental in this development has been the autoregressive conditional heteroskedastic (ARCH) class of models introduced by Engle (1982). The ARCH model and its various extensions have proven very effective tools along these lines. In truth, by any yardstick
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the initial months, demand is expected to grow at a roughly linear rate. Demand is then expected to stabilize. Eventually, demand should begin to decline at a roughly linear rate. Although orders arrive randomly to LT, management expects that, on average, demand will follow the trends outlined above. Management’s main concern is managing the capacity of the factory in response to the complex demand pattern. Delays resulting from insufficient capacity undermine LT’s promised lead times and ultimately
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future expected class sizes, since we have past data on class sizes. We shall also assume that the past pattern will continue in the future and therefore use the time series forecasting method. Year | Quarter | Time period | Pax | Moving Total | Moving Average | Seasonal Index (Actual/ MA) | 2003 | Jan - Mar | 1 | 35 | | | | | Apr - Jun | 2 | 44 | | | | | Jul - Sep | 3 | 54 | | | | | Oct - Dec | 4 | 49 | | | | 2004 | Jan - Mar | 5 | 68 | 182 | 45.50 | 1.49 | | Apr
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In the world of technology analysis, moving average is one of the most popular and widely used indicators. What started with the simple moving average and then towards exponential moving average has with the passage of time and advent of computer programmed software's have made technicians to experiment and come up with new types of data calculation [2]. Mean Reversion is a theory suggesting that prices and returns eventually move back towards the mean or average under the above assumption. A
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