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Forecasting trends with asset prices  [PDF]
Ahmed Bel Hadj Ayed,Grégoire Loeper,Frédéric Abergel
Quantitative Finance , 2015,
Abstract: In this paper, we consider a stochastic asset price model where the trend is an unobservable Ornstein Uhlenbeck process. We first review some classical results from Kalman filtering. Expectedly, the choice of the parameters is crucial to put it into practice. For this purpose, we obtain the likelihood in closed form, and provide two on-line computations of this function. Then, we investigate the asymptotic behaviour of statistical estimators. Finally, we quantify the effect of a bad calibration with the continuous time mis-specified Kalman filter. Numerical examples illustrate the difficulty of trend forecasting in financial time series.
Oil Prices and Financial Markets Activity: Empirical Evidence from Some MENA countries  [cached]
Marwan Al-Nahleh,Khaled Al-Zaubi
International Business Research , 2011, DOI: 10.5539/ibr.v4n2p193
Abstract: This study assesses empirically the effects of oil prices on financial markets activity of some MENA countries (Middle East & North Africa).We have chosen this subject to study aiming to find out and explain if there is a relationship between international oil prices and the prices of the listed securities in the financial markets of Middle East and North Africa. The countries that will be in the sample of analysis are Turkey, Jordan, Egypt, Morocco, Tunis, we targeted these countries of this geographical area based on specific characteristics of these countries as they are oil importers; in the meantime they have sharing borders with big oil exporting countries.
ARIMA Model for Gold Bullion Coin Selling Prices Forecasting  [cached]
Lazim Abdullah
International Journal of Advances in Applied Sciences , 2012, DOI: 10.11591/ijaas.v1i4.1495
Abstract: Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial market forecasting over the past three decades but not very often used to forecast gold prices. This paper attempts to address the forecasting of gold bullion coin selling prices. The forecasting models ARIMAs are applied to forecast the gold bullion coin prices. The result suggests that ARIMA (2, 1, 2) is the most suitable model to be used for forecasting gold bullion coin prices. Closer examination suggests that the gold bullion coin selling prices are in upward trends and could be considered as a worthy investment.
Ripple Effect of Housing Prices Fluctuations among Nine Cities of China  [cached]
Fei-xue HUANG,Yun ZHOU,Cheng LI
Management Science and Engineering , 2010,
Abstract: This paper applies cointegration test, error correction model, vector error correction model, impulse response analysis and variance decomposition to examine the ripple effect of housing prices fluctuations among Chinese cities during the period of first quarter of 1999 to third quarter of 2008. Empirical analysis indicates that housing prices fluctuations among nine Chinese cities do have ripple effect. We divide the cities into three layers: Beijing, Shanghai as the first layer; Shenyang, Tianjin and Xi’an as the second; and Qingdao, Chongqing, Guangzhou and Dalian as the third one. Empirical results show that: (1) housing prices of municipalities directly under the central government such as Beijing and Shanghai representing the first layer have strong influence and still be the main regulatory objects; (2) cities in the second layer can transmit the fluctuations of housing prices and should be concerned; (3) intense fluctuations of housing prices of cities in the third layer should be avoided. So, the government should make targeted regulatory policies to cities in different layers, which is a more efficient way to control the whole system of housing prices, maintain housing prices in a reasonable range, and eventually achieve the goal of building a harmonious society.Key Words: housing prices; ripple effect; cointegration test; error correction model; vector error correction model
Modeling and forecasting electricity spot prices: A functional data perspective  [PDF]
Dominik Liebl
Statistics , 2013, DOI: 10.1214/13-AOAS652
Abstract: Classical time series models have serious difficulties in modeling and forecasting the enormous fluctuations of electricity spot prices. Markov regime switch models belong to the most often used models in the electricity literature. These models try to capture the fluctuations of electricity spot prices by using different regimes, each with its own mean and covariance structure. Usually one regime is dedicated to moderate prices and another is dedicated to high prices. However, these models show poor performance and there is no theoretical justification for this kind of classification. The merit order model, the most important micro-economic pricing model for electricity spot prices, however, suggests a continuum of mean levels with a functional dependence on electricity demand. We propose a new statistical perspective on modeling and forecasting electricity spot prices that accounts for the merit order model. In a first step, the functional relation between electricity spot prices and electricity demand is modeled by daily price-demand functions. In a second step, we parameterize the series of daily price-demand functions using a functional factor model. The power of this new perspective is demonstrated by a forecast study that compares our functional factor model with two established classical time series models as well as two alternative functional data models.
Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches  [PDF]
Abdul Aziz Karia, Imbarine Bujang, Ismail Ahmad
American Journal of Operations Research (AJOR) , 2013, DOI: 10.4236/ajor.2013.32023

An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches in predicting the CPO prices is becoming the matter into concerns. In this study, two artificial intelligence approaches, has been used namely artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). We employed in-sample forecasting on daily free-on-board CPO prices in Malaysia and the series data stretching from a period of January first, 2004 to the end of December 2011. The predictability power of the artificial intelligence approaches was also made in regard with the statistical forecasting approach such as the autoregressive fractionally integrated moving average (ARFIMA) model. The general findings demonstrated that the ANN model is superior compared to the ANFIS and ARFIMA models in predicting the CPO prices.

Electric Power Demand Forecasting of KAVAL Cities
A.K. Bhardwaj,R.C. Bansal,R.K. Saket,A.K. Srivastava
International Journal of Electrical and Power Engineering , 2012, DOI: 10.3923/ijepe.2010.85.89
Abstract: Power load forecasting is having its own importance in a bulk interconnected combined generation, transmission and distribution power system. Accurately load forecasting is important to establish operational plans for power stations and their generation units, implementation of improved plant scheduling. It further provides a reliable and credible resource that ensures stable and economical operation of the power. The crucial issue of managing the demand is of a great importance in formulating the future development policy for the whole country. The case studies analyses the requirement of electricity with respect to the future population for the major forms of energy in the KAVAL cities of Uttar Pradesh state in India. A model consisting of significant key energy indicators have been used for the estimation. Model wherever required refined in the second stage to remove the effect of auto-correlation. The accuracy of the model has been checked using standard statistical techniques and validated against the past data by testing for expost forecast accuracy. The study identifies the urgent need for special attention in evolving effective energy policies to alleviate an energy famine in the near future.
Structural Breaks, Automatic Model Selection and Forecasting Wheat and Rice Prices for Pakistan
Zahid Asghar,Amena Urooj
Pakistan Journal of Statistics and Operation Research , 2012, DOI: 10.1234/pjsor.v8i1.332
Abstract: Structural breaks and existence of outliers in time series variables results in misleading forecasts. We forecast wheat and rice prices by capturing the exogenous breaks and outliers using Automatic modeling. The procedure identifies the outliers as the observations with large residuals. The suggested model is compared on the basis of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) with the usual ARIMA model selected ignoring the possible breaks. Our results strongly support that forecasting with breaks by using General to Specific (Gets through Autometric) model performs better in forecasting than that of traditional model. We have used wheat and rice price data (two main staple foods) for Pakistan.
Forecasting Energy Commodity Prices Using Neural Networks  [PDF]
Massimo Panella,Francesco Barcellona,Rita L. D'Ecclesia
Advances in Decision Sciences , 2012, DOI: 10.1155/2012/289810
Abstract: A new machine learning approach for price modeling is proposed. The use of neural networks as an advanced signal processing tool may be successfully used to model and forecast energy commodity prices, such as crude oil, coal, natural gas, and electricity prices. Energy commodities have shown explosive growth in the last decade. They have become a new asset class used also for investment purposes. This creates a huge demand for better modeling as what occurred in the stock markets in the 1970s. Their price behavior presents unique features causing complex dynamics whose prediction is regarded as a challenging task. The use of a Mixture of Gaussian neural network may provide significant improvements with respect to other well-known models. We propose a computationally efficient learning of this neural network using the maximum likelihood estimation approach to calibrate the parameters. The optimal model is identified using a hierarchical constructive procedure that progressively increases the model complexity. Extensive computer simulations validate the proposed approach and provide an accurate description of commodities prices dynamics. 1. Introduction Energy is a principal factor of production in all aspects of every economy. Energy price dynamics are affected by complex risk factors, such as political events, extreme weather conditions, and financial market behavior. Crude oil is a key and highly transportable component for the economic development and growth of industrialized and developing countries, where it is refined into the many petroleum products we consume. Over the last 10 years, the global demand for crude oil and gas has increased largely due to the rapidly increasing demands of non-OECD countries, especially China [1]. Local gas and coal are mainly used in the electricity generation process and recently their supply and demand experienced a profound transformation. The economic exploitation at higher prices of non-conventional forms of oil and gas, such as shale gas and shale oil, is modifying the demand for the three fossil fuels. The production of shale gas in the US will shortly bring the US to be less dependent on imported oil and, in addition, it means a large part of the electricity generation process has been switched from coal to gas. The deregulation of gas and electricity markets makes the prices of these commodities to be formed in competitive markets. Crude oil and natural gas in the last decade have been largely traded on spot, derivative, and forward markets by producers, consumers, and investors. Crude oil and gas are
Building a House Prices Forecasting Model in Hong Kong  [cached]
Xin Janet,Ka-Chi Lam
Australasian Journal of Construction Economics and Building , 2012,
Abstract: This paper builds a house prices forecasting model for private residential houses in HongKong, based on general macroeconomic indicators, housing related data and demographicfactors for the period of 1980 to 2001. A reduce form economic model has been derivedfrom a multiple regression analysis where three sets and eight models were derived foranalysis and comparison. It is found that household income, land supply, population andmovements in the Hang Seng Index play an important role in explaining house pricemovements in Hong Kong. In addition, political events, as identified, cannot be ignored.However, the results of the models are unstable. It is suggested that the OLS may nota best method for house prices model in Hong Kong situation. Alternative methods aresuggested.
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