Forecasting Ships Prices and Their Influence on Maritime Insurance Market
|
|
- Bruno Booker
- 6 years ago
- Views:
Transcription
1 Forecasting Ships Prices and Their Influence on Maritime Insurance Market Ghiorghe Bătrînca Constanta Maritime University, Constanta, Romania Ana-Maria Burcă Academy of Economic Studies, Bucharest, Romania Abstract The shipping industry has been growing rapidly from year to year and until not too long ago, shipping was both the greatest beneficiary and hammering pulse of globalization. But now the global economic and financial crisis has stifled the boom of this industry. Since the global economic and financial crisis began in 2009, the problems the shipping industry faces have multiplied, generating a high volatility of prices. With the global expansion of the maritime sector, marine insurance is on the forefront nowadays, more than ever before. As the marine insurance premiums vary according to the value of insured assets and their number, the marine insurance market can be examined through the forecast of ships price in the context of deteriorating economic conditions. Keywords: ships price, marine insurance, ARIMA models, forecasting. Introduction Shipping market has passed through one of its most interesting periods in history between 2003 and It all started with significant increase in demand for raw materials in China coupled with low level of investment in new ships as a consequence of low productivity during 1990 s. Market participants were surprised to see such a significant increase in freight rates and many of them considered that this freight levels will not be sustainable for longer term. Few of the owners realized that the imbalance between supply and demand is rapidly growing and they ordered new ships at significantly higher prices than those available in the prior to More and more owners decided to order ships and soon shipyards run out of capacity and during 2007 and early 2008 it was almost impossible to find available space for building new ships before As a consequence of high freight rates, limited capacity of shipyards and significant increase of steel plates price the prices for ships reached unexpected levels. Market continued on a positive note till second half of 2008 when it was obvious that crisis affecting the banking sector would have a significant impact on demand for shipping services and during six months ships value decreased by more than 50 percent. This was somehow coupled with fears that as from 2010 supply will overpass demand and a long period of low freight level would be expected. Increase of ship prices had also a significant impact on industries supporting maritime industry, like banking and insurance, increasing lenders and insurers exposure to loss of a single asset. This paper will present the use of autoregressive integrated moving average (ARIMA) models for forecasting ships prices and will discuss about ships prices influence on maritime insurance market.
2 Literature review The advent of the computer popularized the use of autoregressive integrated moving average (ARIMA) models in many areas of science. But often, the research was of an empirical nature, using benchmark models as a comparison. The list of examples of real applications of ARIMA models include: electricity load by Di Caprio, Genesio, Pozzi and Vicino (1983), automobile insurance by Cummins and Griepentrog (1985), federal funds rate by Hein and Spudeck (1988), macroeconomic data by Dhrymes and Peristiani (1988), department store sales by Geurts and Kelly (1986,1990), Pack (1990), demand for telephone services by Grambsch and Stahel (1990), total population by Pflaumer (1992), tourism demand by Du Preez and Witt (2003) and so on. The existing researches on the use of ARIMA models in shipping industry are scarce. Container trade is of vital importance to liner shipping, waterfront activities and container port development. As container trade drives over fifty per cent by value of Australia s seaborne trade, Amoako (2002) carried out an analysis that provides an overview of container trade at national level and its future trends. The author generated forecasts of future levels of container quantities by using two different methods: dynamic econometric modeling and multivariate autoregressive modeling. The research was conceived with data that exclude double handling, because figures that include double handling or trans-shipment may invalidate growth forecasts. According to the final results, the proportion of goods traded internationally in containers is expected to increase. Nevertheless, the ARIMA model is a good predictor in the short term. Khan et al. (2004) analyzed the application of the autoregressive moving average method and the artificial neural network methods for the prediction of ship motion. An algorithm capable of predicting the motion of a ship is required for the successful deployment of a ship system currently used on ships that operate in open sea environments. The authors show that the artificial neural network is superior to autoregressive moving average techniques and is able to predict the ship motion satisfactorily for up to 10 seconds. They also try to combine multiple time series prediction techniques in order to obtain better overall results than the ones generated by individual techniques. Dashan and Apaydin (2012) investigated the waste amount from ships in Istanbul. The authors succeeded to forecast the amount of different waste collected from transit ships for next two years based on the data recorded between September 2005 and January 2010 by applying ARMA forecasting model. According to their results, the collected amount of waste oil, bilge water, sludge and garbage will increase, while those for ballast and slop will decrease. Overall, the current data remain between the upper and the lower limit values of forecasting data. Data and methodology In this study, the ARIMA models were applied in order to capture and examine the dynamics of the ship prices. For the empirical study, the monthly data series of Capesize ships price in real value for the period September 2003 April 2013 were used. The Capesize ships price was chosen because during the analyzed period it has recorded the highest volatility. Data were collected from Baltic Exchange database and the ARIMA models were built with EViews 7. The ARIMA model is widely used in the field of forecasting and there are a large number of variations of the model proposed in various literatures over the years (Stoica et al.,1999).
3 The Box-Jenkin s (1976) ARIMA modelling procedure considers the time-dependent nature of data to produce efficient estimation of a statistical model which can be interpreted as having generated the sample data. ARIMA specifically models dependent variables as a function of itself lagged from previous periods, i.e. autoregression, and random errors lagged from previous periods, i.e. moving-average. The ARIMA model is a generalization of the autoregressive and the moving average models. The autoregressive (AR) model uses past values of the dependent variable to explain the current value whereas, the moving average (MA) model uses lagged values of the error term to explain the current value of the explanatory variable. The general ARIMA model is called an ARIMA(p,d,q), with p being the number of lags of the dependent variable (the AR terms), d being the number of differences required to take in order to make the series stationary, and q being the number of lagged terms of the error term (the MA terms). An ARIMA(p,d,q) (AutoRegressive Integrated Moving Average with orders p,d,q) model is a discrete time linear equations with noise, of the form: p q k d k 1 α k L ( 1 L) X t = 1 + β k L ε t k = 1 k = 1 Taking in consideration the fact that the time series consist of monthly data, the testing of seasonality becomes imperious. Figure 1 indicates that the seasonality phenomenon is not relevant for the time series considered. 160 SHIP_PRICE by Season Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Means by Season Figure 1. Seasonal graph of ships price Firstly, the ADF test (Augmented Dickey-Fuller) was applied in order to verify the stationarity of time series. A time series is said to be stationary if its mean, variance and its covariances remain constant over time. From an economic point of view, shocks to a stationary time series are temporary and, over time, the effects of the shocks will dissipate. The existence of a unit root was estimated for the original data and the absence of a unit root for the first-difference logarithmic data (Table 1). Usually, the econometric analysis is performed with logarithmic series because it facilitates the interpretation of regression coefficients. Therefore, the variables are integrated of order 1 and denoted by l(1).
4 Table 1. The ADF test for first-difference logarithmic data If the probability is lower than the significance level (1%, 5% and 10%), the null hypothesis is rejected. It can be observed that the first-difference logarithmic data is stationary. Box and Jenkins first introduced ARIMA models in 1976, the term deriving from AR autoregressive, I integrated and MA moving average. Box and Jenkins designed a three-stage method which can be applied in order to estimate and select an appropriate ARIMA model. In the identification stage, the form of the model has to be discovered, because any model may be given more than one different representations. Once the time series stationarity is achieved, the next step is to identify the p and q orders of the ARIMA model. Therefore, the time plot of the series autocorrelation function (ACF) and partial correlation function (PACF) will be visually examined, because they offer access to useful information concerning outliers, missing values and structural breaks in the data (Table 2). Table 2. Autocorrelation function and partial correlation function From the table above, it can be observed that there are two significant spikes on the time plot of the series autocorrelation function (ACF), and then all are zero, while there are also two significant spikes in the partial correlation function (PACF). This suggests that the models might have up to MA(2) and AR(2) specifications. Thus, the possible models are the ARIMA(1,1,1), ARIMA(1,1,2), ARIMA(2,1,1) or ARIMA(2,1,2) models. According to Box and Jenkins, a valid model should be stationary and invertible. Thus, the modulus of each AR coefficient has to be lower than 1, the sum of AR coefficients has to be lower than 1 and the modulus of each root has to be lower than 1. These requirements are fulfilled by all the models that were identified in the previous stage.
5 In the estimation stage, each of the possible models is estimated and various coefficients are analyzed. The estimated models are compared using the Akaike information criterion (AIC), the Schwartz Bayesian criterion (SBC) and Adjusted R-squared (Table 3). The model that minimizes AIC and SBC and has the highest Adjusted R-squared will be chosen. Table 3. Summary results of possible ARIMA models Model ARIMA(1,1,1) ARIMA(1,1,2) ARIMA(2,1,1) ARIMA(2,1,2) AIC SBC Adjusted R- squared According to Table 3, in terms of AIC and SBC, contradictory results were obtained: AIC suggests the ARIMA(2,1,2) model, but SBC suggests the ARIMA(1,1,1) model. But the ARIMA(2,1,2) model has the highest Adjusted R-squared, suggesting that this model is probably the most appropriate one. Therefore, the ARIMA(2,1,2) model is estimated in Table 4 and its validity is tested in Table 5. It can be noted that the model is stationary and invertible. Furthermore, R-squared and Adjusted R-squared tests are higher than 50%. Table 4. Estimation of ARIMA(2,1,2) model
6 Table 5. ARIMA(2,1,2) structure The last stage requires the examination of the goodness of fit of the model. Therefore, the statistical significance of model s coefficients, autocorrelation of residuals, homoskedasticity and the absence of additional ARCH terms will be tested. As it can be observed from Table 4, all the coefficients are statistically significant (the probabilities are lower than the significance level of 5% and 10%). Regarding the quality of residuals, the best view to look at first is Actual, Fitted, Residual Graph (Figure 2). It can be noted that the fit is quite good and the fitted values nearly cover up the actual values. The estimated model fits better in the later part than in the earlier years due to the fact that the residuals become smaller in absolute value Residual Actual Fitted Figure 2. Actual, Fitted, Residual Graph According to the correlogram of residuals (Table 6), there is no serial correlation of error terms (the null hypothesis is accepted because the probabilities of Q-stat are higher than the significance level).
7 Table 6. Correlogram of residuals According to F-statistic and Obs*R-squared tests which are higher than the 10% significance level (Table 7), the null hypothesis of absence of serial correlation of squared errors is accepted. Thus, there are no additional ARCH terms and the presence of homoskedasticity is accepted. Table 7. Heteroskedasticity ARCH test Taking in consideration the results of the tests applied to the estimated model, it can be concluded that the ARIMA(2,1,2) model is appropriate. By using this model, the Capesize ships price will be forecasted for the period May 2013 December Figure 3 illustrates the dynamic forecast of Capesize ships price and its error margins.
8 II III IV I II III IV I II III IV I II III IV SHIP_PRICEF2 ± 2 S.E. Figure 3. Dynamic forecast of Capesize ships price Figure 4 illustrates the fluctuation of Capesize ships price during September 2003 December SHIP_PRICEF Figure 4.The evolution of Capesize ships price The end users of this study can be represented by the specialists in the maritime industry and in the industries supporting the maritime industry, namely insurance and banking, who are directly or indirectly affected and concerned about ships price fluctuation. Conclusions As it can be observed from Figure 3, the Capesize ships price will record a continuously slight increase during May 2013 December Today s ship prices for Capesize vessels are at their historic minimum and once the gap between supply and demand will be reduced, prices will start moving up. One other aspect that has to be taken into consideration when looking at this graph is related to the fact data used in this study refer to prices for 5 years old ships which had a very unusual movement during 2007 and 2008 when they were over 30% higher than prices for new buildings. While this can be easily explained by the fact that five years old ships were able to trade immediately in an extraordinary high
9 market, while new ships were expected to be delivered in at least 24 months when nobody could have predicted the market level. As the marine insurance premiums vary according to the value of insured assets and their number, their evolution during May 2013 December 2016 can be examined through the forecast of ships price. As the value of the ships will start increasing again, it can be expected the exposure to loss of a single ship to increase. The oversupply of ships will probably drive out of the market old ships and sometime substandard ships, which is expected to have a positive influence on the insurance market profitability. References Amoako, J. (2002) Forecasting Australia s International container trade, 25 th Australasian Transport Research Forum. Baltic Exchange website Cummins, J. D. and Griepentrog, G. L. (1985) Forecasting automobile insurance paid claims using econometric and ARIMA models, International Journal of Forecasting, 1, Dashan, E.S. and Apaydin, O. (2013) An Investigation On Waste Amount From Ships in Istanbul, Global NEST Journal, Volume 15, No 1, De Gooijer, J. G. and Hyndman, R.J. (2006) 25 years of time series forecasting, International Journal of Forecasting, 22, Dhrymes, P. J. and Peristiani, S. C. (1988) A comparison of the forecasting performance of WEFA and ARIMA time series methods, International Journal of Forecasting, 4, Di Caprio, U., Genesio, R., Pozzi, S. and Vicino, A. (1983) Short term load forecasting in electric power systems: A comparison of ARMA models and extended Wiener filtering, Journal of Forecasting, 2, Du Preez, J. and Witt, S. F. (2003) Univariate versus multivariate time series forecasting: An application to international tourism demand, International Journal of Forecasting, 19, Geurts, M. D. and Kelly, J. P. (1986) Forecasting retail sales using alternative models, International Journal of Forecasting, 2, Geurts, M. D. and Kelly, J. P. (1990) Comments on: In defense of ARIMA modeling by D.J. Pack, International Journal of Forecasting, 6, Grambsch, P. and Stahel, W. A. (1990) Forecasting demand for special telephone services: A case study, International Journal of Forecasting, 6, Hein, S. and Spudeck, R. E. (1988) Forecasting the daily federal funds rate, International Journal of Forecasting, 4, Khan, A., Cees, B., Kaye, M. and Crozier, M. (2004) Real Time Prediction Of Ship Motions and Attitudes Using Advanced Prediction Techniques, 24th International Congress Of The Aeronautical Sciences.
10 Pack, D. J. (1990) Rejoinder to: Comments on: In defense of ARIMA modeling by M.D. Geurts and J.P. Kelly, International Journal of Forecasting, 6, Pflaumer, P. (1992) Forecasting US population totals with the Box Jenkins approach, International Journal of Forecasting, 8, Stoica, P., Mari, J. and McKelvey, T. (1999) Vector ARMA estimation: an enhanced subspace approach, 38th IEEE Conference on Decision and Control, 4, Studenmund, A. H. (2006) Using Econometrics - a practical guide, fifth edition, Pearson: Occidental College.
Suan Sunandha Rajabhat University
Forecasting Exchange Rate between Thai Baht and the US Dollar Using Time Series Analysis Kunya Bowornchockchai Suan Sunandha Rajabhat University INTRODUCTION The objective of this research is to forecast
More informationTIME SERIES ANALYSIS AND FORECASTING USING THE STATISTICAL MODEL ARIMA
CHAPTER 6 TIME SERIES ANALYSIS AND FORECASTING USING THE STATISTICAL MODEL ARIMA 6.1. Introduction A time series is a sequence of observations ordered in time. A basic assumption in the time series analysis
More informationRomanian Economic and Business Review Vol. 3, No. 3 THE EVOLUTION OF SNP PETROM STOCK LIST - STUDY THROUGH AUTOREGRESSIVE MODELS
THE EVOLUTION OF SNP PETROM STOCK LIST - STUDY THROUGH AUTOREGRESSIVE MODELS Marian Zaharia, Ioana Zaheu, and Elena Roxana Stan Abstract Stock exchange market is one of the most dynamic and unpredictable
More informationAsitha Kodippili. Deepthika Senaratne. Department of Mathematics and Computer Science,Fayetteville State University, USA.
Forecasting Tourist Arrivals to Sri Lanka Using Seasonal ARIMA Asitha Kodippili Department of Mathematics and Computer Science,Fayetteville State University, USA. akodippili@uncfsu.edu Deepthika Senaratne
More informationAutoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia
DOI 10.1515/ptse-2017-0005 PTSE 12 (1): 43-50 Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia Umi MAHMUDAH u_mudah@yahoo.com (State Islamic University of Pekalongan,
More informationAustrian Inflation Rate
Austrian Inflation Rate Course of Econometric Forecasting Nadir Shahzad Virkun Tomas Sedliacik Goal and Data Selection Our goal is to find a relatively accurate procedure in order to forecast the Austrian
More informationOil price volatility in the Philippines using generalized autoregressive conditional heteroscedasticity
Oil price volatility in the Philippines using generalized autoregressive conditional heteroscedasticity Carl Ceasar F. Talungon University of Southern Mindanao, Cotabato Province, Philippines Email: carlceasar04@gmail.com
More information= observed volume on day l for bin j = base volume in jth bin, and = residual error, assumed independent with mean zero.
QB research September 4, 06 Page -Minute Bin Volume Forecast Model Overview In response to strong client demand, Quantitative Brokers (QB) has developed a new algorithm called Closer that specifically
More informationEstimation and application of best ARIMA model for forecasting the uranium price.
Estimation and application of best ARIMA model for forecasting the uranium price. Medeu Amangeldi May 13, 2018 Capstone Project Superviser: Dongming Wei Second reader: Zhenisbek Assylbekov Abstract This
More informationMODELING MAXIMUM MONTHLY TEMPERATURE IN KATUNAYAKE REGION, SRI LANKA: A SARIMA APPROACH
MODELING MAXIMUM MONTHLY TEMPERATURE IN KATUNAYAKE REGION, SRI LANKA: A SARIMA APPROACH M.C.Alibuhtto 1 &P.A.H.R.Ariyarathna 2 1 Department of Mathematical Sciences, Faculty of Applied Sciences, South
More informationEmpirical Market Microstructure Analysis (EMMA)
Empirical Market Microstructure Analysis (EMMA) Lecture 3: Statistical Building Blocks and Econometric Basics Prof. Dr. Michael Stein michael.stein@vwl.uni-freiburg.de Albert-Ludwigs-University of Freiburg
More informationEmpirical Approach to Modelling and Forecasting Inflation in Ghana
Current Research Journal of Economic Theory 4(3): 83-87, 2012 ISSN: 2042-485X Maxwell Scientific Organization, 2012 Submitted: April 13, 2012 Accepted: May 06, 2012 Published: June 30, 2012 Empirical Approach
More informationForecasting the Prices of Indian Natural Rubber using ARIMA Model
Available online at www.ijpab.com Rani and Krishnan Int. J. Pure App. Biosci. 6 (2): 217-221 (2018) ISSN: 2320 7051 DOI: http://dx.doi.org/10.18782/2320-7051.5464 ISSN: 2320 7051 Int. J. Pure App. Biosci.
More informationForecasting the Canadian Dollar Exchange Rate Wissam Saleh & Pablo Navarro
Forecasting the Canadian Dollar Exchange Rate Wissam Saleh & Pablo Navarro Research Question: What variables effect the Canadian/US exchange rate? Do energy prices have an effect on the Canadian/US exchange
More informationForecasting Foreign Direct Investment Inflows into India Using ARIMA Model
Forecasting Foreign Direct Investment Inflows into India Using ARIMA Model Dr.K.Nithya Kala & Aruna.P.Remesh, 1 Assistant Professor, PSGR Krishnammal College for Women, Coimbatore, Tamilnadu, India 2 PhD
More informationShort-run electricity demand forecasts in Maharashtra
Applied Economics, 2002, 34, 1055±1059 Short-run electricity demand forecasts in Maharashtra SAJAL GHO SH* and AN JAN A D AS Indira Gandhi Institute of Development Research, Mumbai, India This paper, has
More informationTime Series and Forecasting
Time Series and Forecasting Introduction to Forecasting n What is forecasting? n Primary Function is to Predict the Future using (time series related or other) data we have in hand n Why are we interested?
More informationTime Series and Forecasting
Time Series and Forecasting Introduction to Forecasting n What is forecasting? n Primary Function is to Predict the Future using (time series related or other) data we have in hand n Why are we interested?
More informationForecasting Bangladesh's Inflation through Econometric Models
American Journal of Economics and Business Administration Original Research Paper Forecasting Bangladesh's Inflation through Econometric Models 1,2 Nazmul Islam 1 Department of Humanities, Bangladesh University
More informationChapter 12: An introduction to Time Series Analysis. Chapter 12: An introduction to Time Series Analysis
Chapter 12: An introduction to Time Series Analysis Introduction In this chapter, we will discuss forecasting with single-series (univariate) Box-Jenkins models. The common name of the models is Auto-Regressive
More informationTime Series Analysis of Currency in Circulation in Nigeria
ISSN -3 (Paper) ISSN 5-091 (Online) Time Series Analysis of Currency in Circulation in Nigeria Omekara C.O Okereke O.E. Ire K.I. Irokwe O. Department of Statistics, Michael Okpara University of Agriculture
More informationAuthor: Yesuf M. Awel 1c. Affiliation: 1 PhD, Economist-Consultant; P.O Box , Addis Ababa, Ethiopia. c.
ISSN: 2415-0304 (Print) ISSN: 2522-2465 (Online) Indexing/Abstracting Forecasting GDP Growth: Application of Autoregressive Integrated Moving Average Model Author: Yesuf M. Awel 1c Affiliation: 1 PhD,
More informationModeling and Forecasting Currency in Circulation in Sri Lanka
Modeling and Forecasting Currency in Circulation in Sri Lanka Rupa Dheerasinghe 1 Abstract Currency in circulation is typically estimated either by specifying a currency demand equation based on the theory
More informationArma-Arch Modeling Of The Returns Of First Bank Of Nigeria
Arma-Arch Modeling Of The Returns Of First Bank Of Nigeria Emmanuel Alphonsus Akpan Imoh Udo Moffat Department of Mathematics and Statistics University of Uyo, Nigeria Ntiedo Bassey Ekpo Department of
More informationThe Evolution of Snp Petrom Stock List - Study Through Autoregressive Models
The Evolution of Snp Petrom Stock List Study Through Autoregressive Models Marian Zaharia Ioana Zaheu Elena Roxana Stan Faculty of Internal and International Economy of Tourism RomanianAmerican University,
More informationFrequency Forecasting using Time Series ARIMA model
Frequency Forecasting using Time Series ARIMA model Manish Kumar Tikariha DGM(O) NSPCL Bhilai Abstract In view of stringent regulatory stance and recent tariff guidelines, Deviation Settlement mechanism
More informationAdvanced Econometrics
Advanced Econometrics Marco Sunder Nov 04 2010 Marco Sunder Advanced Econometrics 1/ 25 Contents 1 2 3 Marco Sunder Advanced Econometrics 2/ 25 Music Marco Sunder Advanced Econometrics 3/ 25 Music Marco
More informationUnivariate linear models
Univariate linear models The specification process of an univariate ARIMA model is based on the theoretical properties of the different processes and it is also important the observation and interpretation
More informationTime Series Analysis of United States of America Crude Oil and Petroleum Products Importations from Saudi Arabia
International Journal of Applied Science and Technology Vol. 5, No. 5; October 2015 Time Series Analysis of United States of America Crude Oil and Petroleum Products Importations from Saudi Arabia Olayan
More informationThe Prediction of Monthly Inflation Rate in Romania 1
Economic Insights Trends and Challenges Vol.III (LXVI) No. 2/2014 75-84 The Prediction of Monthly Inflation Rate in Romania 1 Mihaela Simionescu Institute for Economic Forecasting of the Romanian Academy,
More informationEconometric Forecasting
Robert M. Kunst robert.kunst@univie.ac.at University of Vienna and Institute for Advanced Studies Vienna October 1, 2014 Outline Introduction Model-free extrapolation Univariate time-series models Trend
More informationStochastic Processes
Stochastic Processes Stochastic Process Non Formal Definition: Non formal: A stochastic process (random process) is the opposite of a deterministic process such as one defined by a differential equation.
More informationForecasting Gold Price. A Comparative Study
Course of Financial Econometrics FIRM Forecasting Gold Price A Comparative Study Alessio Azzutti, University of Florence Abstract: This paper seeks to evaluate the appropriateness of a variety of existing
More informationECONOMETRIA II. CURSO 2009/2010 LAB # 3
ECONOMETRIA II. CURSO 2009/2010 LAB # 3 BOX-JENKINS METHODOLOGY The Box Jenkins approach combines the moving average and the autorregresive models. Although both models were already known, the contribution
More informationA Univariate Time Series Autoregressive Integrated Moving Average Model for the Exchange Rate Between Nigerian Naira and US Dollar
American Journal of Theoretical and Applied Statistics 2018; 7(5): 173-179 http://www.sciencepublishinggroup.com/j/ajtas doi: 10.11648/j.ajtas.20180705.12 ISSN: 2326-8999 (Print); ISSN: 2326-9006 (Online)
More informationMODELLING TIME SERIES WITH CONDITIONAL HETEROSCEDASTICITY
MODELLING TIME SERIES WITH CONDITIONAL HETEROSCEDASTICITY The simple ARCH Model Eva Rubliková Ekonomická univerzita Bratislava Manuela Magalhães Hill Department of Quantitative Methods, INSTITUTO SUPERIOR
More informationForecasting of the Austrian Inflation Rate
Forecasting of the Austrian Inflation Rate Case Study for the Course of Econometric Forecasting Winter Semester 2007 by Nadir Shahzad Virkun Tomas Sedliacik Goal setting and Data selection The goal of
More informationA SEASONAL TIME SERIES MODEL FOR NIGERIAN MONTHLY AIR TRAFFIC DATA
www.arpapress.com/volumes/vol14issue3/ijrras_14_3_14.pdf A SEASONAL TIME SERIES MODEL FOR NIGERIAN MONTHLY AIR TRAFFIC DATA Ette Harrison Etuk Department of Mathematics/Computer Science, Rivers State University
More informationLecture 2: Univariate Time Series
Lecture 2: Univariate Time Series Analysis: Conditional and Unconditional Densities, Stationarity, ARMA Processes Prof. Massimo Guidolin 20192 Financial Econometrics Spring/Winter 2017 Overview Motivation:
More informationIntroduction to Forecasting
Introduction to Forecasting Introduction to Forecasting Predicting the future Not an exact science but instead consists of a set of statistical tools and techniques that are supported by human judgment
More informationComparing the Univariate Modeling Techniques, Box-Jenkins and Artificial Neural Network (ANN) for Measuring of Climate Index
Applied Mathematical Sciences, Vol. 8, 2014, no. 32, 1557-1568 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.4150 Comparing the Univariate Modeling Techniques, Box-Jenkins and Artificial
More informationUnivariate, Nonstationary Processes
Univariate, Nonstationary Processes Jamie Monogan University of Georgia March 20, 2018 Jamie Monogan (UGA) Univariate, Nonstationary Processes March 20, 2018 1 / 14 Objectives By the end of this meeting,
More informationA STUDY OF ARIMA AND GARCH MODELS TO FORECAST CRUDE PALM OIL (CPO) EXPORT IN INDONESIA
Proceeding of International Conference On Research, Implementation And Education Of Mathematics And Sciences 2015, Yogyakarta State University, 17-19 May 2015 A STUDY OF ARIMA AND GARCH MODELS TO FORECAST
More informationMODELING INFLATION RATES IN NIGERIA: BOX-JENKINS APPROACH. I. U. Moffat and A. E. David Department of Mathematics & Statistics, University of Uyo, Uyo
Vol.4, No.2, pp.2-27, April 216 MODELING INFLATION RATES IN NIGERIA: BOX-JENKINS APPROACH I. U. Moffat and A. E. David Department of Mathematics & Statistics, University of Uyo, Uyo ABSTRACT: This study
More informationEcon 423 Lecture Notes: Additional Topics in Time Series 1
Econ 423 Lecture Notes: Additional Topics in Time Series 1 John C. Chao April 25, 2017 1 These notes are based in large part on Chapter 16 of Stock and Watson (2011). They are for instructional purposes
More informationMultiple Regression Analysis
1 OUTLINE Basic Concept: Multiple Regression MULTICOLLINEARITY AUTOCORRELATION HETEROSCEDASTICITY REASEARCH IN FINANCE 2 BASIC CONCEPTS: Multiple Regression Y i = β 1 + β 2 X 1i + β 3 X 2i + β 4 X 3i +
More informationTHE APPLICATION OF GREY SYSTEM THEORY TO EXCHANGE RATE PREDICTION IN THE POST-CRISIS ERA
International Journal of Innovative Management, Information & Production ISME Internationalc20 ISSN 285-5439 Volume 2, Number 2, December 20 PP. 83-89 THE APPLICATION OF GREY SYSTEM THEORY TO EXCHANGE
More informationEconomics 618B: Time Series Analysis Department of Economics State University of New York at Binghamton
Problem Set #1 1. Generate n =500random numbers from both the uniform 1 (U [0, 1], uniformbetween zero and one) and exponential λ exp ( λx) (set λ =2and let x U [0, 1]) b a distributions. Plot the histograms
More informationFORECASTING COARSE RICE PRICES IN BANGLADESH
Progress. Agric. 22(1 & 2): 193 201, 2011 ISSN 1017-8139 FORECASTING COARSE RICE PRICES IN BANGLADESH M. F. Hassan*, M. A. Islam 1, M. F. Imam 2 and S. M. Sayem 3 Department of Agricultural Statistics,
More informationARIMA model to forecast international tourist visit in Bumthang, Bhutan
Journal of Physics: Conference Series PAPER OPEN ACCESS ARIMA model to forecast international tourist visit in Bumthang, Bhutan To cite this article: Choden and Suntaree Unhapipat 2018 J. Phys.: Conf.
More informationPrashant Pant 1, Achal Garg 2 1,2 Engineer, Keppel Offshore and Marine Engineering India Pvt. Ltd, Mumbai. IJRASET 2013: All Rights are Reserved 356
Forecasting Of Short Term Wind Power Using ARIMA Method Prashant Pant 1, Achal Garg 2 1,2 Engineer, Keppel Offshore and Marine Engineering India Pvt. Ltd, Mumbai Abstract- Wind power, i.e., electrical
More informationMultivariate Regression Model Results
Updated: August, 0 Page of Multivariate Regression Model Results 4 5 6 7 8 This exhibit provides the results of the load model forecast discussed in Schedule. Included is the forecast of short term system
More informationTESTING FOR CO-INTEGRATION
Bo Sjö 2010-12-05 TESTING FOR CO-INTEGRATION To be used in combination with Sjö (2008) Testing for Unit Roots and Cointegration A Guide. Instructions: Use the Johansen method to test for Purchasing Power
More informationProblem Set 2: Box-Jenkins methodology
Problem Set : Box-Jenkins methodology 1) For an AR1) process we have: γ0) = σ ε 1 φ σ ε γ0) = 1 φ Hence, For a MA1) process, p lim R = φ γ0) = 1 + θ )σ ε σ ε 1 = γ0) 1 + θ Therefore, p lim R = 1 1 1 +
More informationat least 50 and preferably 100 observations should be available to build a proper model
III Box-Jenkins Methods 1. Pros and Cons of ARIMA Forecasting a) need for data at least 50 and preferably 100 observations should be available to build a proper model used most frequently for hourly or
More informationAnalysis. Components of a Time Series
Module 8: Time Series Analysis 8.2 Components of a Time Series, Detection of Change Points and Trends, Time Series Models Components of a Time Series There can be several things happening simultaneously
More informationBUSI 460 Suggested Answers to Selected Review and Discussion Questions Lesson 7
BUSI 460 Suggested Answers to Selected Review and Discussion Questions Lesson 7 1. The definitions follow: (a) Time series: Time series data, also known as a data series, consists of observations on a
More informationUnivariate ARIMA Models
Univariate ARIMA Models ARIMA Model Building Steps: Identification: Using graphs, statistics, ACFs and PACFs, transformations, etc. to achieve stationary and tentatively identify patterns and model components.
More informationEXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY
EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA, 011 MODULE 3 : Stochastic processes and time series Time allowed: Three Hours Candidates should answer FIVE questions. All questions carry
More informationTime Series Analysis
Time Series Analysis A time series is a sequence of observations made: 1) over a continuous time interval, 2) of successive measurements across that interval, 3) using equal spacing between consecutive
More informationFinQuiz Notes
Reading 9 A time series is any series of data that varies over time e.g. the quarterly sales for a company during the past five years or daily returns of a security. When assumptions of the regression
More informationDetermine the trend for time series data
Extra Online Questions Determine the trend for time series data Covers AS 90641 (Statistics and Modelling 3.1) Scholarship Statistics and Modelling Chapter 1 Essent ial exam notes Time series 1. The value
More informationTime series and Forecasting
Chapter 2 Time series and Forecasting 2.1 Introduction Data are frequently recorded at regular time intervals, for instance, daily stock market indices, the monthly rate of inflation or annual profit figures.
More informationAvailable online Journal of Scientific and Engineering Research, 2017, 4(10): Research Article
Available online www.jsaer.com, 2017, 4(10):233-237 Research Article ISSN: 2394-2630 CODEN(USA): JSERBR Interrupted Time Series Modelling of Daily Amounts of British Pound Per Euro due to Brexit Ette Harrison
More informationThe ARIMA Procedure: The ARIMA Procedure
Page 1 of 120 Overview: ARIMA Procedure Getting Started: ARIMA Procedure The Three Stages of ARIMA Modeling Identification Stage Estimation and Diagnostic Checking Stage Forecasting Stage Using ARIMA Procedure
More informationFE570 Financial Markets and Trading. Stevens Institute of Technology
FE570 Financial Markets and Trading Lecture 5. Linear Time Series Analysis and Its Applications (Ref. Joel Hasbrouck - Empirical Market Microstructure ) Steve Yang Stevens Institute of Technology 9/25/2012
More informationLATVIAN GDP: TIME SERIES FORECASTING USING VECTOR AUTO REGRESSION
LATVIAN GDP: TIME SERIES FORECASTING USING VECTOR AUTO REGRESSION BEZRUCKO Aleksandrs, (LV) Abstract: The target goal of this work is to develop a methodology of forecasting Latvian GDP using ARMA (AutoRegressive-Moving-Average)
More informationTime Series Analysis. James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY
Time Series Analysis James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY & Contents PREFACE xiii 1 1.1. 1.2. Difference Equations First-Order Difference Equations 1 /?th-order Difference
More informationForecasting. Simon Shaw 2005/06 Semester II
Forecasting Simon Shaw s.c.shaw@maths.bath.ac.uk 2005/06 Semester II 1 Introduction A critical aspect of managing any business is planning for the future. events is called forecasting. Predicting future
More informationFigure 1. Time Series Plot of arrivals from Western Europe
FORECASTING TOURIST ARRIVALS TO SRI LANKA FROM WESTERN EUROPE K. M. U. B. Konarasinghe 1 * 1 Institute of Mathematics & Management, Nugegoda, Sri Lanka INTRODUCTION Sri Lanka was re-emerging after defeating
More informationAsian Economic and Financial Review. SEASONAL ARIMA MODELLING OF NIGERIAN MONTHLY CRUDE OIL PRICES Ette Harrison Etuk
Asian Economic and Financial Review journal homepage: http://aessweb.com/journal-detail.php?id=5002 SEASONAL ARIMA MODELLING OF NIGERIAN MONTHLY CRUDE OIL PRICES Ette Harrison Etuk Department of Mathematics/Computer
More informationApplication of ARIMA Models in Forecasting Monthly Total Rainfall of Rangamati, Bangladesh
Asian Journal of Applied Science and Engineering, Volume 6, No 2/2017 ISSN 2305-915X(p); 2307-9584(e) Application of ARIMA Models in Forecasting Monthly Total Rainfall of Rangamati, Bangladesh Fuhad Ahmed
More informationReview Session: Econometrics - CLEFIN (20192)
Review Session: Econometrics - CLEFIN (20192) Part II: Univariate time series analysis Daniele Bianchi March 20, 2013 Fundamentals Stationarity A time series is a sequence of random variables x t, t =
More informationThe GARCH Analysis of YU EBAO Annual Yields Weiwei Guo1,a
2nd Workshop on Advanced Research and Technology in Industry Applications (WARTIA 2016) The GARCH Analysis of YU EBAO Annual Yields Weiwei Guo1,a 1 Longdong University,Qingyang,Gansu province,745000 a
More information9) Time series econometrics
30C00200 Econometrics 9) Time series econometrics Timo Kuosmanen Professor Management Science http://nomepre.net/index.php/timokuosmanen 1 Macroeconomic data: GDP Inflation rate Examples of time series
More informationFORECASTING THE INVENTORY LEVEL OF MAGNETIC CARDS IN TOLLING SYSTEM
FORECASTING THE INVENTORY LEVEL OF MAGNETIC CARDS IN TOLLING SYSTEM Bratislav Lazić a, Nebojša Bojović b, Gordana Radivojević b*, Gorana Šormaz a a University of Belgrade, Mihajlo Pupin Institute, Serbia
More informationAPPLIED TIME SERIES ECONOMETRICS
APPLIED TIME SERIES ECONOMETRICS Edited by HELMUT LÜTKEPOHL European University Institute, Florence MARKUS KRÄTZIG Humboldt University, Berlin CAMBRIDGE UNIVERSITY PRESS Contents Preface Notation and Abbreviations
More informationMultiplicative Sarima Modelling Of Nigerian Monthly Crude Oil Domestic Production
Journal of Applied Mathematics & Bioinformatics, vol.3, no.3, 2013, 103-112 ISSN: 1792-6602 (print), 1792-6939 (online) Scienpress Ltd, 2013 Multiplicative Sarima Modelling Of Nigerian Monthly Crude Oil
More informationDynamic Time Series Regression: A Panacea for Spurious Correlations
International Journal of Scientific and Research Publications, Volume 6, Issue 10, October 2016 337 Dynamic Time Series Regression: A Panacea for Spurious Correlations Emmanuel Alphonsus Akpan *, Imoh
More informationMultiple Regression Analysis
1 OUTLINE Analysis of Data and Model Hypothesis Testing Dummy Variables Research in Finance 2 ANALYSIS: Types of Data Time Series data Cross-Sectional data Panel data Trend Seasonal Variation Cyclical
More informationIntroduction to Regression Analysis. Dr. Devlina Chatterjee 11 th August, 2017
Introduction to Regression Analysis Dr. Devlina Chatterjee 11 th August, 2017 What is regression analysis? Regression analysis is a statistical technique for studying linear relationships. One dependent
More informationLesson 13: Box-Jenkins Modeling Strategy for building ARMA models
Lesson 13: Box-Jenkins Modeling Strategy for building ARMA models Facoltà di Economia Università dell Aquila umberto.triacca@gmail.com Introduction In this lesson we present a method to construct an ARMA(p,
More informationNATCOR Regression Modelling for Time Series
Universität Hamburg Institut für Wirtschaftsinformatik Prof. Dr. D.B. Preßmar Professor Robert Fildes NATCOR Regression Modelling for Time Series The material presented has been developed with the substantial
More informationLecture 19 Box-Jenkins Seasonal Models
Lecture 19 Box-Jenkins Seasonal Models If the time series is nonstationary with respect to its variance, then we can stabilize the variance of the time series by using a pre-differencing transformation.
More informationarxiv: v1 [stat.me] 5 Nov 2008
arxiv:0811.0659v1 [stat.me] 5 Nov 2008 Estimation of missing data by using the filtering process in a time series modeling Ahmad Mahir R. and Al-khazaleh A. M. H. School of Mathematical Sciences Faculty
More informationCHAPTER III RESEARCH METHODOLOGY. trade balance performance of selected ASEAN-5 countries and exchange rate
CHAPTER III RESEARCH METHODOLOGY 3.1 Research s Object The research object is taking the macroeconomic perspective and focused on selected ASEAN-5 countries. This research is conducted to describe how
More informationTrends and Unit Roots in Greek Real Money Supply, Real GDP and Nominal Interest Rate
European Research Studies Volume V, Issue (3-4), 00, pp. 5-43 Trends and Unit Roots in Greek Real Money Supply, Real GDP and Nominal Interest Rate Karpetis Christos & Varelas Erotokritos * Abstract This
More informationAvailable online at ScienceDirect. Procedia Computer Science 72 (2015 )
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 72 (2015 ) 630 637 The Third Information Systems International Conference Performance Comparisons Between Arima and Arimax
More informationLecture Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University
Lecture 15 20 Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University Modeling for Time Series Forecasting Forecasting is a necessary input to planning, whether in business,
More informationFORECASTING SUGARCANE PRODUCTION IN INDIA WITH ARIMA MODEL
FORECASTING SUGARCANE PRODUCTION IN INDIA WITH ARIMA MODEL B. N. MANDAL Abstract: Yearly sugarcane production data for the period of - to - of India were analyzed by time-series methods. Autocorrelation
More informationTopic 4 Unit Roots. Gerald P. Dwyer. February Clemson University
Topic 4 Unit Roots Gerald P. Dwyer Clemson University February 2016 Outline 1 Unit Roots Introduction Trend and Difference Stationary Autocorrelations of Series That Have Deterministic or Stochastic Trends
More informationTechnical note on seasonal adjustment for Capital goods imports
Technical note on seasonal adjustment for Capital goods imports July 1, 2013 Contents 1 Capital goods imports 2 1.1 Additive versus multiplicative seasonality..................... 2 2 Steps in the seasonal
More informationpeak half-hourly New South Wales
Forecasting long-term peak half-hourly electricity demand for New South Wales Dr Shu Fan B.S., M.S., Ph.D. Professor Rob J Hyndman B.Sc. (Hons), Ph.D., A.Stat. Business & Economic Forecasting Unit Report
More informationDepartment of Economics, UCSB UC Santa Barbara
Department of Economics, UCSB UC Santa Barbara Title: Past trend versus future expectation: test of exchange rate volatility Author: Sengupta, Jati K., University of California, Santa Barbara Sfeir, Raymond,
More informationForecasting Area, Production and Yield of Cotton in India using ARIMA Model
Forecasting Area, Production and Yield of Cotton in India using ARIMA Model M. K. Debnath 1, Kartic Bera 2 *, P. Mishra 1 1 Department of Agricultural Statistics, Bidhan Chanda Krishi Vishwavidyalaya,
More informationTime Series Analysis. James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY
Time Series Analysis James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY PREFACE xiii 1 Difference Equations 1.1. First-Order Difference Equations 1 1.2. pth-order Difference Equations 7
More informationTime Series I Time Domain Methods
Astrostatistics Summer School Penn State University University Park, PA 16802 May 21, 2007 Overview Filtering and the Likelihood Function Time series is the study of data consisting of a sequence of DEPENDENT
More informationINTRODUCTION TO TIME SERIES ANALYSIS. The Simple Moving Average Model
INTRODUCTION TO TIME SERIES ANALYSIS The Simple Moving Average Model The Simple Moving Average Model The simple moving average (MA) model: More formally: where t is mean zero white noise (WN). Three parameters:
More informationFORECASTING FLUCTUATIONS OF ASPHALT CEMENT PRICE INDEX IN GEORGIA
FORECASTING FLUCTUATIONS OF ASPHALT CEMENT PRICE INDEX IN GEORGIA Mohammad Ilbeigi, Baabak Ashuri, Ph.D., and Yang Hui Economics of the Sustainable Built Environment (ESBE) Lab, School of Building Construction
More informationTechnical note on seasonal adjustment for M0
Technical note on seasonal adjustment for M0 July 1, 2013 Contents 1 M0 2 2 Steps in the seasonal adjustment procedure 3 2.1 Pre-adjustment analysis............................... 3 2.2 Seasonal adjustment.................................
More information