INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET)
|
|
- Amie Casey
- 5 years ago
- Views:
Transcription
1 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN (Print) ISSN (Online) Volume 5, Issue 3, March (204), pp IAEME: Journal Impact Factor (204): (Calculated by GISI) IJARET I A E M E A TIME SERIES MODEL FOR THE EXCHANGE RATE BETWEEN THE EURO (EUR) AND THE EGYPTIAN POUND (EGP) Taha Abdelshafy Abdelhakim Khalaf Department of Electrical Engineering, Assiut University, Assiut, Egypt, 756 ABSTRACT In this paper, we introduce a time series model that is capable of characterizing the exchange rate of the Euro to the Egyptian Pound (EUR/EGP). Since the exchange rate is considered as a financial time series, the traditional autoregressive integrated moving average (ARIMA) model would not be sufficient to model the data series. Financial time series often exhibit volatility clustering or persistence. Therefore, a model which captures the changes in the variance is required. In this paper, we adopt the general autoregressive conditional heteroskedastic (GARCH) model to fit the data. The analysis show that GARCH(,2) captures the heteroskedasticity of the data. I. INTRODUCTION The analysis of experimental data that have been observed at different points in time leads to new and unique problems in statistical modeling and inference. The obvious correlation introduced by the sampling of adjacent points in time should not be neglected in order to have a good model that represents the data []. One approach, advocated in the landmark work of Box and Jenkins, develops a systematic class of models called autoregressive integrated moving average (ARIMA) models to handle time-correlated modeling and forecasting. However ARIMA models work very well with most of time series it does not correctly fit the financial time series. That is because the set of ARIMA model tries to fit the conditional means of a stationary time series (i.e., changes in variance has to be alleviated) however the financial time series often exhibit volatility clustering or persistence. Therefore a model which is able to capture the heteroskedasticity of the data and fit the conditional variances is required. Time series also often exhibit volatility clustering or persistence. In volatility clustering, large changes tend to follow large changes, and small changes tend to follow small changes. The changes from one period to the next are typically of unpredictable sign. Large disturbances, positive or negative, become part of the information set used to construct the variance forecast of the next period s disturbance. In this way, large shocks of either sign can persist and influence volatility forecasts for 73
2 several periods. Volatility clustering suggests a time series model in which successive disturbances are uncorrelated but serially dependent. Recent problems in finance have motivated the study of the volatility, or variability, of a time series. Although ARMA models assume a constant variance, models such as the autoregressive conditionally heteroskedastic or ARCH model, first introduced by Engle (982), were developed to model changes in volatility. These models were later extended to generalized ARCH, or GARCH models by Bollerslev (986). In this project, we would like to find a good model that fits the time of exchange rate EUR/EGP. The daily exchange rate for the year 2008 is considered in this work. The source of the data is [4]. The rest of the report is organized as follows. Section 2 introduces the pre-estimation analysis in order to find the good model that fits the data. Some models are suggested based on the pre-estimation analysis and theye are presented in Section 3. In Section 4, I compare between the suggested models and select the best model that fits the data. Finally, conclusions are drawn in Section 5. II. PRE-ESTIMATION ANALYSIS When estimating the parameters of a composite conditional mean/variance model, we may occasionally encounter some problems problems such as: - Estimation may appear to stall, showing little or no progress; 2- Estimation may terminate before convergence.3- Estimation may converge to an unexpected, suboptimal solution. In order to avoid many of these difficulties it s better to select the simplest model that adequately describes the data, and then performing a pre-fit analysis. This pre-estimation analysis includes - Plot the return series and examine the ACF and PACF; 2- Perform preliminary tests, including McLeod-Li test and the Ljung-Box test. Daily Exch Rate Figure : Original Series Plot: Exchange rate EUR/EGP Figure 2 shows the time series plot of the exchange rate EUR/EGP. Since GARCH models assume a return series, the original exchange rate series has to be converted first to the returns. If x is the original exchange rate series then the returns series r is given by one of the following equations. Day 74
3 x x t t r t = () xt r = log( ) 00 (2) t x t Figure 2: Return series of the exchange rate Figure 3: Sample ACF and sample PACF of the returns series 75
4 In this work, (2) is used to calculate the return series. Figure 2 shows the plot of the return series. From the original series and the return series plots, we notice that the series is heteroskedastic, meaning that its variance varies with time. We also notice that the return series shows volatility clustering. In order to test the data for the conditional means model, we plot the sample autocorrelation function (ACF) and the sample partial autocorrelation function (PACF). Figure 2 shows the sample ACF and sample PACF plots of the returns series. From the shown figure, it is clear that the return series does not exhibit any correlation between data points and there is no real indication that we need to use any correlation structure in the conditional mean. To ensure that there is no conditional mean model required, the tsdiag function is used to test the ARIMA (0,0,0) model. Figure 2 shows the standardized residuals (returns in this case), correlation of the residuals, and results for the Ljung-Box test. These results confirms that the return series matches the characteristics of the white noise. Now, we check the returns series for the conditional variance model. Figure 2 shows the results of the McLeod-Li test when applied to the return series. We notice that all p-values are less than the 5% threshold. Figure 2 shows the sample ACF and sample PACF of the squared series. Although the returns themselves are largely uncorrelated, the variance process exhibits some correlation. From Figure 2 and Figure 2, we conclude that a conditional variance model is required to fit the exchange rate series. Figure 4: Diagnostics of the ARIMA (0,0,0) model of the returns series 76
5 Figure 5: McLeod-Li test of the the return series. Figure 6: Sample ACF and sample PACF of the squared returns series. III. Estimating Model Parameters The presence of heteroskedasticity, shown in the previous analysis, indicates that GARCH modeling is appropriate. The GARCH( p, q) model is defined by the following two equations σ w (3) rt = t t t 77
6 p q t t = α + 0 βiσ t i t i + α jrt j i= j= σ (4) where r t is the returns series, t t section, we will try different values of p and q and estimate the coefficients { σ is the variance of the returns at time t, and w t : N( 0,) } q i i= 0. In this β. α and { } p j Small lags for p and q are common in empirical applications. Typically, GARCH (,), GARCH (2,), or GARCH (,2) models are adequate for modeling volatilities even over long sample periods (see Bollerslev, Chou, and Kroner [2]). Since small lags are preferable, we will start with the simple ARCH() model first. The ARCH() model only consider α 0 and α. The estimated coefficients and their standard errors are stated in Table. The estimated coefficients of GARCH (,), GARCH (,2), and GARCH (2,) models are listed in Tables 2, 3, and 4 respectively. We notice that, the mean value µ is not statistically significant in the all proposed models. Table : ARCH() Model estimated coefficients Coefficient Estimate Std. Error t value Pr( > t ) Significance µ α < 2e 6 *** 0 α < 2e 6 *** j = 0 Table 2: GARCH(,) Model estimated coefficients. Coefficient Estimate Std. Error t value Pr( > t ) Significance µ α *** α e 09 *** β < 2e 6 *** Table 3: GARCH(,2) Model estimated coefficients Coefficient Estimate Std Error t value Pr( > t ) Significance µ α *** α e 0 *** β *** β * 78
7 Table 4: GARCH(2,) Model estimated coefficients Coefficient Estimate Std. Error t value Pr( > t ) Significance µ 6.252e e α.079e e α.53e e e 09 *** α.000e e 02.27e β 8.059e 0.045e e 4 *** IV. MODEL SELECTION AND POST-ESTIMATION ANALYSIS Figure 7: McLeod-Li test of the ARCH() residuals In this section, we select one of the models proposed in the previous section. First, we check the ARCH() model by applying the McLeod-Li test to its residuals. Figure 4 shows the results of the McLeod-Li test of the ARCH() residuals. It is clear that the ARCH() didn t capture the heteroskedasticity of the data very well and therefore this model is not accepted. The residuals of the other three models passed the McLeod-Li (we will only show the results for the selected model). In order to select one of the other three models, the AIC values are listed in Table 5. Based on the AIC values and the significance of the coefficients, the GARCH(,2) model is adopted. The results of the Ljung Box test, the McLeod-Li test, and the sample autocorrelations of the squared residuals are shown in Figures 6, 7, and 8 respectively. From the shown figures, we see that the GARCH(,2) model is a good fit for the returns series of the exchange rate. Figure 9 shows the returns series plot with two Conditional SD Superimposed. It clear that the selected models captures the heteroskedasticity very well. To benefit from the fitted model, we use it to predict 20 days ahead. Figure 0 shows the last 20 points of the return series together with the 20 predicted points. 79
8 Table 5: AIC values for the proposed GARCH models GARCH(,) GARCH(,2) GARCH(2,) AIC Figure 8: Diagnostics of the GARCH(,2) Figure 9: McLeod-Li test of the GARCH(,2) residuals 80
9 Figure 0: Sample ACF and PACF of the GARCH(,2) squared residuals Figure : The returns series with two Conditional SD Superimposed 8
10 Figure 2: Prediction with confidence intervals V. CONCLUSIONS In this project, we studied he characteristics of the time series of the exchange rate Euro to Egyptian pound. We found that the return series has the same correlation properties as that of the white noise and therefore, we chose not to fit a conditional mean model. The squared return series has dependence between its points and the GARCH(,2) model is proved to be a good fit for the series. It was also shown that the selected model captures the heteroskedasticity very well. The fitted model is used to predict 20 days ahead. REFERENCES [] Robert Shumway and David Stoffer, Introduction to Time Series and Its Applications with R examples, Second Edition, Springer. [2] T. Bollerslev, R. Y. Chou, and K. F. Kroner ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence. Journal of Econometrics. vol. 52, 992, pp [3] Matlab Econometric and Financial Toolboxs [4] [5] H. J. Surendra and Paresh Chandra Deka Effects of statistical properties of dataset in predicting performance of various artificial intelligence techniques for urban water consumption time series, IAEME International Journal of Civil Engineering and Technology (IJCIET), vol. 3, no. 2, pp
Volatility Analysis of Nepalese Stock Market
The Journal of Nepalese Business Studies Vol. V No. 1 Dec. 008 Volatility Analysis of Nepalese Stock Market Surya Bahadur G.C. Abstract Modeling and forecasting volatility of capital markets has been important
More informationSTAT758. Final Project. Time series analysis of daily exchange rate between the British Pound and the. US dollar (GBP/USD)
STAT758 Final Project Time series analysis of daily exchange rate between the British Pound and the US dollar (GBP/USD) Theophilus Djanie and Harry Dick Thompson UNR May 14, 2012 INTRODUCTION Time Series
More informationChapter 4 Level of Volatility in the Indian Stock Market
Chapter 4 Level of Volatility in the Indian Stock Market Measurement of volatility is an important issue in financial econometrics. The main reason for the prominent role that volatility plays in financial
More informationLecture 5a: ARCH Models
Lecture 5a: ARCH Models 1 2 Big Picture 1. We use ARMA model for the conditional mean 2. We use ARCH model for the conditional variance 3. ARMA and ARCH model can be used together to describe both conditional
More informationModelling Stock Market Return Volatility: Evidence from India
Modelling Stock Market Return Volatility: Evidence from India Saurabh Singh Assistant Professor, Graduate School of Business,Devi Ahilya Vishwavidyalaya, Indore 452001 (M.P.) India Dr. L.K Tripathi Dean,
More informationThe Analysis of ICBC Stock Based on ARMA-GARCH Model
Volume 04 - Issue 08 August 2018 PP. 11-16 The Analysis of ICBC Stock Based on ARMA-GARCH Model Si-qin LIU 1 Hong-guo SUN 1* 1 (Department of Mathematics and Finance Hunan University of Humanities Science
More informationVariance clustering. Two motivations, volatility clustering, and implied volatility
Variance modelling The simplest assumption for time series is that variance is constant. Unfortunately that assumption is often violated in actual data. In this lecture we look at the implications of time
More informationModeling the volatility of FTSE All Share Index Returns
MPRA Munich Personal RePEc Archive Modeling the volatility of FTSE All Share Index Returns Bayraci, Selcuk University of Exeter, Yeditepe University 27. April 2007 Online at http://mpra.ub.uni-muenchen.de/28095/
More informationGARCH Models. Instructor: G. William Schwert
APS 425 Fall 2015 GARCH Models Instructor: G. William Schwert 585-275-2470 schwert@schwert.ssb.rochester.edu Autocorrelated Heteroskedasticity Suppose you have regression residuals Mean = 0, not autocorrelated
More informationThe Great Moderation Flattens Fat Tails: Disappearing Leptokurtosis
The Great Moderation Flattens Fat Tails: Disappearing Leptokurtosis WenShwo Fang Department of Economics Feng Chia University 100 WenHwa Road, Taichung, TAIWAN Stephen M. Miller* College of Business University
More informationFinancial Econometrics
Financial Econometrics Volatility Gerald P. Dwyer Trinity College, Dublin January 2013 GPD (TCD) Volatility 01/13 1 / 37 Squared log returns for CRSP daily GPD (TCD) Volatility 01/13 2 / 37 Absolute value
More informationINFORMATION EFFICIENCY HYPOTHESIS THE FINANCIAL VOLATILITY IN THE CZECH REPUBLIC CASE
INFORMATION EFFICIENCY HYPOTHESIS THE FINANCIAL VOLATILITY IN THE CZECH REPUBLIC CASE Abstract Petr Makovský If there is any market which is said to be effective, this is the the FOREX market. Here we
More informationVolatility Clustering of Fine Wine Prices assuming Different Distributions
Volatility Clustering of Fine Wine Prices assuming Different Distributions Cynthia Royal Tori, PhD Valdosta State University Langdale College of Business 1500 N. Patterson Street, Valdosta, GA USA 31698
More informationARCH modeling of the returns of first bank of Nigeria
AMERICAN JOURNAL OF SCIENTIFIC AND INDUSTRIAL RESEARCH 015,Science Huβ, http://www.scihub.org/ajsir ISSN: 153-649X, doi:10.551/ajsir.015.6.6.131.140 ARCH modeling of the returns of first bank of Nigeria
More informationMODELING EXCHANGE RATE VOLATILITY OF UZBEK SUM BY USING ARCH FAMILY MODELS
International Journal of Economics, Commerce and Management United Kingdom Vol. VI, Issue 11, November 2018 http://ijecm.co.uk/ ISSN 2348 0386 MODELING EXCHANGE RATE VOLATILITY OF UZBEK SUM BY USING ARCH
More informationEconomics 413: Economic Forecast and Analysis Department of Economics, Finance and Legal Studies University of Alabama
Problem Set #1 (Linear Regression) 1. The file entitled MONEYDEM.XLS contains quarterly values of seasonally adjusted U.S.3-month ( 3 ) and 1-year ( 1 ) treasury bill rates. Each series is measured over
More informationEquity Price Dynamics Before and After the Introduction of the Euro: A Note*
Equity Price Dynamics Before and After the Introduction of the Euro: A Note* Yin-Wong Cheung University of California, U.S.A. Frank Westermann University of Munich, Germany Daily data from the German and
More informationChapter 6 Forecasting Volatility using Stochastic Volatility Model
Chapter 6 Forecasting Volatility using Stochastic Volatility Model Chapter 6 Forecasting Volatility using SV Model In this chapter, the empirical performance of GARCH(1,1), GARCH-KF and SV models from
More informationComputer Lab Session 2 ARIMA, ARCH and GARCH Models
JBS Advanced Quantitative Research Methods Module MPO-1A Lent 2010 Thilo Klein http://thiloklein.de Contents Computer Lab Session 2 ARIMA, ARCH and GARCH Models Exercise 1. Estimation of a quarterly ARMA
More informationModeling Volatility of Price of Some Selected Agricultural Products in Ethiopia: ARIMA-GARCH Applications
Modeling Volatility of Price of Some Selected Agricultural Products in Ethiopia: ARIMA-GARCH Applications Background: Agricultural products market policies in Ethiopia have undergone dramatic changes over
More informationMODELING VOLATILITY OF BSE SECTORAL INDICES
MODELING VOLATILITY OF BSE SECTORAL INDICES DR.S.MOHANDASS *; MRS.P.RENUKADEVI ** * DIRECTOR, DEPARTMENT OF MANAGEMENT SCIENCES, SVS INSTITUTE OF MANAGEMENT SCIENCES, MYLERIPALAYAM POST, ARASAMPALAYAM,COIMBATORE
More informationConditional Heteroscedasticity
1 Conditional Heteroscedasticity May 30, 2010 Junhui Qian 1 Introduction ARMA(p,q) models dictate that the conditional mean of a time series depends on past observations of the time series and the past
More informationJaime Frade Dr. Niu Interest rate modeling
Interest rate modeling Abstract In this paper, three models were used to forecast short term interest rates for the 3 month LIBOR. Each of the models, regression time series, GARCH, and Cox, Ingersoll,
More informationModeling Exchange Rate Volatility using APARCH Models
96 TUTA/IOE/PCU Journal of the Institute of Engineering, 2018, 14(1): 96-106 TUTA/IOE/PCU Printed in Nepal Carolyn Ogutu 1, Betuel Canhanga 2, Pitos Biganda 3 1 School of Mathematics, University of Nairobi,
More informationModelling volatility - ARCH and GARCH models
Modelling volatility - ARCH and GARCH models Beáta Stehlíková Time series analysis Modelling volatility- ARCH and GARCH models p.1/33 Stock prices Weekly stock prices (library quantmod) Continuous returns:
More informationEmpirical Study on Short-Term Prediction of Shanghai Composite Index Based on ARMA Model
Empirical Study on Short-Term Prediction of Shanghai Composite Index Based on ARMA Model Cai-xia Xiang 1, Ping Xiao 2* 1 (School of Hunan University of Humanities, Science and Technology, Hunan417000,
More informationResearch Article The Volatility of the Index of Shanghai Stock Market Research Based on ARCH and Its Extended Forms
Discrete Dynamics in Nature and Society Volume 2009, Article ID 743685, 9 pages doi:10.1155/2009/743685 Research Article The Volatility of the Index of Shanghai Stock Market Research Based on ARCH and
More informationMarket Risk Management for Financial Institutions Based on GARCH Family Models
Washington University in St. Louis Washington University Open Scholarship Arts & Sciences Electronic Theses and Dissertations Arts & Sciences Spring 5-2017 Market Risk Management for Financial Institutions
More informationTime series: Variance modelling
Time series: Variance modelling Bernt Arne Ødegaard 5 October 018 Contents 1 Motivation 1 1.1 Variance clustering.......................... 1 1. Relation to heteroskedasticity.................... 3 1.3
More informationIndian Institute of Management Calcutta. Working Paper Series. WPS No. 797 March Implied Volatility and Predictability of GARCH Models
Indian Institute of Management Calcutta Working Paper Series WPS No. 797 March 2017 Implied Volatility and Predictability of GARCH Models Vivek Rajvanshi Assistant Professor, Indian Institute of Management
More informationForecasting Exchange Rate between Thai Baht and the US Dollar Using Time Series Analysis
Forecasting Exchange Rate between Thai Baht and the US Dollar Using Time Series Analysis Kunya Bowornchockchai International Science Index, Mathematical and Computational Sciences waset.org/publication/10003789
More informationModeling Volatility Clustering of Bank Index: An Empirical Study of BankNifty
Review of Integrative Business and Economics Research, Vol. 6, no. 1, pp.224-239, January 2017 224 Modeling Volatility Clustering of Bank Index: An Empirical Study of BankNifty Ashok Patil * Kirloskar
More informationPortfolio construction by volatility forecasts: Does the covariance structure matter?
Portfolio construction by volatility forecasts: Does the covariance structure matter? Momtchil Pojarliev and Wolfgang Polasek INVESCO Asset Management, Bleichstrasse 60-62, D-60313 Frankfurt email: momtchil
More informationThe Variability of IPO Initial Returns
The Variability of IPO Initial Returns Journal of Finance 65 (April 2010) 425-465 Michelle Lowry, Micah Officer, and G. William Schwert Interesting blend of time series and cross sectional modeling issues
More informationForecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models
The Financial Review 37 (2002) 93--104 Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models Mohammad Najand Old Dominion University Abstract The study examines the relative ability
More informationGARCH Models for Inflation Volatility in Oman
Rev. Integr. Bus. Econ. Res. Vol 2(2) 1 GARCH Models for Inflation Volatility in Oman Muhammad Idrees Ahmad Department of Mathematics and Statistics, College of Science, Sultan Qaboos Universty, Alkhod,
More informationModelling Rates of Inflation in Ghana: An Application of Arch Models
Current Research Journal of Economic Theory 6(2): 16-21, 214 ISSN: 242-4841, e-issn: 242-485X Maxwell Scientific Organization, 214 Submitted: February 28, 214 Accepted: April 8, 214 Published: June 2,
More informationEmpirical Analysis of GARCH Effect of Shanghai Copper Futures
Volume 04 - Issue 06 June 2018 PP. 39-45 Empirical Analysis of GARCH Effect of Shanghai Copper 1902 Futures Wei Wu, Fang Chen* Department of Mathematics and Finance Hunan University of Humanities Science
More informationOptimal Hedge Ratio and Hedging Effectiveness of Stock Index Futures Evidence from India
Optimal Hedge Ratio and Hedging Effectiveness of Stock Index Futures Evidence from India Executive Summary In a free capital mobile world with increased volatility, the need for an optimal hedge ratio
More informationMODELING VOLATILITY OF US CONSUMER CREDIT SERIES
MODELING VOLATILITY OF US CONSUMER CREDIT SERIES Ellis Heath Harley Langdale, Jr. College of Business Administration Valdosta State University 1500 N. Patterson Street Valdosta, GA 31698 ABSTRACT Consumer
More informationMODELING NIGERIA S CONSUMER PRICE INDEX USING ARIMA MODEL
MODELING NIGERIA S CONSUMER PRICE INDEX USING ARIMA MODEL 1 S.O. Adams 2 A. Awujola 3 A.I. Alumgudu 1 Department of Statistics, University of Abuja, Abuja Nigeria 2 Department of Economics, Bingham University,
More informationFinancial Time Series Analysis (FTSA)
Financial Time Series Analysis (FTSA) Lecture 6: Conditional Heteroscedastic Models Few models are capable of generating the type of ARCH one sees in the data.... Most of these studies are best summarized
More informationDeterminants of Stock Prices in Ghana
Current Research Journal of Economic Theory 5(4): 66-7, 213 ISSN: 242-4841, e-issn: 242-485X Maxwell Scientific Organization, 213 Submitted: November 8, 212 Accepted: December 21, 212 Published: December
More informationModel Construction & Forecast Based Portfolio Allocation:
QBUS6830 Financial Time Series and Forecasting Model Construction & Forecast Based Portfolio Allocation: Is Quantitative Method Worth It? Members: Bowei Li (303083) Wenjian Xu (308077237) Xiaoyun Lu (3295347)
More informationThe University of Chicago, Booth School of Business Business 41202, Spring Quarter 2010, Mr. Ruey S. Tsay Solutions to Final Exam
The University of Chicago, Booth School of Business Business 410, Spring Quarter 010, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (4 pts) Answer briefly the following questions. 1. Questions 1
More informationTime series analysis on return of spot gold price
Time series analysis on return of spot gold price Team member: Tian Xie (#1371992) Zizhen Li(#1368493) Contents Exploratory Analysis... 2 Data description... 2 Data preparation... 2 Basics Stats... 2 Unit
More informationUnivariate Time Series Analysis of Forecasting Asset Prices
[ VOLUME 3 I ISSUE 3 I JULY SEPT. 2016] E ISSN 2348 1269, PRINT ISSN 2349-5138 Univariate Time Series Analysis of Forecasting Asset Prices Tanu Shivnani Research Scholar, Jawaharlal Nehru University, Delhi.
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has
More informationARIMA ANALYSIS WITH INTERVENTIONS / OUTLIERS
TASK Run intervention analysis on the price of stock M: model a function of the price as ARIMA with outliers and interventions. SOLUTION The document below is an abridged version of the solution provided
More informationEstimating Historical Volatility via Dynamical System
American Journal of Mathematics and Statistics, (): - DOI:./j.ajms.. Estimating Historical Volatility via Dynamical System Onyeka-Ubaka J. N.,*, Okafor R. O., Adewara J. A. Department of Mathematics, University
More informationAmath 546/Econ 589 Univariate GARCH Models
Amath 546/Econ 589 Univariate GARCH Models Eric Zivot April 24, 2013 Lecture Outline Conditional vs. Unconditional Risk Measures Empirical regularities of asset returns Engle s ARCH model Testing for ARCH
More informationInternational Journal of Business and Administration Research Review. Vol.3, Issue.22, April-June Page 1
A STUDY ON ANALYZING VOLATILITY OF GOLD PRICE IN INDIA Mr. Arun Kumar D C* Dr. P.V.Raveendra** *Research scholar,bharathiar University, Coimbatore. **Professor and Head Department of Management Studies,
More informationThe University of Chicago, Booth School of Business Business 41202, Spring Quarter 2017, Mr. Ruey S. Tsay. Solutions to Final Exam
The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2017, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (40 points) Answer briefly the following questions. 1. Describe
More informationRunning head: IMPROVING REVENUE VOLATILITY ESTIMATES 1. Improving Revenue Volatility Estimates Using Time-Series Decomposition Methods
Running head: IMPROVING REVENUE VOLATILITY ESTIMATES 1 Improving Revenue Volatility Estimates Using Time-Series Decomposition Methods Kenneth A. Kriz Wichita State University Author Note The author wishes
More informationIS INFLATION VOLATILITY CORRELATED FOR THE US AND CANADA?
IS INFLATION VOLATILITY CORRELATED FOR THE US AND CANADA? C. Barry Pfitzner, Department of Economics/Business, Randolph-Macon College, Ashland, VA, bpfitzne@rmc.edu ABSTRACT This paper investigates the
More informationEmpirical Analysis of the US Swap Curve Gough, O., Juneja, J.A., Nowman, K.B. and Van Dellen, S.
WestminsterResearch http://www.westminster.ac.uk/westminsterresearch Empirical Analysis of the US Swap Curve Gough, O., Juneja, J.A., Nowman, K.B. and Van Dellen, S. This is a copy of the final version
More informationSTOCK MARKET EFFICIENCY, NON-LINEARITY AND THIN TRADING EFFECTS IN SOME SELECTED COMPANIES IN GHANA
STOCK MARKET EFFICIENCY, NON-LINEARITY AND THIN TRADING Abstract EFFECTS IN SOME SELECTED COMPANIES IN GHANA Wiredu Sampson *, Atopeo Apuri Benjamin and Allotey Robert Nii Ampah Department of Statistics,
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2016, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2016, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has
More informationModeling and Forecasting Volatility in Financial Time Series: An Econometric Analysis of the S&P 500 and the VIX Index.
F A C U L T Y O F S O C I A L S C I E N C E S D E P A R T M E N T O F E C O N O M I C S U N I V E R S I T Y O F C O P E N H A G E N Seminar in finance Modeling and Forecasting Volatility in Financial Time
More informationYafu Zhao Department of Economics East Carolina University M.S. Research Paper. Abstract
This version: July 16, 2 A Moving Window Analysis of the Granger Causal Relationship Between Money and Stock Returns Yafu Zhao Department of Economics East Carolina University M.S. Research Paper Abstract
More informationARCH and GARCH models
ARCH and GARCH models Fulvio Corsi SNS Pisa 5 Dic 2011 Fulvio Corsi ARCH and () GARCH models SNS Pisa 5 Dic 2011 1 / 21 Asset prices S&P 500 index from 1982 to 2009 1600 1400 1200 1000 800 600 400 200
More informationFinancial Econometrics Notes. Kevin Sheppard University of Oxford
Financial Econometrics Notes Kevin Sheppard University of Oxford Monday 15 th January, 2018 2 This version: 22:52, Monday 15 th January, 2018 2018 Kevin Sheppard ii Contents 1 Probability, Random Variables
More informationDeterminants of Revenue Generation Capacity in the Economy of Pakistan
2014, TextRoad Publication ISSN 2090-4304 Journal of Basic and Applied Scientific Research www.textroad.com Determinants of Revenue Generation Capacity in the Economy of Pakistan Khurram Ejaz Chandia 1,
More informationAn Empirical Research on Chinese Stock Market Volatility Based. on Garch
Volume 04 - Issue 07 July 2018 PP. 15-23 An Empirical Research on Chinese Stock Market Volatility Based on Garch Ya Qian Zhu 1, Wen huili* 1 (Department of Mathematics and Finance, Hunan University of
More informationStudy on Dynamic Risk Measurement Based on ARMA-GJR-AL Model
Applied and Computational Mathematics 5; 4(3): 6- Published online April 3, 5 (http://www.sciencepublishinggroup.com/j/acm) doi:.648/j.acm.543.3 ISSN: 38-565 (Print); ISSN: 38-563 (Online) Study on Dynamic
More informationThe University of Chicago, Booth School of Business Business 41202, Spring Quarter 2009, Mr. Ruey S. Tsay. Solutions to Final Exam
The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2009, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (42 pts) Answer briefly the following questions. 1. Questions
More informationOil Price Effects on Exchange Rate and Price Level: The Case of South Korea
Oil Price Effects on Exchange Rate and Price Level: The Case of South Korea Mirzosaid SULTONOV 東北公益文科大学総合研究論集第 34 号抜刷 2018 年 7 月 30 日発行 研究論文 Oil Price Effects on Exchange Rate and Price Level: The Case
More informationPerformance Dynamics of Hedge Fund Index Investing
Journal of Business and Economics, ISSN 2155-7950, USA November 2016, Volume 7, No. 11, pp. 1729-1742 DOI: 10.15341/jbe(2155-7950)/11.07.2016/001 Academic Star Publishing Company, 2016 http://www.academicstar.us
More informationTHE UNIVERSITY OF CHICAGO Graduate School of Business Business 41202, Spring Quarter 2003, Mr. Ruey S. Tsay
THE UNIVERSITY OF CHICAGO Graduate School of Business Business 41202, Spring Quarter 2003, Mr. Ruey S. Tsay Homework Assignment #2 Solution April 25, 2003 Each HW problem is 10 points throughout this quarter.
More informationReturn Volatility and Asymmetric News Effect in Sri Lankan Stock Market
Return Volatility and Asymmetric News Effect in Sri Lankan Stock Market Sujeetha Jegajeevan a/ Economic Research Department Abstract This paper studies daily and monthly returns in the Colombo Stock Exchange
More informationModelling the stochastic behaviour of short-term interest rates: A survey
Modelling the stochastic behaviour of short-term interest rates: A survey 4 5 6 7 8 9 10 SAMBA/21/04 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Kjersti Aas September 23, 2004 NR Norwegian Computing
More informationModeling Volatility in Financial Time Series: Evidence from Nigerian Inflation Rates
IOSR Journal of Mathematics (IOSR-JM) e-issn: 78-578, p-issn: 319-765X. Volume 11, Issue 4 Ver. IV (Jul - Aug. 015), PP 09-17 www.iosrjournals.org Modeling Volatility in Financial Time Series: Evidence
More informationVolume Effects in Standard & Poor's 500 Prices
IOSR Journal of Economics and Finance (IOSR-JEF) e-issn: 2321-5933, p-issn: 2321-5925.Volume 7, Issue 5 Ver. III (Sep. - Oct. 2016), PP 63-73 www.iosrjournals.org Volume Effects in Standard & Poor's 500
More informationRISK SPILLOVER EFFECTS IN THE CZECH FINANCIAL MARKET
RISK SPILLOVER EFFECTS IN THE CZECH FINANCIAL MARKET Vít Pošta Abstract The paper focuses on the assessment of the evolution of risk in three segments of the Czech financial market: capital market, money/debt
More informationModeling and Forecasting Consumer Price Index (Case of Rwanda)
American Journal of Theoretical and Applied Statistics 2016; 5(3): 101-107 http://www.sciencepublishinggroup.com/j/ajtas doi: 10.11648/j.ajtas.20160503.14 ISSN: 2326-8999 (Print); ISSN: 2326-9006 (Online)
More informationUS HFCS Price Forecasting Using Seasonal ARIMA Model
US HFCS Price Forecasting Using Seasonal ARIMA Model Prithviraj Lakkakula Research Assistant Professor Department of Agribusiness and Applied Economics North Dakota State University Email: prithviraj.lakkakula@ndsu.edu
More informationValencia. Keywords: Conditional volatility, backpropagation neural network, GARCH in Mean MSC 2000: 91G10, 91G70
Int. J. Complex Systems in Science vol. 2(1) (2012), pp. 21 26 Estimating returns and conditional volatility: a comparison between the ARMA-GARCH-M Models and the Backpropagation Neural Network Fernando
More informationFin285a:Computer Simulations and Risk Assessment Section 7.1 Modeling Volatility: basic models Daníelson, ,
Fin285a:Computer Simulations and Risk Assessment Section 7.1 Modeling Volatility: basic models Daníelson, 2.1-2.3, 2.7-2.8 Overview Moving average model Exponentially weighted moving average (EWMA) GARCH
More informationRelationship between Consumer Price Index (CPI) and Government Bonds
MPRA Munich Personal RePEc Archive Relationship between Consumer Price Index (CPI) and Government Bonds Muhammad Imtiaz Subhani Iqra University Research Centre (IURC), Iqra university Main Campus Karachi,
More informationA joint Initiative of Ludwig-Maximilians-Universität and Ifo Institute for Economic Research
A joint Initiative of Ludwig-Maximilians-Universität and Ifo Institute for Economic Research Working Papers EQUITY PRICE DYNAMICS BEFORE AND AFTER THE INTRODUCTION OF THE EURO: A NOTE Yin-Wong Cheung Frank
More informationPrerequisites for modeling price and return data series for the Bucharest Stock Exchange
Theoretical and Applied Economics Volume XX (2013), No. 11(588), pp. 117-126 Prerequisites for modeling price and return data series for the Bucharest Stock Exchange Andrei TINCA The Bucharest University
More informationSt. Theresa Journal of Humanities and Social Sciences
Volatility Modeling for SENSEX using ARCH Family G. Arivalagan* Research scholar, Alagappa Institute of Management Alagappa University, Karaikudi-630003, India. E-mail: arivu760@gmail.com *Corresponding
More informationSHORT-RUN DEVIATIONS AND TIME-VARYING HEDGE RATIOS: EVIDENCE FROM AGRICULTURAL FUTURES MARKETS TAUFIQ CHOUDHRY
SHORT-RUN DEVIATIONS AND TIME-VARYING HEDGE RATIOS: EVIDENCE FROM AGRICULTURAL FUTURES MARKETS By TAUFIQ CHOUDHRY School of Management University of Bradford Emm Lane Bradford BD9 4JL UK Phone: (44) 1274-234363
More informationFE570 Financial Markets and Trading. Stevens Institute of Technology
FE570 Financial Markets and Trading Lecture 6. Volatility Models and (Ref. Joel Hasbrouck - Empirical Market Microstructure ) Steve Yang Stevens Institute of Technology 10/02/2012 Outline 1 Volatility
More informationAsian Economic and Financial Review A REGRESSION BASED APPROACH TO CAPTURING THE LEVEL DEPENDENCE IN THE VOLATILITY OF STOCK RETURNS
Asian Economic and Financial Review ISSN(e): 2222-6737/ISSN(p): 2305-2147 URL: www.aessweb.com A REGRESSION BASED APPROACH TO CAPTURING THE LEVEL DEPENDENCE IN THE VOLATILITY OF STOCK RETURNS Lakshmi Padmakumari
More informationLONG MEMORY IN VOLATILITY
LONG MEMORY IN VOLATILITY How persistent is volatility? In other words, how quickly do financial markets forget large volatility shocks? Figure 1.1, Shephard (attached) shows that daily squared returns
More informationForecasting Prices and Congestion for Transmission Grid Operation
Forecasting Prices and Congestion for Transmission Grid Operation Project Team: Principal Investigators: Profs. Chen-Ching Liu and Leigh Tesfatsion Research Assistants: ECpE Ph.D. Students Qun Zhou and
More informationAN ARTIFICIAL NEURAL NETWORK MODELING APPROACH TO PREDICT CRUDE OIL FUTURE. By Dr. PRASANT SARANGI Director (Research) ICSI-CCGRT, Navi Mumbai
AN ARTIFICIAL NEURAL NETWORK MODELING APPROACH TO PREDICT CRUDE OIL FUTURE By Dr. PRASANT SARANGI Director (Research) ICSI-CCGRT, Navi Mumbai AN ARTIFICIAL NEURAL NETWORK MODELING APPROACH TO PREDICT CRUDE
More informationTHE INFORMATION CONTENT OF IMPLIED VOLATILITY IN AGRICULTURAL COMMODITY MARKETS. Pierre Giot 1
THE INFORMATION CONTENT OF IMPLIED VOLATILITY IN AGRICULTURAL COMMODITY MARKETS Pierre Giot 1 May 2002 Abstract In this paper we compare the incremental information content of lagged implied volatility
More informationA market risk model for asymmetric distributed series of return
University of Wollongong Research Online University of Wollongong in Dubai - Papers University of Wollongong in Dubai 2012 A market risk model for asymmetric distributed series of return Kostas Giannopoulos
More informationThe Impact of Falling Crude Oil Price on Financial Markets of Advanced East Asian Countries
10 Journal of Reviews on Global Economics, 2018, 7, 10-20 The Impact of Falling Crude Oil Price on Financial Markets of Advanced East Asian Countries Mirzosaid Sultonov * Tohoku University of Community
More informationForeign direct investment and profit outflows: a causality analysis for the Brazilian economy. Abstract
Foreign direct investment and profit outflows: a causality analysis for the Brazilian economy Fernando Seabra Federal University of Santa Catarina Lisandra Flach Universität Stuttgart Abstract Most empirical
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2012, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (34 pts) Answer briefly the following questions. Each question has
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2010, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2010, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has
More informationUniversity of Zürich, Switzerland
University of Zürich, Switzerland RE - general asset features The inclusion of real estate assets in a portfolio has proven to bring diversification benefits both for homeowners [Mahieu, Van Bussel 1996]
More informationProperties of financail time series GARCH(p,q) models Risk premium and ARCH-M models Leverage effects and asymmetric GARCH models.
5 III Properties of financail time series GARCH(p,q) models Risk premium and ARCH-M models Leverage effects and asymmetric GARCH models 1 ARCH: Autoregressive Conditional Heteroscedasticity Conditional
More informationKunming, Yunnan, China. Kunming, Yunnan, China. *Corresponding author
2017 4th International Conference on Economics and Management (ICEM 2017) ISBN: 978-1-60595-467-7 Analysis on the Development Trend of Per Capita GDP in Yunnan Province Based on Quantile Regression Yong-sheng
More informationIJMS 17 (Special Issue), 119 141 (2010) CRISES AND THE VOLATILITY OF INDONESIAN MACRO-INDICATORS 1 CATUR SUGIYANTO Faculty of Economics and Business Universitas Gadjah Mada, Indonesia Abstract This paper
More informationVolatility of the Banking Sector Stock Returns in Nigeria
Ruhuna Journal of Management and Finance Volume 1 Number 1 - January 014 ISSN 35-9 R JMF Volatility of the Banking Sector Stock Returns in Nigeria K.O. Emenike and W.U. Ani K.O. Emenike * and W.U. Ani
More informationLAMPIRAN. Null Hypothesis: LO has a unit root Exogenous: Constant Lag Length: 1 (Automatic based on SIC, MAXLAG=13)
74 LAMPIRAN Lampiran 1 Analisis ARIMA 1.1. Uji Stasioneritas Variabel 1. Data Harga Minyak Riil Level Null Hypothesis: LO has a unit root Lag Length: 1 (Automatic based on SIC, MAXLAG=13) Augmented Dickey-Fuller
More information