Mohammad khaleq newaz


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Mohammad khaleq newaz

  1. 1. Measuring Forecast Performance of ARIMA Model: An Application to U.S. Dollar/Bangladeshi Taka Foreign Exchange Rate Presented by Mohammad Khaleq Newaz Research Student PGR Conference 12 November, 2010.
  2. 2. Overview • Effective management of foreign exchange is very important to achieve tolerable inflation and a desired level of economic growth for a country. • It is crucial to forecast the exchange rate to evaluate the benefits and risks attached to the international business environment.
  3. 3. Country profile Official Name : The People’s Republic of Bangladesh Location & Borders : South Asia bordered by India on the east, west & north and by Bay of Bengal on the south and small border strip with Myanmar on the south-east. Area/Land : 147,570 square km-mostly alluvial fertile plain. Territorial Waters 22.22 km. Economic Zone up to 370.40 km. in the high seas, measured from the base line. Population/Literacy : 159.00 million (In 2007)/62.66% (In 2002) Sectoral share of GDP : 21.91% agriculture, 28.44% industry, 49.65% service. GDP/rate of growth : US$ 72.4 billion /6.2% (2007-08) Per Capita GDP/GNI : US$554 / US$599 (2007-08) Annual Export/Import : US$ 15.57 billion / US$ 21.44 billion (2008-2009) Major Trading Partners : USA, EU countries, China, India, Pakistan, Japan, South Korea, Canada, Australia, Malaysia, Hong Kong, Taiwan, Thailand, Indonesia, Saudi Arabia and UAE Country Profile
  4. 4. The existing studies (Hassani & Zhigljavsky, 2010; Ayodeji, 2009; Maya & Gomez, 2008; Pesaran & Pesaran, 2007; Longmore & Robison, 2004) on the forecasting exchange rate have mostly based on advanced currencies. The combination of these two factors has led to a plethora of work on US$/BDT exchange rate. Motivation There is the observation that like all other financial markets but perhaps to an even greater degree, the market for foreign exchange has large temporal variations in volatility.
  5. 5. Objective of the study To investigate which model (ARIMA, exponential smoothing or naive 1) is superior in terms of predictive power of US$/BDT exchange rates.
  6. 6. Data • Source: International Financial Statistics (IFS), monthly publication by the IMF. • Duration : From 1972 till to date (From 1973M1 up to and including 2007M12 will be used for model quantification and statistical verification. The remaining three years i.e. 2008M1 to 2010M4 observations in the sample held back for the purpose of out-of-sample forecast evaluation). • Frequency: Monthly • Sample size : 432
  7. 7. • ARIMA (Autoregressive Integrated Moving Average): Autoregressive – future values depend on previous values of the data Moving average – future values depend on previous values of the errors Integrated – refers to differencing the data • Exponential smoothing : Forecast this month equals last month’s forecast plus a proportion of the forecast error last month. • Naive 1 or no change model : The Naїve1 or no change model assumes that a forecast of a series at a particular period equals the actual value at the last period available Methods • MAPE (Mean absolute percentage error) has been used as a measure of forecasting accuracy because this accuracy criterion has the advantage of being measured in unit-free terms (Witt & Witt, 1991).
  8. 8. A plot of 1st difference of US$/BDT exchange rate over time US$/BDT exchange rate over time Results and discussions
  9. 9. **H0: The possess a unit root / the data are not trend stationary Currency Test for unit root in - ADF* PP* Ng. Perron** US $/BDT Level 0.9951 0.9941 1.82970 1st Difference 0.0000 0.0000 -202.18 *Sig at < 0.05, ** MZa value (– 8.100, at 5% level) Unit root test result
  10. 10. Model Constant MA(1) (0,1,1) (0,0,0) with constant t statistics Significance 0.142 5.751 0.000 -0.103 -2.152 0.032 Exponential smoothing ARIMA model Model 𝛂 γ δ Winters’ additive 1.000 0.001 0.001
  11. 11. Comparison of models US$/BDT ARIMA Exponential Smoothing Naïve 1 Model (0,1,1) (0,0,0) with constant Winters’ additive No change model MAPE 1.031% 1.054% 6.1571% Ranking 1 2 3
  12. 12. A plot of observed and forecasted of US$/BDT exchange rate over time
  13. 13. • The main contribution of this study is evaluating the forecast performance of the various time series models in a comprehensive and systematic way. • Empirical results in this study will also pave the way for future research. Contributions
  14. 14. • It has been observed in the literature that several models are widely used by academics and practitioners to forecast exchange rate. • Nowadays there is no consensus about which method is superior in terms of forecasting accuracy (Poon and Granger, 2003; Taylor, 2005; Andersen et al., 2006). Some authors conclude that time series forecasting models are superior (Engle, 1982; Bollerslev, 1986). Other studies (i.e. Benavides & Capistran, 2009) argue that the combination of the models can yield better results. Thus, the future study will be concentrate on “combination forecasts” method instead of forecasts made by individual model. Limitations and further work
  15. 15. Thank you all.