Zaheer Abbas

Comparing Predictive Power of Exchange Rate Models in Emerging Asian Economies

In this paper, we have compared forecasting performance of three economic and two autoregressive models of exchange rate in five Asian economies; namely Pakistan, India, Indonesia, Korea and Sri Lanka. Models include purchasing power parity (PPP), interest rate parity (IRP), adhoc model, random walk model (RWM) and autoregressive integrated moving average (ARIMA) model. To compare the predictive capacity of these models, four statistical measures, namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Median of Absolute Deviation (MAD) and Success Ratio (SR) have been used. Our results document that models based on economic fundamentals do not outperform random walk and ARIMA in all the sample economies. If the effect of outliers is controlled, then adhoc model defeat single variable based parities i.e interest rate pairty and purchasing power parity. economic models have better predictive capacity except in case of Indonesian Rupiah, where random walk model defeat economic models even after controlling the effect of outliers. RMSE, MAE and MAD favor economic models. However, SR reports different results in different economies. In Pakistan, India and Korea, it gives vote to IRP while in Indonesia and Sri Lanka, it favors random walk model.

How investor sentiments spillover from developed countries to developing countries

Abstract This paper applies a sentiment index to check the influence of regional developed countries like Japan and Germany on the sentiments in regional developing countries like Pakistan and Turkey, respectively. The index has been created using a principal component approach with modified proxies. The results indicate that there is a significant influence of developed markets on developing markets also the sentiment index created in this study is good indicator regarding the return pattern of the stock exchange. This study has followed the footsteps of previous studies for methodology and the time period which is used ranged from 29 December 2004 to 31 December 2014. The weekly data has been collected with Wednesday prices so to avoid the white noise, start-of-the week and end-of-the week effect.