Khurshid M. Kiani

Forecast Performance of Neural Networks and Business Cycle Asymmetries

Forecast performance of artificial neural network models are investigated using Ashley et al. (1980) and the neural network nonlinearity test proposed by Lee et al. (1993) is employed to find possible existence of business cycle asymmetries in Canada, France, Japan, UK and USA real GDP growth rates. The results show that neural network models are more accurate than linear models for in-sample forecasts. However, when comparing the out-of-sample, linear models performed better than neural network models in all series. Results from neural network tests show that business cycle asymmetries do prevail in all the series.

Forecasting Forward Exchange Rate Risk Premium in Singapore Dollar/US Dollar Exchange Rate Market

In this research, monthly forward exchange rates are evaluated for possible existence of time varying risk premia in Singapore forward foreign exchange rates against US dollar. The time varying risk premia in Singapore dollar is modeled using non-Gaussian signal plus noise models that encompass non-normality and time varying volatility. The results from signal plus noise models show statistically significant evidence of time varying risk premium in Singapore forward exchange rates although we failed to reject the hypotheses of no risk premium in the series. The results from Gaussian versions of these models are not much different and are in line with Wolff (1987) who also used the same methodology in Gaussian settings. Our results show statistically significant evidence of volatility clustering in Singapore forward exchange rates. The results from Gaussian signal plus noise models also show statistically significant evidence of volatility clustering and non-normality in Singapore forward foreign exchange rates. Additional tests on the series show that exclusion of conditional heteroskedasticity from the signal plus noise models leads to false statistical inferences.

Financial Factors and Financial Crises: Evidence from Financial Statements of Mainland Chinese Firms

We work with financial factors in conjunction with the macro-financial variables of interest to study the behavior of these factors in terms of their relationship, importance, and significance with firm growth in China during and around the 1997 Asian financial crisis and the 2008 global financial crisis. Our results show that the financial factors portray identical behavior during the 1997 Asian financial crisis as well as during the 2008 global financial crisis. Toward the end of the each of these financial crises, firm size became more important and positively related to firm growth revealing that investors became more cautious about the safety of their investments rather than high returns. The results also show that variation in growth rates in economic activity is stronger than that of sales growth rates that are determined by the financial factors and the other macro-financial variables of interest. Thus, our results show that China is not expected to encounter a financial crisis of the form of the recent financial crises.

No Predictable Components in G7 Stock Returns

We search for time-varying predictable components in monthly excess stock index returns over the risk free rates in the G7 countries. The predictable components provide an estimate of the expected excess returns. Our unobserved components model improves on Conrad and Kaul (1988) by taking into account fat tails widely documented in returns data. Statistical hypotheses tests fail to reveal any significant time-varying predictable components in excess returns for any of the countries, except Canada. Our results are in sharp contrast to Conrad and Kaul (1988), who do isolate time-varying expected returns in weekly size-weighted portfolio returns using the same methodology but in a Gaussian setting.