Ikhlaas Gurrib

This study investigates if the biggest players in major foreign currencies futures markets are affected by current and previous financial conditions. Using root mean squared errors (RMSE), normalized RMSE, and Nash-Sutcliffe efficiency, this study compares the impact of current, 1 and 2 week lags of financial conditions onto foreign currency futures players’ net positions. The financial conditions indices used are UFCI, STLFSI, NFCI and ANFCI with weekly data set from January 2007 till December 2018. The US dollar index futures is included as a benchmark, since the financial conditions are based on US data and the most actively traded foreign currencies are paired against the USD. While RMSE and NRMSE gave mixed results into how current, 1 week and 2 weeks lagged Financial Conditions Indices (FCIs) values are related to speculators and hedgers’ net positions, lagged NFCI captured the highest correlation with both players’ net positions in Japanese Yen. 95% prediction levels encompassed the actual net positions held, including the financial crisis of 2008-2009. Forecasts were lower (higher) for hedgers (speculators) than actual net positions held during the same period. Comparatively, in the period 2016-2017, hedgers (speculators) net positions forecasts were higher (lower) than actual positions. The latter could be explained by FCIs not being affected during this period’s event, compared to net positions. While net positions data were stationary, excess kurtosis was present pointing to non-normal and autocorrelated series. This suggests the need to look into other components like non-reportable long or short positions in future analysis.

The aim of this study is to test a trading system based on the average directional index, which is complemented with the parabolic stop and reverse indicator. The trend-based system is tested onto the most actively traded USD based foreign currency pairs, using both monthly and weekly data set over 2000–2018. Sharpe and Sortino measures are used to track the performance of the currency pairs, based on total risk and downside risk assumptions. Results are robust tested by decomposing the data into pre and post 2008 financial crisis. Using an investment horizon over 18 years, the reliance upon the monthly model produced lower maximum drawdowns and lesser trades than the weekly model. While Swiss Franc had the best (worse) performance in the monthly (weekly) based model, the Chinese Renminbi witnessed the worse (best) performance in the monthly (weekly) based model. Pre and post financial crisis decompositions suggest the weekly-based system is more reliable than the monthly one with relatively more trades and positive performance, where the Chinese Renminbi and Japanese Yen posted the highest Sharpe and Sortino values of 0.996 and 4.452 respectively in the post crisis period. Proportionately high level of negative returns coupled with relatively low positive Sharpe and Sortino values, however, suggest that a trading system relying on the average directional index and parabolic stop and reverse indicator to be further tested and analyzed at higher frequencies.

This paper sheds light on the relationship between the Nasdaq Composite Index and a newly proposed Energy Futures Conditions Index (EFCI). While various financial conditions indices provide information about the financial stability of a country, the existence of an energy condition index, using futures markets, is scarce. Using weekly data over the period 1992–2017, this paper introduces an energy futures index using principal component analysis and test its predictability over the Nasdaq Composite Index. The EFCI captures 95% of the variability inherent in crude oil, heating oil and natural gas futures’ total reportable positions. Stability in forecast errors over different lags suggests a one week lag is sufficient to forecast weekly Nasdaq Composite Index. 95% prediction levels support that the estimated model captures actual equity market index values, except for the 2000 technology bubble. Distributions of level data were non-normal, not serially correlated and homoscedastic under the whole sample period, with diagnostics on pre and post technology bubble crisis showing mixed results. While differencing ensured homoscedastic errors in the forecasting model, Granger causality supported non-causality from both energy futures and equity markets, suggesting no evidence of cross market information flows.