The recent political turmoil in Crimea and the resulting trade sanctions led to reports of significant movements in the Russian Rouble. The news prompted us to review the history of one-year foreign exchange rate movements for the Rouble along with a number of the high-growth economies to see whether they had proved more volatile on a one-year basis than the developed currencies most insurer assets and liabilities are in.
The chart below shows summary statistics for the one-year movements in the currency against the US dollar for each of the BRICS1 and MINT2 economies. Plotted alongside is how Sterling has behaved over a 10-year and 40-year period.
We can see a remarkable variety of volatilities in the various exchange rates. The Brazilian Real and South African Rand have been the most volatile of the currencies considered. Other countries, such as China and Nigeria, have sought to fix their exchange rates. While China has been particularly successful, Nigeria has only been able to constrain the interquartile range of movements. Interestingly, the Sterling exchange rate does not appear to be less volatile those of our basket of emerging economies, in fact it has the third highest of the 99.5th percentiles.
While probably not material components of investment portfolios, we expect a continuing rapid increase in general insurance liability exposure to these economies over the next decade. Firms should therefore start to think about how their actuarial and risk modelling and validation of business decisions need to evolve in response.
The data plotted are mean, median, interquartile range, 0.5th percentile, 99.5th percentile and extremes of the ratios of the spot exchange rate each day to what it was a year previously, so that 100% represents no movement in the exchange rates. Values above 100% indicate a relative weakening of the emerging market currency. A log scale is used so that movements are treated symmetrically. The spot exchange rates are taken from days between the beginning of 2005 and the end of March 2014. The data consist of 1551 observations for each currency, with the exception of the Sterling from 1976, which contains 9681 data points.
1BRICS: Brazil, Russia, India, China and South Africa.
2MINT: Mexico, Indonesia, Nigeria and Turkey.