3:Correlates of private capital flows.
Note: Volatility diff is RTS volatility minus S&P volatility and Return diff is S&P return minus RTS return, so both coefficients are expected to be positive.
The regression results are quite interesting. The most statistically significant variable is the volatility on the Russian market, which has the expected positive sign that indicates that increased volatility increases net capital outflows. The other statistically significant variable is returns in the US market, but there is no offsetting effect from returns in the Russian market. The oil price variables are also not significant, which is perhaps a bit surprising given their importance for growth and investments. However, it could be the case that high oil prices both generate foreign exchange earnings in Russia that could leave the country as capital flows and encourage inflows into the Russian economy, and this estimate reflects that these two forces cancel each other out.
In principle, the relative volatility and return between the domestic and foreign market should matter for flows, and if the regression is run on these variables instead, the importance of volatility is further enhanced while the return variable becomes statistically insignificant. However, the overall explanatory power of such a regression is greatly reduced and is the reason the more detailed specification discussed earlier is preferred. The exact causal links and mechanisms cannot be investigated fully in this setting since there may be an effect going from capital flows from Russia to volatility in the Russian stock market. In the end, however, it is clear that volatility is an important correlate of capital flows that warrant a closer look.
Determinants of Returns and Volatility
The next item to investigate is how returns and uncertainty in the Russian stock market have developed and to what extent this can be understood by external and domestic factors. Again, the stock market here is viewed as a way to measure returns and uncertainty more broadly that would be correlated with capital flows, investments and likely also consumer confidence (which is not analysed further here but is an important demand side factor for growth). There are several factors that we can expect will affect returns and volatility on the Russian stock market. First, stock markets today are linked globally, and the developments on global markets are captured by the US market’s S&P500 index. We also know that many of the companies on the Russian stock market are linked to the energy sector, and therefore, international oil prices should matter for the valuation of the RTS. The S&P500 and Brent oil price are exogenous factors, so we can run a regression explaining variation in the return and volatility of the RTS with these variables as explanatory variables.
Table 4:Correlates of stock market returns and volatility.
Source: Author’s estimate based on market data.
Table 4 confirms that US stock market returns and changes in oil prices have a significant impact on returns in the Russian market. The estimation shows that coefficients are quite robust to estimating the relationship since the start of the RTS index in 1995 or focusing on the years after the global financial crisis.10 In the case of returns, the lags of US returns and oil price changes are significant, which is somewhat contrary to regular arguments about efficient markets that would immediately include all new information. The reasons for this apparent anomaly could include rather mechanical explanations such that the markets are located in different time zones, to market frictions that would lead to a somewhat delayed response.11 The coefficients on the lags are slightly smaller in the more recent years, which could be a result of reduced frictions, but the coefficients are still highly significant in both samples.
For volatility in the Russian market, the volatility in the US market and the volatility in oil prices are also highly significant and together explain about a third of the Russian volatility. The coefficients are again stable across the two samples and do not indicate a structural break in the relationship between the earlier and later time period. Note that the full set of explanatory variables that are included in the table were allowed to enter the first set of regressions, but insignificant variables were omitted from the final estimation to generate robust models from which we can compute residuals in the next stage.
The residuals computed from the estimated model mentioned earlier show the returns and volatility in the RTS that are unexplained by the external factors that are included in the model. This would thus include both domestic and foreign policy events that are not captured by changes in the US market or oil prices. Of course, the residuals will also include company-specific factors that influence the expected performance of the Russian stock market that we would not think of as Russian domestic or foreign policy events. For this reason, the residuals are noisy signals of these factors, but we can still use the residuals to look at what happens in the market at times when we know there are important policy events taking place and we have at least filtered out two important external sources of variation in the Russian market.
The residual (or excess) returns and volatility are shown in Figure 7. It is clear that the early years of transition were more volatile in the stock market as well, but at around the new millennium, volatility went down. However, this relative calm was then interrupted with the global financial crisis and then again in 2014. Since this chapter is about macroeconomic developments during the reign of Putin, we will investigate what events have coincided with a large movement in the stock market since 2000.
Figure 7:Excess returns and volatility.
Source: Author’s calculations based on data and estimations in Table 4.
In order to select the events to investigate further, we focus on days where the residuals are unusually large and volatility is at extreme levels. In Figure 8, this is defined as negative daily returns of more than 5% and a rolling 20-day daily return volatility of more than 2%. In terms of number of days with negative returns, years 2000 and 2008 stand out. Both years were associated with major events in global financial markets (dot-com crash and global financial crisis), while in 2000, Putin was elected president for the first time and Russia was fighting a war in Chechnya. As for years with high volatility, 2000 and 2008 are again high on the list, but so are 2003 and 2014 (and 2015). In 2003, there was the Yukos affair and trial of its owner Mikhail Khodorkovsky, and in 2014/2015 there was the annexation of Crimea, involvement in Eastern Ukraine and long list of sanctions and counter-sanctions between Russia and the West. For sure, a significant amount of volatility in the Russian market is due to external events, but an even greater amount of volatility is home-made by Russian domestic and foreign policy decisions during Putin’s term in office. It is again important to note that volatility plays a key role around the home-made events, so studies that simply focus on the impact on returns and absolute levels of capital flows may miss a significant part of the effect these events will have on investments and future growth.
The observations from Figure 8 can be complemented by a listing of the most negative days on the Russian market and the days with the highest volatility. If we construct a top-20 list of the days with the most negative returns since Putin became president, 2008 stands out with 9 of the 20 days with the stock market falling by 16% on the worst day as Russia was hit by the global financial crisis. The year 2014 accounted for 3 of the 20 worst days with
