Borochkin A.A.National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation borochkin@yandex.ru
Importance Researching the market predictability enables to compare profits of speculative trading and return on alternative investment and estimate the imbalance in the financial system. Objectives The purpose of the study is to offer an approach to quantify the level of predictability of the Russian foreign exchange market. Methods Preliminary analysis of data rests on descriptive statistical methods. To describe the influence of rare events on foreign exchange rate, I apply the case study method. Trading strategies for the Russian FX market are developed based on generalized autoregressive conditional heteroskedasticity (GARCH) models. Results The Russian currency market is predictable mostly during crisis periods. The market predictability was the lowest in the period of high and growing oil prices, and has tended to increase over ten recent years. Conclusions Mega-regulator can decrease the predictability of the Russian currency market and prevent speculation on market volatility by reducing the number of rare events that cause sharp one-off changes in currency quotations.
Pilipenko Z.A. [Price shocks in the world commodity markets under reforming the elements of the global economic system]. Ekonomicheskie nauki = Economic Sciences, 2015, no. 123, pp. 67‒71. (In Russ.)
Polyakov E.N. [An empirical analysis of capital account impact on balance sheets of the banking system and the corporate sector (a stress-test for the Russian economy's reaction to external shocks]. Sovremennye issledovaniya sotsial'nykh problem, 2013, no. 6. (In Russ.) Available at: Link.
Della Corte P., Ramadorai T., Sarno L. Volatility Risk Premia and Exchange Rate Predictability. Journal of Financial Economics, 2016, vol. 120, iss. 1, pp. 21‒40.
Lomivorotov R.V. [The impact of external shocks and monetary policy on the Russian economy]. Voprosy Ekonomiki, 2014, no. 11, pp. 122‒139. (In Russ.)
Minakir P.A. [Shocks and institutions: Paradoxes of Russian crisis]. Prostranstvennaya ekonomika = Spacial Economics, 2016, no. 1, pp. 7‒13. (In Russ.)
Andryushin S.A. [Argument in favor of the Russian ruble exchange rate management]. Voprosy Ekonomiki, 2015, no. 12, pp. 51‒68. (In Russ.)
Didenko A., Dubovikov M., Poutko B. Forecasting Coherent Volatility Breakouts. Vestnik Finansovogo universiteta = Bulletin of Financial University, 2015, no. 1, pp. 30‒36.
Sidorov S.P., Date P., Balash V.A. et al. [Stock Volatility Modeling Using an Augmented GARCH Model with Jumps]. Matematicheskoe modelirovanie v ekonomike, strakhovanii i upravlenii riskami. Mezhdunarodnaya molodezhnaya nauchno-prakticheskaya konferentsiya [Proc. Sci. Conf. Mathematical Modeling in Economics, Insurance and Risk Management]. Saratov, SSU Publ., 2013, pp. 13–20.
Zinenko A.V. [Assessing the impact of political and economic events on the MICEX index using a GARCH-model]. Vestnik Krasnoyarskogo gosudarstvennogo agrarnogo universiteta = Bulletin of KrasSAU, 2014, no. 6, pp. 9‒16. (In Russ.)
Borusyak K.K. [Non-linear dynamics of the Russian stock market in risk management tasks]. Zhurnal novoi ekonomicheskoi assotsiatsii = Journal of the New Economic Association, 2011, no. 11, pp. 85–106. (In Russ.)
Kirillov K.V. [Modeling the stock exchange movements during low and high volatility periods]. Vestnik Donskogo gosudarstvennogo tekhnicheskogo universiteta = Vestnik of DSTU, 2013, vol. 13, no. 7-8, pp. 5‒14. (In Russ.)
Karadzhich V., Tserovich Yu. [Market risk for Western Balkans countries during the world financial crisis]. Економiчний часопис-XXI, 2014, no. 11-12, pp. 19‒23. (In Russ.)
Kisilevskii S.O. [GARCH process modeling (1.1) to analyze volatility under international diversification of portfolio of shares]. Servis v Rossii i za rubezhom = Service in Russia and Abroad, 2011, no. 4, pp. 64‒69. (In Russ.)
Bollerslev T. Glossary to ARCH (GARCH). Available at: Link.
Glosten L.R., Jagannathan R., Runkle D.E. On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 1993, vol. 48, no. 5, pp. 1779‒1801.
Al-Khazali O.M., Pyun C.S., Kim D. Are Exchange Rate Movements Predictable in Asia-Pacific Markets? Evidence of Random Walk and Martingale Difference Processes. International Review of Economics & Finance, 2012, vol. 21, no. 1, pp. 221‒231.
Al-Khazali O.M., Leduc G., Pyun C.S. Market Efficiency of Floating Exchange Rate Systems: Some Evidence from Pacific-Asian Countries. Global Finance Journal, 2011, vol. 22, no. 2, pp. 154‒168.
Charles A., Darné O., Kim J.H. Exchange-Rate Return Predictability and the Adaptive Markets Hypothesis: Evidence from Major Foreign Exchange Rates. Journal of International Money and Finance, 2012, vol. 31, no. 6, pp. 1607‒1626.
Luukka P., Pätäri E., Fedorova E., Garanina T. Performance of Moving Average Trading Rules in a Volatile Stock Market: The Russian Evidence. Emerging Markets Finance and Trade, 2016, vol. 52, iss. 10, pp. 1‒17.
