Fluke of stochastic volatility versus GARCH inevitability or which model creates better forecasts?
The paper proposes the thorough investigation of in-sample and out-of-sample performance of five GARCH and two stochastic volatility models, estimated on the Russian financial data. The data includes prices of Aeroflot and Gazprom stocks and Ruble against US dollar exchange rates. In our analysis we use probability integral transform for in-sample comparison and Mincer-Zarnowitz regression along with classical forecast performance measures for out-of-sample comparison. Studying both the explanatory and the forecasting power of the considered models we came to the conclusion that stochastic volatility models perform equally or in some cases better than GARCH models.
This paper reviews difficulties concerning a development of single-name CDS price (spread) dynamics model for the purpose of determination of margin requirements. It also discusses a possibility to construct such a model using information about respective equity prices and option implied volatilities. Finally, it presents the basic step towards the former idea demonstrating results for the CDS written on Gazprom senior debt.
The paper aims at finding the most accurate VaR model for the four most liquid Russian stocks. Among the possible VaR modeling techniques, the estimates considered in this work are based on GARCH models with six different distributions. A back testing analysis is performed to evaluate the accuracy of the alternative models and to find the worst predictable period in terms of the volatility behavior.
Hedging is one of the most popular strategies for market risk management. Hedging is aimed at decreasing the volatility, or variability, of portfolio returns. The portfolio usually consists of the spot assets and hedging instruments. The latter can be represented by futures, options and over-the-counter assets such as forwards and swaps. While futures’ hedging is rather simple it’s quite widespread in practice. This paper is aimed at comparison of four hedging strategies, where the spot asset is stock and hedging instrument is futures. For this purpose five Russian stocks from Moscow Exchange are selected and analyzed for the period from the 1st of December 2015 till the 29th of February 2016.
The key element of the hedging strategy is the calculation of the hedging coefficient. The latter shows what part of the stocks’ value in the portfolio should be covered by futures. In this paper the hedging coefficient is computed through internal rate of return, ordinary least squares (OLS) and maximum likelihood. The latter is able to estimate hedging coefficient taking into account heteroskedasticity, because the regression errors follow GARCH model. Further hedging strategies are compared by such criteria as standard deviation of portfolio returns, portfolio Value-at-Risk and hedging efficiency.
According to the results the most efficient strategy is one based on internal rate of returns. The other criteria show that the same strategy together with OLS demonstrates better results. Correction for heteroskedasticity made through maximum likelihood did not allow improving hedging efficiency.
The research can be extended in the several directions, namely considering options’ hedging; adding to the portfolio other spot assets, for example, commodities and currencies; taking into account investors’ risk aversion in the calculations of hedging coefficients; introducing transaction costs in the model.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.