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Of all publications in the section: 4
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Working paper
Peresetsky A., Yakubov R. MPRA Paper. University Library of Munich, Germany, 2015. No.  64579.
In this paper a Kalman-filter type model is used to extract a global stochastic trend from discrete nonsynchronous data on daily stock market index returns from different markets. The model allows for the autocorrelation in the global stochastic trend, which means that its increments are predictable. It does not necessarily mean the predictability of market returns, since the global trend is unobservable. The performance of the model for the forecast of market returns is explored for three markets: Japan, UK, US.
Added: Jun 21, 2015
Working paper
Grigoryeva L., Ortega J., Peresetsky A. MPRA Paper. University Library of Munich, Germany, 2015. No.  64503.
This paper introduces a method based on the use of various linear and nonlinear state space models that uses non-synchronous data to extract global stochastic financial trends (GST). These models are specifically constructed to take advantage of the intraday arrival of closing information coming from different international markets in order to improve the quality of volatility description and forecasting performances. A set of three major asynchronous international stock market indices is used in order to empirically show that this forecasting scheme is capable of significant performance improvements when compared with those obtained with standard models like the dynamic conditional correlation (DCC) family.
Added: Jun 21, 2015
Working paper
Peresetsky A. MPRA Paper. University Library of Munich, Germany, 2011. No. 41508.
In this paper we empirically test the dependence of the Russian stock market on the world stock market, world oil prices and Russian political and economic news during the period 2001-2010. We find that oil prices are not significant after 2006, the Japan stock index is significant over the whole period, since it is the nearest market index in terms of closing time to the Russian stock index. We find that political news like Yukos arrests or news on the Georgian war have a short term impact, since there are many other shocks, the structural instability of the Russian financial market is confirmed.
Added: Mar 16, 2013
Working paper
Peresetsky A. MPRA Paper. University Library of Munich, Germany, 2011. No. 41507.
The binary and multinomial logit models are applied for prediction of the Russian banks defaults (license withdrawals) using data from bank balance sheets and macroeconomic indicators. Significantly different models correspond to the two main grounds for license withdrawal: financial insolvency and money laundering. Analysis of data for the period 2005.2–2008.4 for accurate prediction of a bank’s financial insolvency, which is the focus of interest for the Russian Deposit Insurance Agency, demonstrates that the multinomial model doesn’t outperform the binary model.
Added: Mar 16, 2013