Forecasting Coherent Volatility Breakouts (with M.Dubovikov, B.Poutko)

The paper develops an algorithm for making long-term (up to three months ahead) predictions of volatility reversals based on long memory properties of financial time series. The approach for computing fractal dimension using sequence of the minimal covers with decreasing scale is used to decompose volatility into two dynamic components: specific and structural. We introduce two separate models for both, based on different principles and capable of catching long uptrends in volatility. To test statistical significance of its abilities we introduce several estimators of conditional and unconditional probabilities of reversals in observed and predicted dynamic components of volatility. Our results could be used for forecasting points of market transition to an unstable state.


Model of FX rate volatility, based on fractal features of financial time series (B.Poutko, A.Didenko, M.Dubovikov.)

Paper develops volatility forecasting model of RUR/USD exchange rate. To forecast volatility we decompose it to components, characterizing fractal structure of financial time series. Using regression analysis we confirm quasi-cyclical time structure for one of the fractal parameter. We then discuss capacity of the method to predict volatility, including forecasting market transition to unsteady state.

(Applied Econometrics, vol. 36(4), pages 79-87R code and dataset is available upon request: alexander.didenko (at) or from my profile).

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DEA-based rating of pension fund managers (E.Fedorova, A.Didenko)

Authors develop DEA model for assessing investment performance and calculate quarterly efficiency scores for 46 fund managers from Russian Federation, covering period 2004–2012. Subsequent analysis shows that higher scores come through low net asset value, high level of risk premiums and low level of expected losses. Key impacts to efficiency change are the level of diversification, relative market value of assets, and the age of management company.

(Published in Russian in “Economic Analysis: Theory and Practice” 40 (391), p.48-57)

Innovations as factor of absorptive capacity of FDI spillovers across regions of Russian Federation (A.Didenko, T.Egorova)

We study how innovations affect increase of regional TFP as a result of productivity spillovers from FDI, and confirm the presence of phenomenon in Russian data. We model TFP using DEA with the human capital, energy and and capital as inputs and the gross regional product as output. We develop innovations index for the regions of the RF, which proxies for regional absorptive capacity, based on 17 variables, characterizing economic, social and infrastructural aspects of regional development. FDI is measured as the the sum of ratios of sales of firms with FDI to the total sales in the region times squared distance to neighboring regions.

(Published in Review of Business and Economic Sciences, 2(3), September 2014)

Productivity Spillovers in the Russian Federation: The Case of the Chemical Market (A.Kuzyaeva, A.Didenko)

Foreign direct investment (FDI) and international trade are suggested to be major conduits of international technology transfer. The present paper aims to extend the current empirical literature by determining the effect and the source of productivity spillover in Russia in case of chemical industry. In order to find out the existence of FDI and trade productivity spillover we applied Ericson and Pakes (1995) and Olley and Pakes (1996) methodology. We estimated the model model for companies from chemical industry for the period 2007-2012. Our results confirm FDI and trade productivity spillovers in Russian chemical industry. The size of FDI spillovers is more important than imports-related spillovers. Based on the empirical results, we may predict that Russia’s accession to the World Trade Organization in 2012 should result in productivity growth. However, further research on this topic will be possible when the statistical data becomes available for several years after accession.

(Published in Review of Business and Economic Sciences, 2(3), September 2014)

Using constant volume scale for modeling fractal characteristics of financial time series (A.Didenko, M.Dubovkiov, B.Poutko)

We propose to use intrinsic time scale based on volume when measuring fractal dimension of financial time series. (Dubovikov, 2004) introduces a new method of measuring fractal dimension which is superior to other methods, including well-known Hurst index in terms of  speed of asymptotic. As a downside, estimates obtained with new method, are noisy and hard to predict, which in turn complicates its use in practice. We demonstrate that sampling time-series across volume scale, instead traditional physical time scale, could significantly improve predictability of fractal dimension.

(Published in Russian in “Science in Modern Information Society IV” , Fall 2014, ISBN 978-1-50232-179-4)

What impacts efficiency of pension fund managers in Russia (E.Fedorova, A.Didenko, D.Sedykh.)

We use data envelopment analysis (DEA) to assess efficiency of 46 pension fund managers in Russia during 2004-2012. Our DEA model represents pension fund portfolio as decision making unit transforming risk, human and financial capital to active return and quality of diversification. We find that the highest impact to efficiency of pension funds have stock market returns, while interest rates, corporate debts, FX rate have lower impact, and energy prices have no impact at all. Bigger and more mature portfolios with higher share of equities and cash would have lower returns. Seasonal factor impact is also high with third quarter being the toughest for managers. We explain it by scale effects and constraints implied by funds’ investment declarations.

(Published in Russian in «Financial analytics: science and experience», ISSN 2073-4484, issue No. 33(219) – 2014 September)

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