Category Financial Econometrics and Empirical Market Microstructure

Dynamic of the Rating Agencies Activities in Russia

The growth of the number of Russian agencies ratings has been significant in recent years. Four Russian rating agencies achieved registration in the Russian Ministry of Finance as well as three international ones. Due to this fact, the question of the integration of these agencies’ efforts and comparison of their rating scales is important. As for now we have nearly 700 ratings for banks only. We observed a threefold growth in 5 years (2006-2011). We also see that the number of ratings given by Russian agencies is roughly similar to the international agencies’ ratings (Kaminsky et al. 2011b).

Despite the comparative growth in the number of ratings, the rating methods are largely unclear, and expertise plays a significant role...

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Volatility is not Risk

When asking investors whether they would prefer high or low volatility investments, most opt for low. Few can tolerate the turbulence of volatility. But low volatility only means low visible risk. What if we put it this way: “Would you prefer low volatility with the possibility of large hidden risk? Or high volatility, but at least what you see is what you get?” Despite record low volatility, early 2007 was the most risky time to be invested. And March 2009 presented exceptional opportunity for returns, despite record short term volatility. As Knut Kjaer observes: “The (future) reward for risk may be at the highest when the market sentiment for risk taking is at the lowest.”

The implication is that as volatility declines, our priority should shift to iden­tifying hidden structural ...

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The Results of Evaluation on Generated Data

A suggested method of detecting and estimation of structural breaks in a time series was used on a generated time series with a specified pattern of dependence.

For analysis we can use six types of copulas, described in Table 1. Dependence from lagged value was generated at different levels, corresponding to Kendall’s rank correlations —0.8, —0.6, —0.4, —0.2, 0, 0.2, 0.4, 0.6, 0.8. Since not all copulas can describe all of the above levels of dependence, we only used 34 copulas.


Value of rank correlation


-0.8, -0.6, -0.4, -0.2, 0.2, 0.4, 0.6, 0.8


-0.8, -0.6, -0.4, -0.2, 0.2, 0.4, 0.6, 0.8


0, 0.2, 0.4, 0.6, 0.8




-0.2, 0, 0.2


-0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8

Table 1 Generat...

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The Risk Factor Interrelation Model

The dependence structure of risk factors was described by a t-copula (Genest et al. 2009). Copulas were used instead of the well-known Pearson’s linear correlation because the latter one has many drawbacks (Schimdt 2006) such as:

• It is impossible to capture the full dependency composition of risk factors;

• If the correlation is equal to zero it does not mean that the factors are independent;

• It does not work correctly for distributions with heavy tails because it supposes that risk factor variances are finite (which contradicts the empirical data).

The maximum likelihood method was used to estimate the parameters of the t – copula (Charpentier 2006). Historical data for stochastic component St of the AR(1)- GARCH(1,1) model error was used as a sample for this estimation...

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On the Modeling of Financial Time Series

Aleksey Kutergin and Vladimir Filimonov

Abstract This paper discusses issues related to modeling of financial time series. We discuss so-called empirical “stylized facts” of real price time-series and the evolution of financial models from trivial random walk introduced by Louis Bachelier in 1900 to modern multifractal models, that nowadays are the most parsimonious and flexible models of stochastic volatility. We focus on a partic­ular model of Multifractal Random Walk (MRW), which is the only continuous stochastic stationary causal process with exact multifractal properties and Gaussian infinitesimal increments. The paper presents a method of numerical simulation of realizations of MRW using the Circulant Embedding Method and discuss methods of its calibration.

Keywords Circulant Em...

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AIG Case Study Using FNA HeavyTails™ Outlier Monitor

FNA’s recently launched HeavyTails application is network implementation of the Adaptive Stress Testing framework. The initial focus of HeavyTails is to monitor global market outliers.[25]

AIG’s collapse during the U. S. subprime crisis is a classic early warning case study. As policy makers wrestled with the implosion of Lehman Brothers, they were blindsided by AIG, which would require a record $182 bn bailout. Policy makers realized AIG was on the precipice only “days before its imminent collapse” recounts Phil Angelides, Chair, Financial Crisis Inquiry Commission.

When analyzing the unusual price action of AIG stock, it becomes evident that major market participants suspected AIG’s precarious state for many months...

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