Category Financial Econometrics and Empirical Market Microstructure

Rating Comparisons in the Literature

Among the first papers aimed to compare the ratings of different agencies was the one by Beattie and Searle (1992). Long-term credit ratings were gathered from 12 international credit rating agencies (CRA) that used similar scales. The sample of differences between the pairs of ratings for the same issuer was found. Around 20 % of the pairs in that sample involved differences in excess of two gradations. That may be explained by differing opinions about the financial stability of the issuers, as well as by different methodologies used by the rating agencies. But the average difference between ratings of the main international agencies S&P and Moody’s was insignificant.

Cantor and Packer (1994) compared Moody’s ratings of the international banks with the ratings of nine other rating agenc...

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The Destabilizing Effect of Stability

Hyman Minsky’s “Financial Instability Hypothesis” (1992) is often summarized as “stability breeds instability.” As Lawrence H. Meyer observed in Lessons from the Asian Crisis (1999): “a period of stability induces behavioral responses that erode margins of safety, reduce liquidity, raise cash flow commitments relative to income and profits, and raise the price of risky relative to safe assets-all combining to weaken the ability of the economy to withstand even modest adverse shocks.” In the case of the Asian Crisis, it was pegged currencies which allowed Asian banks and corporations to raise cheap USD financing. Financial imbalances built up, but did not register in the artificial low volatility of pegged currencies.[21] When the first FX tremors started in Thailand in May 19...

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Problem Statement

Following Brodsky et al. (2009), the problem of structural break detection could be formulated through nonparametric estimation of copulas applied to time series. In contrast to the original paper, this approach was stated for structural break detection in time series; the hypothesis about independence of multidimensional vectors of observations is not proven.

Let us look at a batch of observations of time series x1 … xN. Assuming

time series of the form AR(m) with nonlinear dependence structure of previous

observations, in any time of moment t = m + 1 … N, it can be assumed that

dependence from lagged values is defined through some continuous (m + 1)-

dimensional copula Ct(xt; xt_ 1; … xt_m). The problem of determining structural

break is that hypothesis H0: C2 = … = CN about the permanen...

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. The Risk Factors Evolution Model

To describe this evolution, the following AR(1)-GARCH(1,1) model (Posedel 2005) is applied for each risk factor:

rt = д C mr,_i + £

£t = atS,

Подпись: .2Подпись: -2 't _1 a,2 = ! C Pi£:_1 C «ia,

where rt—return at time t, д—basic value of return, £—model error, which is decomposed to St—stochastic component and at—conditional standard deviation at time t, ! —basic value of at.

The stochastic component of error St is often considered as a simple random variable with standard normal distribution. However from empirical data one can clearly see that this cannot be true, because it’s distribution usually has heavy tails.

In this paper we use Pareto distribution from extreme value theory to simulate this feature in the following way:

• The AR-GARCH model fitted onto historical returns gives historical valu...

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Simulation Results

Our numerical simulation includes the GM model, first-stage modification (the market-maker’s uncertainty about real asset value) and second-stage modification (simultaneous uncertainty of the market-maker and the informed trader about real asset value).

We made only one trial simulation of model modification to study changes in inventory risk, price, spread and the market-maker’s financial result.[8] This simulation is only the first attempt and we recognize the need for further simulations to test our results.

We performed the simulation according to the following conditions: V = 10$ (low value), V = 20$ (high value), 1 = 0.5 (starting possibility of V = V), д = 0.2 (share of informed traders), T = 1,000 (time periods)...

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Japan Case Study

We’ll use a recent Japan stress test case study to illustrate how this would work. As a consequence of massive quantitative easing, Japanese equities and bonds entered an exceptional bull market from 2012 to early 2013. By April 4, 2013 Japan was entering bubble territory with JGB yields reaching a historical low at 35 bp and with equities up 80 % over the last year. JGB downside (price) outliers then started escalating as Gold crashed on April 12 and 15. Then on May 23, after the Fed announced potential tapering of QE and after a lower than expected China PPP announcement, Japanese equities dropped by 7.32 % in 1 day. This was a classic early warning signal—after a long ebullient run tied to low interest rates, risk finally returned...

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