Category Financial Econometrics and Empirical Market Microstructure

Price Formation Mechanism

The aim to understand the price formation mechanism is not novel. It is well known that price process of any financial instrument follows a stochastic-like path: a price path can include or not a deterministic trend; but in any case the price process is smeared by noise movements. The noise movements are known as market volatility, and they make the price unpredictable. These noise movements can be decomposed into two components: the first component is called regular noise, it represents noise that is frequent but does not bring any abrupt changes, the second component is known as price jumps, it designates rare but very abrupt price movements...

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Building an Adaptive Stress Library

This insight that market intelligence is not evenly distributed drives the design of our Adaptive Stress Library to harness intelligence from Innovators and Early Adopters.

Innovators foresee potential risks that are imperceptive to most. As Frederic Bastiat recognized in 1850, this ability to foresee is what differentiates visionary and ordinary economists:

In the department of economy, an act, a habit, an institution, a law, gives birth not only to an effect, but to a series of effects. Of these effects, the first only is immediate; it manifests itself simultaneously with its cause – it is seen. The others unfold in succession – they are not seen: it is well for us, if they are foreseen...

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Setting VaR Limits Based on Portfolio Insurance and Quantile Hedging

These drawbacks are addressed in a dynamic model proposed by StraBberger (2002). In this model, the market risk of a stock portfolio[36] is managed through VaR limits in a continuous time. The underlying idea is a combination of portfolio insurance with synthetic put options (Rubinstein and Leland 1981) and “quantile hedging” (Follmer and Leukert 1999). As in the model by Beeck et al. (1999), the annual risk limit is defined as a maximum cumulative loss over a year, and is dynamically adjusted for the trader’s daily P&L. However, the annual risk limit is translated not into a daily VaR limit, but directly into a daily position limit using the daily VaR parameters.[37] The daily position limit is adjusted using a risk- aversion scalar (at) and, by construction, is equal to or smaller ...

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Monte-Carlo Simulation Schema

One of the key requirements for the stress-testing model is the ability to estimate changes in the rating structure of the portfolio over time. The most obvious approach for this task is to incorporate migration matrixes into the model. Due to the dependence of the rating migration dynamics on the economic cycle, it is recommended to use different migration matrixes for stress and expansion scenarios.

We propose the following Monte-Carlo simulation schema, which takes into account the proposed density function (3) and migration matrixes:

1. For the given macro-variable dynamics (from the macro-forecast) for the stress­testing period, conditional PDs are calculated [using (2)] for each rating class— Thsi.

2. The normal random variable Z is generated (systemic factor).


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Smoothing Data for Further Analysis and Preliminary Observations

In this work several microstructure variables were researched, including

• Stock return and price

• Price change and its absolute value

• Spread and relative spread (ratio of spread to price)

Due to systematic noise in microstructure data, it is necessary to smooth the data for further analysis. In this work, one of the modern wavelet methods was used. The basic principle of wavelet smoothing is performing wavelet decomposition and applying a “smoothing” transformation for wavelet coefficients for a certain threshold level. By looking at the smoothed trajectory of a variable, we can already discern whether its behavior is regular or not (Antoniou and Vorlow 2005)...

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Comparison of Ratings: Methods and Algorithms

The rating process has some problems, such as

• A relatively small number of updated communicative ratings.

• Difficulties of comparison of estimation between different rating agencies.

• Absence of any integrative effect from available competitive estimations of independent agencies.

• A demand for extended usage on independent rating estimations primarily owing to modeling techniques.

We aim to achieve a comparison capability of independent estimations of different ratings. In this way the elaboration and development of the approaches and methods are especially urgent because of synergy opportunities connected with the limitations mentioned above...

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