Category Financial Econometrics and Empirical Market Microstructure

Comparison of Portfolio Management Strategies

Despite the great potential of the developed models, most of them have not been applied to real data. To prove the usefulness of portfolio management models for practitioners, we apply some of the contemporary results in this field to real MICEX trading data and give recommendations for their usage. Our database consists of the complete tick-by-tick limit order book for MICEX shares from January 2006 through June 2007. We consider only liquid shares, such as LKOH, RTKM and GAZP, because only during sufficiently intensive trading does it become possible to calibrate models for the real market.

We consider the problem of optimal purchase of a single-asset portfolio over a given period and compare the performance of the following strategies:


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Model-Dependent Price Jump Indicators

1. The Difference Between Bi-power Variance and Standard Deviation

The method is based on two distinct measures of overall volatility, where the first one takes into account the entire price time movement while the second one ignores the contribution of the model-dependent price jump component. Barndorff-Nielsen and Shephard (2004a) discuss the role of the standard variance in the models where the underlying process follows Eq. (1): the standard variance captures the contribution from both the noise and the price jump process unlike the realized variance, which does not take into account the term with price jumps. It is called the realized bi-power variance...

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The Second Wave: HSBC’s February 2007 Loss

The second major jump in subprime volatility occurred on February 23, 2007, the day after HSBC announced a $10.5 bn loss in their US subprime holdings. It looked like a classic exogenous Black Swan shock, as AAA spreads instantaneously tripled from 11 to 31 bp (an unprecedented 12 sd daily outlier). Spreads soon stabilized
below 20 bp again, bolstered by “relative value” trades: traders bet that increasing subprime delinquencies would hurt the first loss equity or mezzanine tranches of CDOs, but thought that the AAA rated securities were immune. And they decided to make the trades “carry neutral” and some even went so far as to characterize this as a “hedge.” If BBB’s were trading at 200 bp and AAA’s were 20 bp, shorting $1 bn BBB meant buying $10 bn AAA...

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How to Disentangle the Impact of HFT on Market Liquidity from Other Factors?

There are a number of challenges for evaluation of the influence of HFT on market quality. First, it is very difficult to disentangle the impact of HFT on the market quality from other technological and regulatory innovations which led to substantial changes in the market structure of many trading venues, e. g., decimalization in U. S. equity markets. Then, it is also difficult to identify HFT traders even if researchers have access to agent-resolved data, which is extremely rare (Kirilenko et al. 2011; Hagstromer and Norden 2013; Benos and Sagade 2012). One explanation suggests that many traders pursue a variety of strategies which both provide and take liquidity depending on market conditions...

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The Mike-Farmer Model (2008)

In the publication of Farmer et al. (2006) in the Future Enhancement chapter, there were announced important properties of the order flow for a future upgrade of the model. Parts of these features were introduced in Mike and Farmer (2008). We call this model the MF model. This model was distinguished from the previous one in:

• Trending of order flow

• Power placement of limit prices

• Non-Poisson order cancellation process

Later, this model was upgraded and analyzed in Chakraborti et al. (2011), Gu and Zhou (2009), and He and Wen (2013). The first and most important assumption that signifies order flow is a long memory process (Bouchaud et al. 2004; Lillo and Farmer 2004; Lillo et al. 2005).

The first step for the construction of the model is the estimation of the Hurst exponent usi...

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System of Models and Synergy of Rating Estimations

Previously we considered the capabilities which were given to us by rating mappings and models. Later we will discuss the synergy of these approaches as instruments of the Joint Rating Environment system (JRE-system). Such a system may be used for risk management in commercial banks; its main components for financial institutions are presented in Fig. 4.

The main part of such a system is the correspondence between rating scales, including the connection with internal ratings. They provide the opportunity to compare different ratings, as well as to use a comparable estimation of ratings received by several models. The synergy of such estimations gives a basic scale by independent risk weightings.

The system of models brings to the IRB Approach some possibilities, among which there may indic...

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