Category Financial Econometrics and Empirical Market Microstructure

The Mike-Farmer Model Without the Cancellation Process (MFWC)

It is an interesting question about what there would be on the market if there were no cancellations. Would trading or the market be stable or not? We realize the MF model without cancellations (we call it MFWC).

3 Model Upgrading

The most important thing that we try to improve in the MF model is the distribution of order price. We cut distribution into two parts: one with a positive tail and one with a negative tail. We find that both tails of distribution fit a good by power-law distribution with a tail exponent = —2.15 for positive values and a tail exponent = —2.493 for negative values (we inversed the negative tails and after that the estimate coefficients). Power-law poorly describes the center of distribution, when orders are put at the best prices...

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Spread Modelling Under Asymmetric Information

Sergey Kazachenko

Abstract Bid-ask spread is a key measure of pricing efficiency in a microstructure framework. Today there is no universal model of spread formation that includes all three factors of transaction costs, inventory risk (losses in case of a changing value of a stored asset) and information asymmetry that influence the behaviour of traders and market-makers. Empirical evaluations of these three components of spread are very contradictory (Campbell et al., The econometrics of financial markets. University Press, Princeton, 1997; Easley and O’Hara, Microstructure and asset pricing. In: George MC, Milton H, Rene HS (eds) Handbook of the economics of finance. Elsevier, Amsterdam, pp 1022-1047, 2003)...

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Cross Asset Class ETF Analysis

StressGrades time series can help us visualize the interrelationship between risk themes. Figure 16 represents major stress themes using ETFs. Observe the sequen­tial cascading of systemic risk starting with the February 27, 2007 equity outlier. In June an outlier drop in 10Y bond yields signaled deflation fear, and in August a jump in GLD signaled escalating inflation fears. Again, note the log scale for PStress.

Especially noteworthy is the increasingly synchronized increase in (Normal Implied) PStress observed across all asset classes after August 1, 2007 as systemic risk increased. Equally noteworthy is the synchronized decline in (Normal Implied) PStress in early 2009, signaling a systemic recovery.

11 StressQ

StressQ is a snapshot of where volatility levels are currently compared to...

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Key Drivers of Default

According to the mortgage literature, the key determinants of default initially include observable socio-demographic characteristics, terms of the mortgage con­tract, mortgage characteristics, and macroeconomic conditions.

Terms of a credit contract are practically used as proxy variables to estimate the risk of a particular borrower. For example, mortgages with low loan-to value ratio (LTV) are attractive for non-liquid borrowers. The probability that they could face a serious problem of repayment of a loan is much higher. Moreover, borrowers with LTVs higher than 90 % think as holders, because they do not invest a lot of their own capital and are less motivated to overcome obstacles with repayment of a loan...

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Literature Review

Most of the studies about tick size present in the literature are case studies of the impact of a reduction of tick size on market quality, i. e. on microstructural quantities like the narrowing of the bid-ask spread (Loistl et al. 2004) or liquidity provision (Goldstein et al. 2000; Ahn et al. 2007). The part of literature more related with our work is composed by papers that have revealed how the investors actually use the price resolution allowed by the tick size. We focus also on statistical properties of price fluctuations (Onnela et al. 2009; Munnix and Schafer 2010; La Spada et al. 2011; Gopikrishnan 1999; Plerou et al. 1999) and on the connection between bid – ask spread and midprice dynamics (Dayri and Rosenbaum 2013; Wyart et al. 2008; Robert and Rosenbaum 2011).

The concept of p...

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Calibration of the Model

One of the most important issues for the practical applications is the estimation of the three unknown parameters of MRW model (ct, A, L) with the real data.

The parameter ct can be estimated using the scaling relation for the variance of the increments of the MRW process:

A

 

о –

 

0

 

50

 

100

l(Scale)

 

150

 

200

 

Fig. 8 Calibration of a2 based on MRW sample of length 219 for A2 = 0.06, a = 7.5 • 10_5 and L = 2048. Triangles represent empirical observations and red line corresponds to the linear regression (20). The estimated a2 equals to 7.7237 • 10~5

 

image180

where At is the scale oflog-returns (e. g, 1-, 5-, 10-, 20-min etc.)...

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