Condiciones de humedad antecedentes

ICA methods, in contrast to the correlation-based transformations obtained in PCA for the market liquidity factors, not only de-correlate the liquidity measures in the asset cross-section each day, but also reduce higher-order statistical dependencies. Whereas PCA minimises the covariance of the data, ICA minimises higher-order statistics such as the fourth-order cumulant (or kurtosis), thus minimising the mutual information of the output. Specifically, PCA yields orthogonal vectors of high energy content in terms of the variance of the signals, whereas ICA identifies independent components for non-Gaussian signals.

After regressing each asset’s liquidity against the three ICA components maximising non- Gaussianity, every day, we again obtained strong evidence to suggest that the assets dominating the PCA analysis due to Gaussianity violations, such as Nexans SA, also correspond to those that were very well explained in a linear projection by the ICA components. The coefficients of determination for the daily regressions for the other assets in the analysis are displayed in boxplots in Subplot 2 of Figure 5.7.

For the assets which are determined to coincide with the independent components from the ICA analysis, we have seen that these will dominate the PCA decomposition. We therefore suggest that ICA can be used as a preliminary step to identify whether these non-Gaussian features exist, and consequently, whether a PCA approach would be appropriate.

5.3

Liquidity resilience for high frequency data

In addition to the considerations regarding the appropriateness of the statistical assumptions for an analysis of commonality, we note that existing liquidity commonality approaches only reflect the aspects of liquidity measure chosen. In the case of the spread, this would be the tightness, and in the case of the XLM, it would also reflect the depth. However, since such measures cannot quantify liquidity resilience (which can be understood as the speed of liquidity replenishment), the associated commonality analysis will not reflect this aspect of liquidity either. Here, we extend the analysis to determine if the liquidity commonality observed is also present when one incorporates notions of resilience.

In Chapter 4, we introduced the Threshold Exceedance Duration (TED) and explained how one can use a parametric survival regression model to model the variation in the TEDs over time, where the duration variable of interest is denoted by τ . Using this formulation, one can obtain model based estimates of the expected (log) duration of an exceedance over a chosen threshold j of the liquidity measure, i.e. E[ln(τ[j])| ˆβ, x] given covariates x that are based on the LOB structure. They are a subset of those considered in the previous chapter, which were found to be more explanatory for the variation of the TED. They are as follows:

5.3. Liquidity resilience for high frequency data 137

• The total number of asks in the first 5 levels of the LOB at time t, obtained according to x(1)_t =P5 i=1   V a,i t  

(where |·| is the number of orders at a particular level)

• The total number of bids in the first 5 levels of the LOB at time t, obtained according to x(2)_t =P5 i=1   V b,i t   

• The total ask volume in the first 5 levels of the LOB at time t, obtained according to x(3)_t =P5

i=1T V a,i t

• The total bid volume in the first 5 levels of the LOB at time t, obtained according to x(4)_t =P5

i=1T V b,i t

• The instantaneous value of the liquidity measure (spread or XLM) at the point at which the i-th exceedance occurs

• The number x(6)_t of previous TED observations in the interval [t − δ, t], with δ = 1s • The time since the last exceedance, x(7)_t

• The average of the last 5 log TEDs, x(8)_t

• A dummy variable indicating if the exceedance occurred as a result of a market order to buy, x(9)_t

• A dummy variable indicating if the exceedance occurred as a result of a market order to sell, x(10)_t

5.3.1

Summarising resilience behaviour

Once E[ln(τ[j])| ˆβ, x] is obtained for a large range of liquidity thresholds we can combine these to obtain the Liquidity Resilience Profile (LRP). The LRP is a summary of the expected re- silience behaviour of an asset across different liquidity thresholds:

Definition 5.3.1. The daily Liquidity Resilience Profile is a curve of the expected TEDs as a function of the liquidity threshold, given the state of the LOB, as quantified by the covariates characterising the LOB for the given asset.

To facilitate comparison between the liquidity resilience behaviour of assets at different threshold levels, we present results for the logarithm of the expected TED, i.e. we calculate E[ln(τ[j])| ˆβ[j], x[j]], where the j are threshold levels j = 1, . . . , 9 corresponding to deciles of the empirical spread or XLM distribution. We can then identify the commonality in the

5.3. Liquidity resilience for high frequency data 138 0 1 2 3 1 2 3 4 5 6 7 8 9 Threshold (decile)

Conditional Expected ln(TED)

0 1 2

1 2 3 4 5 6 7 8 9

Threshold (decile)

Conditional Expected ln(TED)

Figure 5.8: Liquidity resilience profile for Credit Agricole in the normal LOB regime, in

which covariates take their median values. The x-axis represents the decile threshold used in the TED definition, and the curve is obtained by considering thresholds corresponding to deciles of the empirical distribution of the liquidity measure - in this case, the spread(left) and XLM(right).

expected TED over the median spread, or the 9th decile of the XLM, for example. We explain how the smooth functional representation is obtained in the following section.

We should note here that we will diverge somewhat from previous analyses, which only quantified the temporal commonality between the liquidity of individual assets. The temporal component of liquidity resilience commonality is captured through the similarity in the expected daily exceedance times over a threshold (measured through the TED metric). However, as we have obtained a representation of the expected exceedance time as a function of the level of the threshold, we can also quantify this commonality at different thresholds.

The functional representation enables us to then establish whether such commonality in liquidity resilience behaviour exists at any or all levels of the liquidity measure. The part of the curve corresponding to low thresholds of the spread or XLM indicates the expected time to return to a high level of liquidity, which would interest a brokerage house trying to minimise execution costs. The part of the curve corresponding to high thresholds, on the other hand, indicates the expected duration of periods with very low liquidity, which could be considered by a regulator as part of their efforts to ensure uninterrupted liquidity in financial markets.

We note that it would also be possible to extract such a functional form for the LRP directly, by performing the regression across all thresholds at once. However, it is more compu- tationally efficient to do so in stages, by first fitting the survival regression model per threshold and then estimating the best fitting curve across the conditional expected TEDs using functional data analysis.