Tail Risk Measurement in Crypto-Asset Markets

Abstract

The paper examines the relationships among market assets during stressful times, using two recently proposed econometric modeling techniques for tail risk measurement: the extreme downside hedge (EDH) and the extreme downside correlation (EDC). We extend both measures taking into account the sensitivity of asset's return to innovations not only from the overall market index, but also from its components, by means of network modelling. Applying our proposal to the cryptocurrencies market, we find that crypto-assets can be clustered in two groups: speculative assets, such as Bitcoin, which are mainly "givers" of tail contagion; and technical assets, such as Ethereum, which are mainly "receivers" of contagion.

Basel IV and the structural relationship between SA and IMA

“…As for the rest of the techniques, it does not seem possible to extract definite conclusions about their behavior, bar from the unsuitability of Historical Simulation and linear models. In terms of the traditional VaR Backtesting, FHS and CV deliver somewhat mixed results, with GARCH variants faring slightly better than EGARCH counterparts in FHS category (Columns [3] to [6]) and GARCH-t and EGARCH-t in FHS (Columns [7] to [10]), which appears to signal the relative preeminence of the specification for the FHS and the distributional assumption for CV. Therefore, it would not seem sensible to apply any other model than EVT, as their performance across the board implies 19 .…”

“…Table 5.6 depicts the capital levels at the start of the evaluation period. A simple look shows that both specifications do not suffer Backtesting penalties (Table 5.6 Columns [3] to and [13] to [15]), hence their capital values are not in principle deemed to be distorted by those surcharges. Furthermore, the fact that both Spearman and Kolmogorov-Smirnov tests fall into the Green Zone translates in the absence of the surcharge envisaged in ( 17) and ( 18) for both variants (Table 5.6 Columns [6] to [7] and [16] to [17]).…”

“…Across the board, the official model nearly doubles the Simplified technique, again mainly due to the stressed calibration of the reduced factors (S&P BMV/IPC). However, the flip side of the increased capital is its instability as long as the quantity of assets is reduced (the standard deviation raises from 2.73% and 2.61% to 7.06% and 5.31% for P1 and P2 respectively (Table 5.8 Column [2] against [10] and [4} against [12], second line), in turn deriving from the ESFC (i.e., a portfolio of 4 assets each) as the rest of the factors of (13) remain unaltered; conversely, the path for simple ES does not appear spiky as the diversification decreases (Table 5.8 Columns [3], [5], [7], [9], [11] and [13], second line). The average capital surplus reflects the modelling nature, with maximum and minimum values of around 43% and 26% (Table 5.8 Columns [10] and [8] respectively) versus 23% and 13% (Table 5.8 Columns [11] and [5] respectively) 21 .…”

“…al. (2021) 3. Langthaler and Lederer (2022) question the 'Basel IV' denomination on the grounds that the series of reforms still belong to the finalisation of the Basel iii standards launched in 2016, notwithstanding which the majority of the financial literature backs the change in view of the sweeping nature of the reforms.…”