Abstract
In this paper, we study the relationships among financial market sub-segments as a way to identify potential financial distress through increased co-movements among them. To study how sub-markets are mutually co-dependent, we combine granular data on over-the-counter derivatives by trade repositories and the joint probability of distress (JPoD) approach introduced by the International Monetary Fund. We define an indicator that combines several distress drivers and observe that results on co-dependencies are similar to those that would be expected: similarities between financial and contractual terms seem to be responsible for stronger co-movements among sub-markets. However, high values for JPoD even in correspondence of quite dissimilar sub-markets suggest the presence of other drivers that should be investigated in future research. To the best of our knowledge, this is the first empirical study on systemic risk assessment based on micro-founded trade repositories’ data on interest rate swaps.
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Notes
For instance, a useful review on the application of network theory tools and methodologies can be found in Upper (2011).
For a deeper study on the divergences between the European Union and the USA in financial market regulations, see, e.g., Acharya et al. (2010), Lannoo (2013), and Valiante (2010); a valuable reference for better understanding the key requirements involved in the aggregation of TRs data is provided by FSB (2014).
Iason Ltd is a consulting firm operating in risk management tools and applications. For references, see https://doi.org/www.iasonltd.com/.
This is a software developed by the consulting firm IASON Ltd. For references, see https://doi.org/www.financial-machineries.com/gtr-analytics.htm.
Data refer to BIS statistics and to single currency contracts only. For further references, see https://doi.org/www.bis.org/statistics/derstats.htm.
See Pallavicini and Brigo (2013).
We further check for double counting in the transactions by controlling for contractual terms. In particular, we consider as duplicated deals those transactions that are equal in terms of dissemination ID, contractual expiry, effective date, end date, price, and notional amount.
For references, see https://doi.org/www.bis.org/statistics/dt07.
If there are missing values due to lack of data, we replace them by the cubic spline interpolation of the available points. To limit potential biases due to outliers, for each sub-market we cut off 0.025 of the area in each tail of the reference sample distribution.
In addition, one could argue that Eq. (1) can be improved by generalizing it with some parameters to be calibrated in some optimal way, such as:
$$\begin{aligned} I_{i,t}\left( \alpha ,\beta ,\gamma ,\delta \right) = \ln \left( \frac{\mathrm{max}_{i,t}}{\mathrm{min}_{i,t}}\right) ^{\alpha } \times \frac{\sigma _{i,}^{\beta }}{(\hbox {Avgvolume}_{i,t}^{\gamma } \times \hbox {Num}_{i,t}^{\delta })}. \end{aligned}$$(2)Estimates for September 2013 might even reflect the backload process of the deals. For instance, in the European Union, the EMIR regulation was in force since February 2014. At that time, also the deals already in existence were uploaded by a massive backload process. Hence, we doubt the quality of the oldest data. In fact, from the effective trade repository feed-running process, the distress indicators become lower and more stable. Note also that the VIX popular index, i.e., the volatility index of the S&P index level, did not reach abnormal levels at the end of 2014. In September 2013, the average level was 14.65 %, just 50 bps higher than the average level of 2014 14.14 %.
The joint distress of pairs of sub-markets, as well as related terminology, are concepts introduced in this paragraph and in Sect. 3. Hereinafter, any reference to existing expressions must be considered in the context of our work.
For further references, see Mahfoud (2012).
The parameters that correspond to the independence case are: 0 (asymptotic value) for the Frank and the Clayton copulas, 1 for the Gumbel copula.
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Acknowledgements
The authors are grateful for financial support from Consiglio Nazionale delle Ricerche—Programma Nazionale della Ricerca, Project CRISIS Lab. They are also thankful for the very valuable comments, which greatly improved the paper, made by the Editor and the anonymous referee. They thank Iason Ltd for making the dataset available. Special thanks go to A. Castagna for his precious collaboration. The usual disclaimer applies.
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Irene Crimaldi and Laura Gianfagna are members of the Italian Group “Gruppo Nazionale per l’Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA)” of the Italian Institute “Istituto Nazionale di Alta Matematica (INdAM)”.
Appendix
Appendix
In the paper, we focused on a copula dimension equal to 2. Generalizations to higher dimensions are feasible, although it is worth remembering that in our case we are dealing with only nine sub-markets. Below, we briefly report the trivariate case and present estimates similar to those in Table 8. The algorithm retains the possibility of choosing between three different copulas (Frank, Gumbel, and Clayton). We estimated all possible triple results, namely, 84 positions (the number of possible combinations for nine sub-markets), although for the sake of conciseness we report only the first ten positions. Both tables show the value of the parameter estimated by our procedure minus the parameter for the independence case (diff_theta). Table 12 is ordered by decreasing maximum likelihood; Table 11 is obtained by ordering the triples by decreasing diff_theta.
Terns that appear in the first ten positions of both tables should be used in the financial analysis of the sub-markets, since those should represent the most trustworthy results as they are found in two different ordering criteria. Triples (4,5,8), (5,6,8), and (1,5,6) appear in both Tables 11 and 12. The first two pairs share similar contractual terms, that is, they refer to swap frequency legs equal to (6m3m) and present a short Manhattan-like distance (it is 4 in both cases, computing by summing distances among each couple in the tern). Conversely, tern (1,5,6) shows quite different features and presents a higher distance (it is 6). Thus, even in the trivariate case, dissimilarity among contractual and financial terms can imply strong co-movement. Below, we analyze the tables in more detail, providing a comparison with the sub-markets that appeared to be co-dependent in the bivariate case as shown in Table 8.
The ranking in Table 11 is based on diff_theta. Estimates are coherent with those in Table 8: in the first positions, we observe combinations of pairs (5,6), (5,8), (2,3), and (8,9), that is, pairs of sub-markets that are strongly co-dependent in the bivariate case are more likely to influence co-movement also in the trivariate case. Hence, relevant relationships among pairs of sub-markets seem to emerge regardless of the dimension of the copula. In addition, we also compare these results to those provided in Table 10, which gives the output of JPoD (not implemented in the trivariate case): we find again that sub-markets (5,6), (5,8), (2,3), and (8,9) hold top positions in the ranking. Finally, in Table 12 we show how sub-markets are ranked based on the maximization of the log-likelihood. Even in this case (similar to the one discussed in Sect. 5), the results are coherent among Tables 12 and 11, once we consider the estimates within the chosen type of copula. Overall, this is further evidence of the robustness of our procedure; however, increasing the copula dimension too much may lead to meaningless results when having few sub-markets.
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Bonollo, M., Crimaldi, I., Flori, A. et al. Assessing financial distress dependencies in OTC markets: a new approach using trade repositories data. Financ Mark Portf Manag 30, 397–426 (2016). https://doi.org/10.1007/s11408-016-0275-7
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DOI: https://doi.org/10.1007/s11408-016-0275-7
Keywords
- Financial distress interdependence
- Joint probability of distress
- Interest rate swaps
- Systemic risk
- Micro-founded trade repositories’ data