Abstract
The objective of this paper is to examine the relationship between bank characteristics, in particular value, performance and volatility of bank stock returns, and its exposure to financial derivative contracts. The study is based on 109 publicly traded European banks over the period from 2005 to 2010. The database contains both accounting data from Bankscope and manually collected information from the notes to financial statements. After controlling for bank-specific characteristics, time effects and cross-country differences, we find that banks efficiently using hedging derivatives have a lower risk and a higher value. However, this relationship becomes less pronounced or is inversed in the post-crisis period and concerns both trading and hedging derivatives. For systemically important banks heavily involved in derivatives, market volatility of stock returns is higher and valuations are lower. We notice, however, that derivatives play second fiddle to bank risk and performance. Our findings corroborate the importance of distinction of derivatives by the purpose of use, which becomes less obvious for investors in the post-crisis period. Our results have important policy implications, especially in the light of the recent debate over the necessity of separation of risky banking activities from commercial bank branches (for instance, as proposed in Liikanen report) in an attempt to reduce systemic risk. We emphasise the need for a higher transparency of disclosures regarding hedge accounting and harmonisation of reporting formats across EU.
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Notes
As at the end of the first quarter of 2012, the total notional amounts of derivatives in US banks amounted to 227,982 bn USD, that was 18.6 times as much as the total banking assets. For top players in the derivatives markets, this ratio was even more spectacular: 38.8 for J.P.Morgan and Chase, 39.5 for Citibank, 420.1 for Goldman Sachs. Source: Office of the Comptroller of the Currency (2012).
See, for example, the model of Dai and Lapointe (2010) for Canadian financial sector which accounts for the presence of both types of derivatives in each surveyed bank.
Under IFRS, the hedge effectiveness is assessed by the extent to which the changes in the fair value or cash flows of hedged item are offset by changes in the fair value or cash flows of the hedging instrument (par. 71–102, 105 AG 94–132 of IAS 39). Similar approach was preserved in IFRS 9 [par. 6.4.1 (c)].
For example, some of the contracts designated as trading may be effectively hedging derivatives according to internal risk management practices, but do not qualify for hedging designation as they do not formally meet some of the requirements, such as hedge ratio. In November 2013, IFRS 9 has been modified and thus has become more principles-based, but these recent amendments do not apply to our sample.
Top 10 banks that we do not include fail to provide sufficiently detailed description of derivative usage. These banks are: Deutsche Bank AG, Crédit Agricole S.A., ING Groep NV, Natixis, Bank of Greece, SNS Reaal NV, Delta Lloyd NV-Delta Lloyd Group, Van Lanschot NV, DVB Bank SE, Groupe Bruxelles Lambert.
Even after normalising derivatives notional amounts by size, the distribution is slightly positively skewed, skewness is equal to 3.95 for hedging derivatives and 3.57 for trading derivatives. We therefore estimate the models with transformed values by taking a natural logarithm of corresponding ratios. The results remain consistent. They are available upon request.
The only reason for zero notionals is that a bank explicitly claims that it does not use derivatives. There are no cases in the database when fair value is nonzero and notionals are zero. There are 29 (22%) and 59 (45%) banks that do not use trading and hedging derivatives for at least 1 year, respectively.
An alternative interpretation of the difference between positive and negative fair values of derivatives is that of net current credit exposure. It is the key indicator used by the OCC to assess credit risk in derivatives transactions (Office of the Comptroller of the Currency 2012, p. 5).
Although the indicator-based approach to GSIB identification takes into account bank size, the linear relation between bank size and GSIB dummy is not particularly strong in our sample (0.46). Hence, our models may include both variables simultaneously.
Although Chernenko and Faulkender (2011) use different specifications, we see no contradiction with previous findings as datasets are drastically different (in particular, with respect to variables used in our paper).
F-test is used to choose between the pooled model and the model with fixed effects; Breusch–Pagan Lagrange multiplier test enables to choose between the pooled model and the model with random effects; Hausman test permits to choose between the models with fixed and random effects. To address the shortcomings of Hausman test, we also performed panel bootstrap method as a robustness check, which had no effect on our conclusions. We take into consideration that our baseline regression may suffer from endogeneity issues stemming from either potential reverse causality between derivatives use and risk and performance measures or the simultaneous impact of unobserved factors on both our derivatives holdings and dependent variables. To address this issue, we estimate a panel model with instrumental variables, by treating derivatives measures as endogenous. We use their corresponding lagged values (1-year lag) as instruments. According to endogeneity tests, derivative measures can be treated as exogenous variables in our models. Our conclusions remain unchanged. All results are available upon request.
Besides the results reported in this section, we have also analysed the breakdown of derivatives by the underlying asset: interest rate, currency, credit, equity and commodity. Our findings are available upon request.
For brevity, the estimation results with alternative classifications of credit derivatives in the model with derivatives fair values as explanatory variables are omitted. They are available upon request.
As an additional robustness check, we perform clustering to control for the structure of our sample. We apply k-medians method based on time-averaged values of four variables: Hedging notional amount, Trading notional amount, Hedging net fair value, Trading net fair value. Our main findings remain robust to within-cluster analysis. For brevity, we do not present them here. They are available upon request.
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Acknowledgements
We are very grateful for helpful comments from the editor and two anonymous referees. H. Penikas acknowledges the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and support within the framework of a subsidy by the Russian Academic Excellence Project ‘5-100’.
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Appendix
Appendix
See Tables 6, 7, 8, 9, 10, 11, 12, 13.
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Titova, Y., Penikas, H. & Gomayun, N. The impact of hedging and trading derivatives on value, performance and risk of European banks. Empir Econ 58, 535–565 (2020). https://doi.org/10.1007/s00181-018-1545-1
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DOI: https://doi.org/10.1007/s00181-018-1545-1