SciELO - Scientific Electronic Library Online

vol.21 número1High inventory levels: The raison d'être of township retailersMeasuring and profiling financial literacy in South Africa índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados



Links relacionados

  • Em processo de indexaçãoCitado por Google
  • Em processo de indexaçãoSimilares em Google


South African Journal of Economic and Management Sciences

versão On-line ISSN 2222-3436
versão impressa ISSN 1015-8812

S. Afr. j. econ. manag. sci. vol.21 no.1 Pretoria  2018 



Procyclicality in tradeable credit risk: Consequences for South Africa



Dirk Visser; Gary W. van Vuuren

Department of Risk Management, School of Economics, North-West University, South Africa





BACKGROUND: Tradeable credit assets are vulnerable to two varieties of credit risk: default risk (which manifests itself as a binary outcome) and spread risk (which arises as spreads change continuously). Current (2017) regulatory credit risk rules require banks to hold capital for both these risks. Aggregating these capital amounts is non-trivial.
AIM: The aim was to implement the bubble value at risk (buVaR) approach, proposed by Wong (2011) to overcome the risk aggregation problem. This method accounts for diversification and for procyclicality and operates by inflating the positive side of the underlying return distribution, in direct proportion to prevailing credit spread levels (usually liquid credit default swap spreads.
SETTING: The principal setting for the study was the South African credit market which represents a developing market. Previous work by Wong (2011) focussed only on developed markets.
METHODS: Using South African data, closed form solutions were derived for free parameters of Wong's formulation, and the relationship between the spread level and the response function was developed and calibrated.
RESULTS: The results indicate that the original calibrations and assumptions made by Wong (2011) would result in excessive capital requirement for South African banks. Estimates obtained from this work suggest further calibration is required to cover the unique features of the South African milieu. Considerable differences compared with other markets were also found.
CONCLUSION: The application of buVaR to South African government bond credit default swaps spreads highlighted the metric's countercyclical properties that would potentially have countered bubble developments had they been implemented during the credit crisis of 2008/2009. Regulatory authorities should take this important metric into account when allocating South African bank's credit risk capital.




Credit losses experienced in the financial crisis of 2008-2009 emphasised the complexities surrounding tradeable credit instruments. In their fundamental review of the trading book, the Basel Committee on Banking Supervision (BCBS 2013) asserted that credit-related products had been a major source of losses and the approach and treatment of these positions were severely flawed. The crisis also prompted financial institutions to change their evaluation and management of credit risk substantially (BCBS 2015).

A 2013 global survey conducted by the Joint Forum on Banking Firms and Supervisors revealed that since the crisis banks have improved governance and risk reporting and that risk aggregation has become far more sophisticated (BCBS 2015). Modelling enhancements driven by regulatory requirements stress testing and crisis experience has significantly shifted institutions' reliance to internal models as the 'search for yield' intensifies in a post-crisis low interest rate environment (BCBS 2015). Despite these promising developments, the BCBS has attempted to align the requirements of the trading book1 more closely with those of the banking book (BCBS 2013). The BCBS (2013) proposes substantial revisions to many aspects of the treatments of market risk - these have now (2018) been reviewed by market participants and approved for implementation (BCBS 2014).

In the trading book, tradeable credit instruments are associated with two sources of risk. Firstly, default risk in which there is a probability that the underlying issuer may forfeit its obligation through contractual non-compliance (Brown & Moles 2014) and, secondly, spread risk in which losses arise from changes in an instrument's credit spread. This spread can be defined as the instrument's yield relative to that of a comparable-duration default free instrument and is not attributable to defaults or credit migrations (BCBS 2009). The complicated relationship between credit and market risk and the aggregation of these risks are emphasised by the fact that approximately two-thirds of financial crisis (2008-2009) losses were attributable to spread risk with only a third of the losses stemming from actual defaults (BCBS 2011). Sophisticated financial instruments based on securitisation played a pivotal role in the financial crisis masking risk and increasing the interconnectedness of financial markets. A downward spiral of the quality of the securities backing these instruments caused several rating downgrades of these instruments and precipitated chaos within globally interconnected financial markets. Further, the procyclical nature of these markets, albeit contributing significantly to the pre-crisis market euphoria, fuelled one of the biggest busts in financial history (Bank for International Settlements 2008; Papaioannou et al. 2013).

Procyclicality - defined as those economic quantities that are positively correlated with the overall state of the economy (Van Vuuren 2012) - leads to institutions reducing lending capacity throughout market busts. Regulatory capital models using data from busts would recommend banks keep higher levels of capital as well (this is also influenced by stress testing of models); the opposite is true in financial booms. This procyclical characteristic of markets, methods and metrics has been countered by the introduction of the countercyclical capital buffer (CCB) by the BCBS in 2010 which is based on the aggregate credit-to-GDP gap ratio (BCBS 2010). The buffer is mostly untested in emerging and developing markets and thus the complexities regarding its implementation and timing have not been comprehensively divulged.

Value at risk (VaR) has been the preferred market risk measurement tool since 1994 (BCBS 1994), but suffers from significant shortcomings, many of which played a significant role in the crisis. These problems were partially ameliorated by the replacement of VaR with expected shortfall (ES) in 2018 (BCBS 2013). Although VaR is relatively simple to calculate, it does not account for risk aggregation and is also procyclical in nature. Further, VaR models used in credit risk measurement are mostly dependent on regulatory information such as rating transition matrices making these models slow to reflect current market conditions. ES solves some of VaR's shortcomings as it embraces not only the likelihood of losses, but also the size of losses beyond the VaR confidence level, thus accounting for risk in a more comprehensive manner (BCBS 2013). However, procyclicality remains an issue as ES uses the same historical data produced by markets as VaR does. This necessitates the use of a CCB. However, further analysis of the proposed buffer components has suggested alternatives to the credit-to-GDP gap. The research includes that of Barrell et al. (2010), Shin (2013) and Behn et al. (2013) who all proposed alternatives to this metric. Drehmann and Tsatsaronis (2014) review these studies and argue that the credit-to-GDP gap is the best standalone early warning indicator over forecast horizons of 2 to 5 years. They further find that the debt service ratio (DSR) is the best single indicator for forecast horizons shorter than 2 years. Drehmann and Tsatsaronis assert that both judgement and quantitative analysis are required by policymakers as there are no foolproof models that provide an effective rule-based countercyclical measure. Further, although the credit--to-GDP gap ratio was found to be optimal, Drehmann and Tsatsaronis found that a combination of other indicators may work better for certain jurisdictions.

Applying conventional VaR to tradeable credit instruments poses challenges, as all the risks pertaining to these instruments are not adequately captured in the measurement (Amato & Remolona 2003; Elton et al. 2001). Further, under the Basel Market Risk framework, it is also not a requirement to measure multiple risks in one model. Spread and default risk are currently simply added together after being individually measured without accounting for diversification possibilities as prescribed. Although aggregation issues present problems regarding diversification and even compounding of risks, Wong's (2011) bubble VaR (buVaR) proposes a unified method of combining these two forms of risks avoiding the problem of risk aggregation. Further, the model relies on credit spread data avoiding transition matrices or ratings information. Credit default swap (CDS) data are forward-looking and highly liquid as they are readily available daily (Huang, Zhou & Zhu 2009). Using these data, Wong illustrates the asymmetry between defaults and spread movements and asserts that spread widening will always precede defaults. Although Wong calibrated the buVaR credit risk model for several countries, South Africa was not among them. This article explores that calibration, estimates potential standardisation and provides deeper insight into the model's applications in the South African milieu.

The remainder of this article proceeds as follows: the 'Literature study' section explores tradeable credit instruments and the regulatory treatment thereof. The section also highlights difficulties in the measurement of credit risk under regulatory recommendations. The choice of data and relevance thereof is explained in 'Data and methodology' section along with the mathematics of the metrics being assessed. The logic behind Wong's (2011) credit buVaR and how the metric may sufficiently account for diversification possibilities when spread and default risk are combined into one metric are also discussed here. Results obtained from analysis and scenario simulations are then illustrated and discussed in the next section and the final section concludes.


Literature study

Regulatory treatment of tradeable credit instruments

Conventionally, a bank's trading book is predominantly affected by market risk, with the banking book being mostly susceptible to credit risk (BCBS 2009). However, tradeable credit instruments introduce credit risk to the trading book and the regulatory capital calculated for these in the crisis were woefully insufficient. These tradeable credit instruments do, however, provide for price discoveries in secondary markets through credit spreads.

Institutions holding portfolios of debt securities or derivatives to hedge risk stemming from their securities, face several forms of risk. Spread movements is one such risk, while the possibility of issuer defaults of tradeable credit instruments is another. Correlated defaults between issuers of securities pose further risk to banks and financial institutions (Wong 2011).

Spread risk

Spread risk is traditionally modelled using historical simulations and applying a VaR approach. Simulated returns are generated from the spread variable over an observation period - between 1 and 3 years under the Basel formulation. The return vector is and each portfolio position is mapped to benchmark issuer risk factors, so if the portfolio is subject to N risk factors, there are N return vectors. Return vectors are combined to derive a portfolio profit and loss distribution. Under the Basel II formulation, spread VaR is the VaR over a 10-day period at a 99% confidence level as it is for standard market risk instruments and portfolios (BCBS 1994).

In what has been informally labelled Basel IV, the BCBS have suggested changes to the trading book/banking book boundary by addressing issues such as the trading book definition, trading book components and ineligible trading book instruments (BCBS 2013). Such rigorous proscriptions were limited to mere footnotes in the revisions of the Basel II market risk framework (BCBS 2011) - now, they have become standard features of the way banks will need to treat trading books. A summary of the current boundary definitions compared to proposals are highlighted in Table 1.



Default risk

Because entities only default once, an historical simulation approach is impossible since there is no time series of observable default history available. Monte Carlo simulations are used instead. Possible occurrences of ratings migration (of which default is a special case) are generated over a 1-year simulation horizon. The Monte Carlo simulation assumes that credit default driver follows a particular distribution: simulated values are mapped to an end-state rating (upgrades, downgrades or a default). The probabilities of these trajectories from the current rating to the end-state ratings (including default) are embedded within the rating transition matrix. These matrices are populated with single-year transition rates assembled by credit rating agencies using empirical historical statistics of defaults and upgrades or downgrades in selected benchmark sectors. The portfolio positions are mapped to relevant sectors before the simulation begins. Correlation coefficients are determined independently and used to capture correlation risk between the sectors. Regulatory capital rules require that the VaR of this distribution is determined at a 99.9% confidence level and a time horizon of 1 year - this is known as credit VaR. Crouhy, Galai and Mark (2000) provide a comprehensive overview of contemporary, available credit risk models.

The responses received by the BCBS from commentators mostly suggested that integrating the default component of the trading book into market risk models, presents several challenges and complexities. The BCBS thus decided that the total credit risk capital charge for both the standardised and models-based approaches would comprise two components: an integrated credit spread risk capital risk charge and an incremental default risk (IDR) charge (BCBS 2013).

Aggregation problems

The complex relationship between market and credit risk gives way to aggregation issues including the inability to clearly identify diversification issues and related compounding effects. This challenge also fuelled the perception that adding separately estimated risk components for market and credit risk will most certainly be conservative, due to not all diversification possibilities being considered. However, the BCBS suggests from the financial crisis learnings that non-linear interactions between market and credit risk may reinforce each other and lead to even more severe losses. The BCBS further suggests that under a top-down aggregation approach (as commonly used in practice) diversification benefits should be approached with caution (indeed, the BCBS now restricts the use of negative correlations in portfolio assembly and construction, BCBS 2013).

Adding independently estimated risk measurements in a top-down approach is flawed in that it assumes that perfect correlations between market and credit risks exist. Since both risk forms are affected by the same economic factors, some diversification benefits are expected. However, non-linear interactions between market and credit risk may lead to instances where the combined total risk is higher than the sum of individually measured components.2 These compounding effects often arise when market and credit risks are inseparably connected, that is, default losses from instruments depend on the movements in market risk factors or conversely when the values of instruments affected by market risk factors are dependent on defaults or rating changes.

Diversification benefits are, however, not unobtainable and the BCBS highlights this by showing the interactions between interest rates and credit risk in the banking book. Creating a hypothetical bank, Drehmann, Sorensen and Stringa (2008) place emphasis on modelling the entire banking book including assets, liabilities and interest sensitive off-balance sheet items. Drehmann et al. argue that non-linear effects created by the interaction between interest rates and default probabilities may be difficult to capture outside of integrated models measuring total risk. Alessandri and Drehmann (2007) further assess aggregate risk and required capital by using a set of stylised assumptions to calibrate the model to the profile of a typical UK bank. Through this they show diversification benefits such that the capital kept for an integrated risk measurement for interest rate and credit risk is less than would have been kept for credit risk if measured separately.

Diversification benefits are not guaranteed when using VaR, since the market risk measure is neither coherent nor sub-additive. Coherent risk measures such as ES guarantee the diversification benefits if an integrated measurement of total risk is measured. If separate measurements are done for market and credit risk, diversification benefits cannot be guaranteed even if coherent measurements are used. The choice of metric is not the only challenge related to integrated risk measurement. The metrics used in credit and market risk are not entirely comparable. For example, market risk models capture complete return distributions whereas credit risk models account mostly for losses stemming from defaults and ignore gains. A further challenge emerges in the different horizons that the risks are measured in, despite credit risk becoming more tradeable in the 21st century because of financial innovations such as securitisation.

The BCBS conclude that although integrated risk models have high data and technological demands, the aggregation and integrated measurement of market and credit risk should be done consistently in such a way that a common horizon is imposed, and all income, profits and losses are accounted for. However, the challenges mentioned, as well as the fact that a top-down approach is favoured by most banks with these approaches involving simple correlations i