Comparing Australian and US Corporate Default Risk using Quantile Regression

The severe bank stresses of the Global Financial Crisis (GFC) have underlined the importance of understanding and measuring extreme credit risk. The Australian economy is widely considered to have fared much better than the US and most other major world economies. This paper applies quantile regression and Monte Carlo simulation to the Merton structural credit model to investigate the impact of extreme asset value fluctuations on default probabilities of Australian companies in comparison to the USA. Quantile regression allows modelling of the extreme quantiles of a distribution which allows measurement of capital and PDs at the most extreme points of an economic downturn, when companies are most likely to fail. Daily asset value fluctuations of over 600 Australian and US investment and speculative entities are examined over a ten year period spanning pre-GFC and GFC. The events of the GFC also showed how the capital of global banks was eroded as defaults increased. This paper therefore also examines the impact of these fluctuating default probabilities on the capital adequacy of Australian and US banks. The paper finds highly significant variances in default probabilities and capital between quantiles in both Australia and the US, and shows how these variances can assist banks and regulators in calculating capital buffers to sustain banks through volatile times.Classification-JEL:Probability of default; Quantile regression; Australian banks; United States banks.

Tail Risk for Australian Emerging Market Entities

Whilst the Australian economy is widely considered to have fared better than many of its global counterparts during the Global Financial Crisis, there was nonetheless extreme volatility experienced in Australian financial markets. To understand the extent to which emerging Australia entities were impacted by these extreme events as compared to established entities, this paper compares entities comprising the Emerging Markets Index (EMCOX) to established entities comprising the S&P/ASX 200 Index using four risk metrics. The first two are Value at Risk (VaR) and Distance to Default (DD), which are traditional measures of market and credit risk. The other two focuses on extreme risk in the tail of the distribution and include Conditional Value at Risk (CVaR) and Conditional Distance to Default (CDD), the latter metric being unique to the authors, and which applies CVaR techniques to default measurement. We apply these measures both prior to and during the GFC, and find that Emerging Market shares show higher risk for all metrics used, the spread between the emerging and established portfolios narrows during the GFC period and that the default risk spread between the two portfolios is greatest in the tail of the distribution. This information can be important to both investors and lenders in determining share or loan portfolio mix in extreme economic circumstances. Classification-JEL:Conditional value at risk; Conditional distance to default; Australian emerging markets

Bank Risk: Does Size Matter?

The size of banks is examined as a determinant of bank risk. A wide range of banks are examined across four regions, including Australia, Canada, Europe and the USA. Four risk metrics are considered including Value at Risk (VaR), Conditional Value at Risk (CVaR, which measures risk beyond VaR), Probability of Default (PD) using Merton structural methodology, and Conditional Probability of Default (CPD, the author’s own model which measures risk based on extreme asset value fluctuations. Daily equity and asset value fluctuations are included in the analysis, including pre-GFC and GFC periods. In addition to examining size in isolation as a determinant of bank risk, the paper uses fixed effects panel data regression to examine the significance of size as a risk determinant in conjunction with a range of other independent variables. The study finds mixed results among the four regions with no conclusive evidence of significant association between size and risk

Default risk in the European automotive industry

This paper examines credit risk in the European automotive industry. Distance to Default (DD) is calculated using the Merton structural credit model. In addition, we modify the Merton model to generate an innovative measure of credit risk at the extremes of the asset value fluctuations distribution, which we call Conditional Distance to Default (CDD). The credit risk of all listed automotive stocks on the S&P Euro Index is compared to all the other industries on this index, which comprises 180 stocks with geographic and sectoral diversity. The study spans the 10 years from 2000 to 2009 divided into pre-GFC and GFC periods. Our metrics find the automotive industry to be of high risk relative to other European industries, particularly during the GFC. We also find that our CDD metric is better able to capture the extreme credit risk prevalent in the industry during the GFC than traditional DD metrics

Japanese Banks: Tail Risk and Capital Buffers

This paper applies quantile regression to a structural credit model to investigate the impact of extreme bank asset value fluctuations on capital adequacy and default probabilities (PD) of Japanese Banks. Quantile regression allows modelling of the extreme quantiles of a distribution which allows measurement of capital and PDs at the most extreme points of an economic downturn, when banks are most likely to fail. Outcomes are compared to traditional structural measures. We find highly significant variances in capital adequacy and default probabilities between quantiles, and show how these variances can assist banks and regulators in calculating capital buffers to sustain banks through volatile time

Extreme equities risk in emerging markets

The huge volatility experienced by equities markets during the Global Financial Crisis (GFC) underlined the importance of understanding market risk in extreme economic conditions. Whilst the Australian economy is widely considered to have fared better than many of its global counterparts during the GFC, there was nonetheless extreme volatility experienced in Australian financial markets. To understand the extent to which emerging Australian entities were impacted by these extreme events as compared to established entities, this paper compares entities comprising the Emerging Markets Index (EMCOX) to established entities comprising the S&P/ASX 200 Index using four risk metrics. The first two are Value at Risk (VaR) and Distance to Default (DD) which are traditional measures of market and credit risk. The other two focus on extreme risk in the tail of the distribution and include Conditional Value at Risk (CVaR) and Conditional Distance to Default (CDD), the latter metric being unique to the authors and which applies CVaR techniques to default measurement. We apply these measures both prior to and during the GFC, including an analysis of high, medium and low risk quantiles and find that Emerging Market shares show higher risk for all metrics used, the spread between the emerging and established portfolios narrows during the GFC period and that the default risk spread between the two portfolios is greatest in the tail of the distribution. This information can be important to both investors and lenders in determining share or loan portfolio mix in extreme economic circumstances

Xtreme Credit Risk Models: Implications for Bank Capital Buffers

The Global Financial Crisis (GFC) highlighted the importance of measuring and understanding extreme credit risk. This paper applies Conditional Value at Risk (CVaR) techniques, traditionally used in the insurance industry to measure risk beyond a predetermined threshold, to four credit models. For each of the models we use both Historical and Monte Carlo Simulation methodology to create CVaR measurements. The four extreme models are derived from modifications to the Merton structural model (which we term Xtreme-S), the CreditMetrics Transition model (Xtreme-T), Quantile regression (Xtreme-Q), and the author’s own unique iTransition model (Xtreme-i) which incorporates industry factors into transition matrices. For all models, CVaR is found to be significantly higher than VaR, and there are also found to be significant differences between the models in terms of correlation with actual bank losses and CDS spreads. The paper also shows how extreme measures can be used by banks to determine capital buffer requirements

Primary sector volatility and default risk in Indonesia

The Indonesian market is a critical market to the South East Asian region, being that region’s largest economy. The primary sectors of the Indonesian economy, incorporating Agriculture and Mining, are of critical importance to the country, representing approximately one quarter of GDP and providing nearly 40% of the nation’s employment. Mining and Agriculture stock returns significantly outperformed the Indonesian Stock Exchange (IDX) composite index in the five years leading up to Global Financial Crisis (GFC), and experienced savage falls during the GFC. Against this background, we examine the market and credit risk of these sectors during the pre-GFC, GFC and post-GFC periods. Market risk is measured using Value at Risk (VaR) and Conditional Value at Risk (CVaR). VaR is a popular metric which measures potential losses over a specific time period, up to a selected threshold. A key downside of this metric is that it says nothing of the extreme risk beyond VaR, which is a major limitation for this study, given the extreme volatility experienced by the primary sectors in Indonesia over the studied period. We therefore also use CVaR, which measures the extreme risk beyond VaR. For credit risk, we use the Merton-KMV Distance to Default (DD) metric, as well as our own Conditional DD (CDD) metric to measure extreme default risk. The key advantage that the Merton-KMV model has over other credit models, it that it incorporates fluctuating asset values. This makes it more responsive to changes in market conditions than most other credit models which remain static between rating periods. The importance of fluctuating asset values in measuring credit risk has been raised by the Bank of England (2008), who make makes the point that not only do asset values fall in times of uncertainty, but rising probabilities of default make it more likely that assets will have to be liquidated at market values. Similar to VaR, the Merton-KMV model has deficiencies in that it uses the standard deviation of asset value fluctuations, which tends to smooth the volatility and does not capture tail risk over that period. Our CDD model is able to measure risk at the most extreme times of the economic cycle, which is precisely when firms are most likely to fail, and when banks are most likely to experience high credit losses. We find that market risk for the primary industries is significantly higher than the broader market, and that there is a relatively higher difference between VaR and CVaR, indicating a higher tail risk. Mining, in particular has a higher market risk than other Indonesian sectors. Interestingly, this is not the case with credit risk, where the risk for Agriculture is lower than the overall market, and the risk for Mining is not significantly different to the overall market. This is because the leverage of a firm is a key component of the Merton-KMV model and we find the leverage for the Agriculture and Mining industries to be far more conservative than the broader market. This means that these primary sectors are able to withstand relatively higher levels of asset volatility. These findings can benefit both lenders and investors when considering the inclusion of these sectors in their investment or loan portfolio mix

Comparing Australian and US Corporate Default Risk Using Quantile Regression

The severe bank stresses of the Global Financial Crisis (GFC) have underlined the importance of understanding and measuring extreme credit risk. The Australian economy is widely considered to have fared much better than the US and most other major world economies. This paper applies quantile regression and Monte Carlo simulation to the Merton structural credit model to investigate the impact of extreme asset value fluctuations on default probabilities of Australian companies in comparison to the USA. Quantile regression allows modelling of the extreme quantiles of a distribution which allows measurement of capital and PDs at the most extreme points of an economic downturn, when companies are most likely to fail. Daily asset value fluctuations of over 600 Australian and US investment and speculative entities are examined over a ten year period spanning pre-GFC and GFC. The events of the GFC also showed how the capital of global banks was eroded as defaults increased. This paper therefore also examines the impact of these fluctuating default probabilities on the capital adequacy of Australian and US banks. The paper finds highly significant variances in default probabilities and capital between quantiles in both Australia and the US, and shows how these variances can assist banks and regulators in calculating capital buffers to sustain banks through volatile times

Modelling tail credit risk using transition matrices

Innovative transition matrix techniques are used to compare extreme credit risk for Australian and US companies both prior to and during the global financial crisis (GFC). Transition matrix methodology is traditionally used to measure Value at Risk (VaR), a measure of risk below a specified threshold. We use it to measure Conditional Value at Risk (CVaR) which is the risk beyond VaR. We find significant differences in VaR and CVaR measurements in both the US and the Australian markets. We also find a greater differential between VaR and CVaR for the US as compared to Australia, reflecting the more extreme credit risk that was experienced in the US during the GFC. Traditional transition matrix methodology assumes that all borrowers of the same credit rating transition equally, whereas we incorporate an adjustment based on industry share price fluctuations to allow for unequal transition among industries. Our revised model shows greater change between Pre-GFC and GFC total credit risk than the traditional model, meaning that those industries that were riskiest during the GFC are not the same industries that were riskiest Pre-GFC. Overall, our analysis finds that our innovative modelling techniques are better able to account for the impact of extreme risk circumstances and industry composition than traditional transition matrix techniques