Akhmad Kramadibrata

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:

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.

A Quantile Analysis of Default Risk for Speculative and Emerging Companies

Using quantile regression, this article examines default risk of emerging and speculative companies in Australia and the United States as compared to established investment entities. We use two datasets for each of the two countries, one speculative and one established. In the US we compare companies from the S&P 500 to those on the Speculative Grade Liquidity Ratings list (Moodys Investor Services, 2010). For Australia, we compare entities from the S&P/ASX 200 to those on the S&P/ASX Emerging Companies Index (EMCOX). We also divide the datasets into GFC and Pre-GFC periods to examine default risk over different economic circumstances. Quantile Regression splits the data into parts or quantiles, thus allowing default risk to be examined at different risk levels. This is especially useful in measuring extreme risk quantiles, when corporate failures are most likely. We apply Monte Carlo simulation to asset returns to calculate Distance to Default using a Merton structural credit model approach. In both countries, the analysis finds substantially higher default risk for speculative as compared to established companies. The spread between speculative company and established company default risk is found to remain constant in Australia through different economic circumstances, but to increase in the US during the GFC as compared to pre-GFC. These findings are important to lenders in understanding, and providing for, default risk for companies of different grades through varying economic cycles.Classification-JEL:

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.

South African regulatory reforms of funds of hedge funds

The financial services industry in South Africa is subject to strong regulation. Investments in funds of hedge funds (FoHFs) have been impacted by regulations such as limitations on pension fund investments and exchange controls that restrict the flow of funds overseas and encourage local investment. Nonetheless, South Africa has experienced strong FoHFs growth and changing regulation is opening up investment opportunities in these products. This chapter examines FoHFs in South Africa, with particular emphasis on the changing regulatory climate in South Africa before, during, and after the global financial crisis.

Understanding the Regulation Impact: US Funds of Hedge Funds After the Crisis

The aftermath of the Global Financial Crisis has heralded substantially increased regulation in the United States that impacts on funds of hedge funds (FoHFs). This chapter weighs up the pros and cons of this shifting regulation landscape, and examines the impact of key regulation such as the Dodd–Frank Act on funds and fund volumes. We also discuss potential flow-on effects of the European Alternate Investment Fund Managers directive on the US FoHFs industry.

Short Selling Stock Indices On Signals From Implied Volatility Index Changes: Evidence From Quantile Regression Based Techniques

Publisher Summary This chapter investigates the profitability of a strategy based on short selling indices. It is constructed on the principle of the importance of leverage effects, which relates to the fact that increases in volatility are linked with falls in stock prices and vice versa for decreases in volatility. Several new techniques based on the use of forward-looking implied volatilities on two indices, the FTSE100 and the S&P 500 are used to generate short and long trading strategies in the two indices. These strategies employ variants of quantile regression-based techniques, including linear quantile regression, kernel-based quantile regression, and quantile regression random forests, to predict quantile intervals and employ changes in these to generate a trading strategy. Out of these, Kernel-based quantile regression methods appear to generate the greatest returns in the hold-out sample periods and dominate buy and hold returns. Kernel quantile regression is an evolving quantile regression technique in the field of nonlinear quantile regressions. As it is capable of modeling the nonlinear behavior of time series data, it proves to be more efficient in forecasting risk than other methods, including linear quantile regression.

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: