Credit Supply and Demand and the Australian Economy

The paper explores the lending behaviour of financial intermediaries over the business cycle in the light of new theories emphasising agency costs. During a “credit crunch” loans from financial intermediaries are unobtainable at any price, so that credit may have a “causal” role in influencing economic outcomes in the short run. Tests of this phenomenon show that it is not supported by the Australian data. However, while credit may not “cause” economic activity it may, nevertheless, have useful leading indicator properties. This is because the demand for credit is based on expectations about future demand as well as the current cost of credit. Indeed, monetary policy operates in part via intertemporal substitution in demand, which is reflected in, though not caused by, the behaviour of credit. These properties of credit are shown to be broadly consistent with Australian data.

Australian Banking Risk: The Stock Market’s Assessment and the Relationship Between Capital and Asset Volatility

The likelihood of a bank failing, within a given period of time, is a function of the variability in its income and its ability to withstand losses. These determinants depend, in turn, on the volatility of the return on bank assets and the bank’s level of capital. Although accounting measures of the volatility of the rate of return on bank assets and bank capital-asset ratios are published on a regular basis, market prices provide alternative risk measures. This paper uses share prices to estimate these risks measures for 15 Australian banks that were listed on the Australian Stock Exchange for all, or part of, the period 1983 to 1998. Option prices are also used to generate alternative estimates of these risk measures, the results of which corroborate those obtained from share prices. We find that the market’s assessment of the capital-asset ratio for the Australian banking sector has risen considerably over the sample period. There has also been a slight upward trend in the volatility of asset returns. These two trends have opposite effects on the market’s assessment of total bank risk: rising capital-asset ratios reduce bank risk, but rising asset volatility increases it. To uncover which trend has dominated, we examine a couple of measures of total bank risk, which summarises the net impact of movements in both the capital-asset ratio and asset volatility. These additional risk measures suggest that the riskiness of the sector has declined. In investigating the relationship between banks’ capital-asset ratio and asset volatility over time, we find that increases in the growth of the capital-asset ratio precede increases in asset volatility which, in turn, cause a slowdown in capital growth.solvency; capital; banking

A Decade of Australian Banking Risk: Evidence from Share Prices

The stability of the banking sector has long been a matter of concern for public policy. The likelihood of bank failure depends on two factors: (i) the variability of bank income (which primarily reflects the variability of the rate of return on bank assets), and (ii) the capacity of a bank to absorb losses in the short run (which depends on bank capital). Accounting measures of the volatility of the rate of return on bank assets and bank capital ratios may not reflect the appropriate economic concepts. In this paper, we use share price data to calculate the economic values of Australian bank asset volatilities, capital ratios and the potential public sector liability which might arise as a result of claims by depositors of a failed bank. The public sector liability is found to be extremely small. We find that the estimated capital ratio for the Australian banking sector has risen over the past decade, while there has been no noticeable increase in the riskiness of banks. A preliminary investigation of the relation between asset volatility and bank capital is conducted which suggests that riskier banks do tend to maintain higher capital ratios, and that there is a positive relation between the two variables across time.

Default Risk and Derivatives: An Empirical Analysis of Bilateral Netting

This paper discusses the determination of a capital charge to cover default risk on a netted derivatives portfolio. Different methods of setting a capital charge are investigated. Their ability to track a more sophisticated measure of credit risk is tested for Australian banks’ portfolios. The effect on the level of credit risk of moving from an environment without bilateral netting, to one where netting has firm legal basis, is examined. We find that, while there are theoretical grounds for arguing that more sophisticated measures would track exposures more closely than the approach currently used in capital adequacy requirements, as an empirical matter, no single formulation clearly outranked any other.

Measuring Traded Market Risk: Value-at-risk and Backtesting Techniques

The proposed market-risk capital-adequacy framework, to be implemented at the end of 1997, requires Australian banks to hold capital against market risk. A fundamental component of this framework is the opportunity for banks to use their value-at-risk (VaR) models as the basis of the market-risk capital charge. Value-at-risk measures the potential loss on a portfolio for a specified level of confidence if adverse movements in market prices were to occur. This paper examines the VaR measure and some of the techniques available for assessing the performance of a VaR model. The first section of the paper uses a simple portfolio of two spot foreign exchange positions to illustrate three of the approaches used in the calculation of a VaR measure: variance-covariance, historical simulation and Monte-Carlo simulation. It is concluded that, although VaR is a very useful tool, it is not without its shortcomings and so should be supplemented with other risk-management techniques. The second section of the paper focuses on the use of backtesting – the comparison of model-generated VaR numbers with actual profits and losses z– for assessing the accuracy of a VaR model. Several statistical tests are demonstrated by testing daily VaR and profit and loss data obtained from an Australian bank. The paper concludes that, although the tests are not sufficiently precise to form the basis of regulatory treatment of banks’ VaR results, the tests do provide useful diagnostic information for evaluating model performance.

Explaining Forward Discount Bias: Is it Anchoring?

Anchoring is a well-documented behaviour pattern. It occurs when agents form their expectations of an objective variable by only partially adjusting from some given starting value. We present a model of the foreign exchange market in which there are two types of traders: those who are fully rational and those whose expectations are anchored to the forward exchange rate. Under plausible conditions, a significant proportion of the anchored traders survive in the market in the long-run. The model explains both forward discount bias in the direction consistently observed in foreign exchange markets and the results of surveys of market participants’ exchange rate expectations.

Value at Risk: On the Stability and Forecasting of the Variance-covariance Matrix

Over the past decade value at risk (VaR) has become the most widely used technique for the quantification of market-risk exposure. VaR is a measure of the potential loss that may occur from adverse moves in market prices (interest rates, exchange rates, equity prices and so forth). The capacity for a VaR measure to accurately predict future risk exposures depends upon the forecasts of the volatility of market rates and the correlations between the various market rates (that is, the variance-covariance matrix) incorporated into the VaR model. In this paper we first present the results of tests of the stability of the variances, covariances and correlations for exchange rates and Australian interest rates. Secondly, we assess the performance of several time-series models that may be used to forecast the variance-covariance matrix. In particular three models for the variance-covariance matrix are considered: equally weighted historical variances and covariances, exponentially weighted averages of historical variances and generalised autoregressive conditional heteroskedasticity (GARCH). We conclude that simple models perform as well as their more sophisticated GARCH counterparts.value at risk; market risk; volatility; correlation; GARCH