Measuring Integrated Market and Credit Risk in Bank Portfolios: An Application to a Set of Hypothetical Banks Operating in South Africa

The banking crises of the ‘90s emphasize the need to model the connections between financial environment volatility and the potential losses faced by financial institutions resulting from correlated market and credit risks. Due to the number of variables that must be modeled and the complexity of the relationships an analytical solution is not feasible. We present here a numerical solution based on a simulation model that explicitly links changes in the relevant variables that characterize the financial environment and the distribution of possible future bank capital ratios. This forward looking quantitative risk assessment methodology allows banks and regulators to identify potential risks before they materialize and make appropriate adjustments to bank portfolio credit qualities, sector and region concentrations, and capital ratios on a bank by bank basis. It also has the potential to be extended so as to assess the risks of correlated failures among a group of financial institutions (i.e., systemic risk analyses). This model was applied by the authors to the study of the risk profile of the largest South African Banks in the context of the Financial System Stability Assessment program undertaken by the IMF in 1999. In the current study, we apply the model to various hypothetical banks operating in the South African financial environment and assess the correlated market and credit risks associated with business lending, mortgage lending, asset and liability maturity matches, foreign lending and borrowing, and direct equity, real estate, and gold investments. It is shown to produce simulated financial environments (interest rates, exchange rates, equity indices, real estate price indices, commodity prices, and economic indicators) that match closely the assumed parameters, and generate reasonable credit transition probabilities and security prices. As expected, the credit quality and diversification characteristics of the loan portfolio, asset and liability maturity mismatches, and financial environment volatility, are shown to interact to determine bank risk levels. We find that the credit quality of a bank's loan portfolio is the most important risk factor. We also show the risk reduction benefits of diversifying the loan portfolio across various sectors and regions of the economy and the importance of accounting for volatility shocks that occur periodically in emerging economies. Banks with high credit risk and concentrated portfolios are shown to have a high risk of failure during periods of financial stress. Alternatively, banks with lower credit risk and broadly diversified loan portfolios across business and mortgage lending are unlikely to fail even during very volatile periods. Asset and liability maturity mismatches generally increase bank risk levels. However, because credit losses are positively correlated with interest rate increases, banks with high credit risk may reduce overall risk levels by holding liabilities with longer maturities than their assets. Risk assessment methodologies which measure market and credit risk separately do not capture these various interactions and thus misestimate overall risk levels.     

Virtual Currencies,the Money Market,and Monetary Policy

Virtual currencies are in vogue mainly due to two factors. First, as a protest against authority-driven monetary policy decisions and second, as alternatives to deficits in some monetary systems arising out of political instability or other causes. Assuming that virtual currencies indeed (partially) replace national currencies as payment vehicles, we attempt, in this article, to integrate the virtual currency supply and demand into the Keynesian money market framework. This article presents a few results for the central banks and outlines problems that may result for monetary policy formulation. Since this is the first such attempt to model a national money market as a combination of nationally-issued currency and globally-issued virtual currency, certain simplistic assumptions have been made. Nevertheless, the model offers directions on the impact of virtual currencies on the monetary system and the national money market. Additionally, the paper integrates the official standpoints of the European Central Bank and the Bank of England on this topic.     

A note on accounting conservatism in residual income and abnormal earnings growth equity valuation

The impact of conservative accounting on residual income (RI) and abnormal earnings growth (AEG) valuation models is investigated in this note. Limiting the analyses to information dynamics constrained models (the core models in Ohlson, 1995; Ohlson & Juettner-Nauroth, 2005), we find that both models can handle accounting conservatism if the persistence factors of residual income or abnormal earnings growth fulfil certain conditions. In a comparison of permissible time-series specifications, the AEG model can potentially handle more conservatively biased earnings in the first forecast period than the RI model. However, this requires that the growth of the conservative bias in the second forecast period is not too large. In a 0-NPV competitive equilibrium with a constant steady state growth, both models work equally well. Further elaborations indicate that, in the presence of accounting conservatism a reasonable value of the persistence factor of residual income in the RI model should be in the interval between 1.0 and R (where R = 1 + discount rate), whereas the persistence factor of abnormal earnings growth in the AEG model should be close to 1.0. This implies that the persistence factor in the RI model appears to have been understated while the persistence factor in the AEG model appears to have been overstated in previous empirical research.     

A push to cycling—exploring the e-bike's role in overcoming barriers to bicycle use with a survey and an intervention study

In Norway, as in many countries, there is a political goal to increase bicycle use. The electric bicycle (e-bike) is a promising tool for achieving this goal, given the hilliness of the country. However, little is yet known about the deterrents of cycling in Norway in general, and in particular how the purchase of an e-bike could be stimulated.

In the current study, 5500 respondents from a convenience sample among car owners were asked about their perceptions of bicycling in general, and of e-bikes in particular as well as their willingness to pay (WTP) for an e-bike. Randomly selected participants (N = 66) were given access to an e-bike for a limited time (2 or 4 weeks). A second questionnaire captured the same perceptions and WTP post-intervention. The results were compared with a control group (N = 214).

The results showed that those who cycle the least were most interested in buying an e-bike and that prior knowledge of the e-bike corresponded with a higher desire to buy one. Pro-environmental values did not predict interest in e-bikes, neither did norms and attitudes toward cycling. The WTP for an e-bike increased after having experienced the benefits for those who used an e-bike compared to those who did not. Price reduction of the e-bike (e.g. VAT exemption), spread of knowledge among the wider population, and actions to offer an e-bike experience may therefore be effective strategies for further expansion of the e-bike in the transport system and thereby to increase bicycle use in Norway.     

Trans‐Tasman Transmission of Government Spending Shocks

This paper investigates the international transmission of fiscal shocks between two closely‐linked, open economies. We estimate impulse response functions using a semi‐structural vector auto regressive (VAR) model and quarterly data from Australia and New Zealand for the period 1973:3–2008:4. We compare our empirical results with impulse response functions from a calibrated two‐country international real business cycle model with habit formation and adjustment costs to investment. We show that a positive shock to Australian government consumption leads to an increase in Australian output initially and then to a decline in the medium term, while the New Zealand output is negatively affected both in the short and medium term. This result is in line with the recent literature that reports beggar‐thy‐neighbour effect of positive government spending shocks.     

A comprehensive German business cycle chronology

Business cycle chronologies offer reference points for empirical studies used as benchmarks for business cycle and recession theory. A quasi-official chronology exists for the US economy, but not for most European countries, including Germany. While most papers dealing with business cycle dates rely on one specific method, I present and discuss a number of different dating approaches based on the classical business cycle. These are applied to German GDP data comprising 1970–2006. Finally, based on the results of the different methods, a consensus business cycle chronology for the German economy is suggested.     

Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP*

In this article, we merge two strands from the recent econometric literature. First, factor models based on large sets of macroeconomic variables for forecasting, which have generally proven useful for forecasting. However, there is some disagreement in the literature as to the appropriate method. Second, forecast methods based on mixed‐frequency data sampling (MIDAS). This regression technique can take into account unbalanced datasets that emerge from publication lags of high‐ and low‐frequency indicators, a problem practitioner have to cope with in real time. In this article, we introduce Factor MIDAS, an approach for nowcasting and forecasting low‐frequency variables like gross domestic product (GDP) exploiting information in a large set of higher‐frequency indicators. We consider three alternative MIDAS approaches (basic, smoothed and unrestricted) that provide harmonized projection methods that allow for a comparison of the alternative factor estimation methods with respect to nowcasting and forecasting. Common to all the factor estimation methods employed here is that they can handle unbalanced datasets, as typically faced in real‐time forecast applications owing to publication lags. In particular, we focus on variants of static and dynamic principal components as well as Kalman filter estimates in state‐space factor models. As an empirical illustration of the technique, we use a large monthly dataset of the German economy to nowcast and forecast quarterly GDP growth. We find that the factor estimation methods do not differ substantially, whereas the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the Factor MIDAS models, which confirms the usefulness of the mixed‐frequency techniques that can exploit timely information from business cycle indicators.     

Technical analysis and computer trading

The efforts of the central banks of Western industrial countries to stabilize exchange rates following the Louvre Agreement demonstrated yet again how difficult it can be to influence events in the foreign exchange markets. What factors are currently affecting exchange rates? What significance attaches to technical analysis in this context?