Optimal investment with fixed financing costs

Models with a premium on external finance produce counterfactual predictions about liquidity management. We address this shortcoming by introducing a fixed cost of increasing external finance into an otherwise standard investment/financing problem. This additional financial friction is well-motivated by case studies and our analysis shows that it generates more realistic predictions about liquidity management: firms hold external finance and idle cash simultaneously, and may invest an additional dollar of cash flow in liquidity rather than repaying external funds or investing in productive capital. In addition to better fitting the stylized facts about the time-series and cross-sectional pattern of liquidity holding, these results may help shed light on the fragility of estimates of investment–cash flow sensitivities.     

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.     

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.     

Talent or Not? Employee Reactions to Talent Identification

In this article, we examine the effect of talent identification on employee attitudes. Building on social exchange theory, we analyze the association between employees' perceptions about whether or not they have been formally identified as “talent” and the following attitudinal outcomes: commitment to increasing performance demands, building skills, and supporting strategic priorities; identification with the unit and the multinational enterprise; and turnover intentions. Our analyses of 769 managers and professionals in nine Nordic multinational corporations reveal a number of differences between employees who perceive that they have been identified as “talent” and those who either perceive that they have not been identified or do not know whether they have been identified. We found only limited differences between the two latter categories.     

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.     

Factors influencing processes of radical change in organizational belief systems

The recent development of a cognitive, interpretive approach to organizational analysis has helped to increase our understanding of organizational action. This paper discusses the relationship between organizational beliefs and organizational change. Based upon a review of empirical studies of strategic reorientations, the article presents a model of factors that influence processes which lead to changes in organizational belief systems. It is argued that radical changes in organizational belief systems are influenced by organizational results, by characteristics of the organizational environment, by intra-organizational factors, and by characteristics of the current organizational belief systems. These factors are discussed and some ways to further augment our knowledge of this phenomenon are indicated.     

Framing the field of international human resource management research

This article presents an overarching framework of the international human resource management field. The framework has four different levels: Macro (encompassing countries, regions and industries), the Multinational Corporation, Unit (typically subsidiary) and Individual (including teams, employees and their family members). At each level, we make a distinction between Influencing Factors, the HRM Function (encompassing both the HR department and HR policies and practices), Proximal Outcomes and Distant Outcomes of HRM. The framework allows us to examine existing research and suggest avenues for future work.