Interventions in Markets with Adverse Selection: Implications for Discount Window Stigma

I study the implications for central bank discount window stigma of a workhorse model of adverse selection in financial markets. In the model, firms (banks) need to borrow to finance a productive project. There is limited liability and firms have private information about their ability to repay their debts, which gives rise to the possibility of adverse selection. The central bank can ameliorate the impact of adverse selection by lending to firms. Discount window borrowing is observable and it may be taken as a signal of firms' credit worthiness. Under some conditions, firms borrowing from the discount window may pay higher interest rates to borrow in the market, a phenomenon often associated with the presence of stigma. I discuss these and other outcomes in detail and what they suggest about the relevance of stigma as an empirical phenomenon.     

Pricing Defaultable Bonds Using a Lévy Jump‐Diffusion Model

This paper uses a reduced‐form approach to derive a closed‐form pricing formula for defaultable bonds. The authors specify the default hazard rate as an affine function of multiple variables which follow the Lévy jump‐diffusion processes. Because such specification allows greater flexibility in the generation of a valid probability of default, their pricing model should be more accurate than the valuation models in traditional studies, which ignore the jump effects. This paper also proposes a new method for estimating the parameters in a Lévy Jump‐diffusion process. The real data from the Taiwanese bond market are used to illustrate how their model can be applied in practical situations. The authors compare the pricing results for the influential variables with no jump effects, with jump magnitudes following the normal distribution, and with jump magnitudes following the gamma distribution. The results reveal that the predictive ability is the best for the model with the jump components. The valuation model shown in this paper should help portfolio managers more accurately price defaultable bonds and more effectively hedge their portfolio holdings.     

Costs and potential funding of expanded public pension coverage in Asia

Public pension burdens in most emerging Asian economies are still relatively small. However, there are a number of reasons to believe that they will increase markedly in the coming years. First, many Asian economies will face rapidly ageing populations, which will raise pension and other old‐age‐related spending dramatically. Second, as economies develop, political pressures to expand the coverage of public pensions and raise pension benefits will likely increase. The first objective of this paper is to identify the potential fiscal burden of public pensions in 23 emerging Asian economies, based on econometric models and forecasts of GDP and demographic trends. Using two different methodologies yields estimated increases in the average share of public pension expenditures in GDP of 1.0 percentage point and 3.6 percentage points by 2030 compared with current levels. We believe the latter estimate is more realistic. The second objective is to recommend policies to provide adequate funding for public pension needs, including enhancing the efficiency of social insurance programs, improving the balance of revenues and expenditures, implementing more explicit fiscal rules and frameworks, and establishing stronger fiscal surveillance at the national and regional levels.     

Prediction based on entrepreneurship-prone personality profiles: sometimes worse than the toss of a coin

Small Business Economics - The human personality predicts a wide range of activities and occupational choices—from musical sophistication to entrepreneurial careers. However, which method...     

Sparse Signals in the Cross‐Section of Returns

This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling one‐minute‐ahead return forecasts using the entire cross‐section of lagged returns as candidate predictors. The LASSO increases both out‐of‐sample fit and forecast‐implied Sharpe ratios. This out‐of‐sample success comes from identifying predictors that are unexpected, short‐lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identify predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals.     

Towards an operations strategy model of servitization: the role of sustainability pressure

Servitization requires an important strategic shift to drive changes in the operations of manufacturing firms. Using a large-scale survey, the purpose of this paper is (1) to build and validate an operations strategy model of servitization confirming previous case study findings on servitization as a strategic action and (2) to explore the role of sustainability pressures in, and the sustainability performance effects of, pursuing service-based operations strategies. To reach these objectives, a dataset including the responses of 735 manufacturing plants from 21 different countries is analyzed using the PLS-SEM method. The results indicate that the sustainability pressures of stakeholders can push manufacturers to adopt a service-based operations strategy, materializing in the provision of both basic (product-oriented) and advanced (customer-oriented) services (BAS and ADS). Our analysis further indicates that while offering BAS is a precondition for ADS provision, only ADS can offer a competitive edge for manufacturers, both in terms of service and sustainability-related operational performance.     

Demonstration of a tool for automatic learning and re-use of knowledge in the activated sludge process.

Wastewater treatment plant operators encounter complex operational problems related to the activated sludge process and usually respond to these by applying their own intuition and by taking advantage of what they have learnt from past experiences of similar problems. However, previous process experiences are not easy to integrate in numerical control, and new tools must be developed to enable re-use of plant operating experience. The aim of this paper is to investigate the usefulness of a case-based reasoning (CBR) approach to apply learning and re-use of knowledge gained during past incidents to confront actual complex problems through the IWA/COST Benchmark protocol. A case study shows that the proposed CBR system achieves a significant improvement of the benchmark plant performance when facing a high-flow event disturbance.