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In this paper, we consider the disassembly-to-order (DTO) problem, where a variety of returned products are disassembled to fulfill the demand for specified numbers of components and materials. The objective is to determine the optimal numbers of returned products to disassemble so as to maximize profit and minimize costs. We model the DTO problem using a multi-criteria decision-making approach. Since the conditions of returned products are unknown, the yields from disassembly are considered to be stochastic. To solve the stochastic problem, we use one of the two heuristic approaches (viz., one-to-one approach or one-to-many approach) that converts the problem into a deterministic equivalent. We compare the performance of the two heuristic approaches using a case example.

This study examines the effect of using a computerized decision aid on student cognitive effort and learning in the first tax course. Students at a mid-western university in the United States prepared a 1040 tax return using either paper or tax software from a given set of taxpayer information. Students using paper forms reported higher levels of cognitive effort than did students using the tax software, however, no association between self-efficacy and cognitive effort was found. A test for association between decision aid type and inferential (higher-level) learning (the third level of Bloom's taxonomy) found cognitive effort to be statistically significant. The study also found a significant interaction between cognitive effort and experience. These results suggest that paper forms, which require students to work through task processes, may be better instructional tools for helping students acquire a deeper understanding of subject matter. Although tax software provides potential benefits of increased accuracy and speed, practitioners should be aware of its limitations as a learning tool.

This chapter reassesses the economics of interest rate risk management in light of the global financial crisis by developing a derivative-based integrated treatment of interest rate and credit risk interrelation. The decade-long historical data on credit default swap spreads and interest rate swap rates are used as proxy measures for credit risk and interest rate risk, respectively. An elasticity of interest rate risk and credit risk, considered a function of the business cycle phases, maturity of instruments, economic sector, creditworthiness, and other macroeconomic parameters, is investigated for optimizing economic capital. This chapter sheds light on how financial institutions may address hedge strategies against downside risks implementing the proposed derivative-based integrated treatment of interest rate and credit risk assessment allowing for optimization of interest rate swap contracts. The developed framework of integrated interest rate and credit risk management is of special importance for emerging markets heavily dependent on foreign capital as it potentially allows emerging market banks to improve risk management practices in terms of capital adequacy and Basel III rules. Analyzing diversification versus compounding effects, it allows enhancing financial stability through jointly optimizing Pillar 1 and Pillar 2 economic capital.

This chapter explores the yield curve movements in the interest rate swap markets of four major currencies, the Japanese yen (JPY), the US dollar (USD), the pound sterling (GBP), and the Swiss franc (CHF), by principal component analysis (PCA), focusing on the explanatory power of each driver. Comparing the cumulative proportions of the first three principal components, the “level” changes seem to explain the yield curve movements far better than the “ratio” changes in the case of the JPY (96.1% vs. 38.3%) and CHF (97.2% vs. 41.9%), and they are only marginally worse for the USD (97.7% vs. 98.5%) and GBP (96.5% vs. 98.3%). In all markets, the explanatory power (proportion) of the first PC (PC1) is over 82%, and most of the movements can be explained by it. Furthermore, the explanatory power (cumulative proportion) from PC1 up to the third PC (PC3) is over 96%. Thus, it can be considered that most of the movements can be explained by the first three PCs. In addition, we investigate whether there is a structural change in yield curve movements before and after the global financial crisis of 2007–2008 (GFC). If we use daily “level” changes for the PCA, the GFC has no impact on the yield curve movements for all major currencies. The three PCs retain good explanatory power.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

Recently the world economy was confronted to the worst financial crisis since the great depression. This unprecedented crisis started in mid-2007 had a huge impact on the European government bond market. But what are the main drivers of this “perfect storm” that since 2009 affects EU government bond market as well? To answer this question, we propose an empirical study of the determinants of the sovereign bond spreads of EU countries with respect to Germany during the period 2003–2010. Technically, we address two main questions. First, we ask what share of the change in sovereign bond spreads is explained by changes in the fundamentals, liquidity, and market risks. Second, we distinguish between EU member states within and outside the Euro area and question whether long-term determinants of spreads affect EU members uniformly. To these ends, we employ panel data techniques in a regression model where spreads to Germany (with virtually no default risk) are explained by set of traditional variables and a number of policy variables. Results reveal that large fiscal deficits and public debt as well as political risks and to a lesser extent the liquidity are likely to put substantial upward pressures on sovereign bond yields in many advanced European economies.

This paper compares alternative estimation procedures for multi-level factor models which imply blocks of zero restrictions on the associated matrix of factor loadings. We suggest a sequential least squares algorithm for minimizing the total sum of squared residuals and a two-step approach based on canonical correlations that are much simpler and faster than Bayesian approaches previously employed in the literature. An additional advantage is that our approaches can be used to estimate more complex multi-level factor structures where the number of levels is greater than two. Monte Carlo simulations suggest that the estimators perform well in typical sample sizes encountered in the factor analysis of macroeconomic data sets. We apply the methodologies to study international comovements of business and financial cycles.