Monte Carlo methods for estimating,smoothing, and filtering one- and two-factor stochastic volatility models

One- and two-factor stochastic volatility models are assessed over three sets of stock returns data: S&P 500, DJIA, and Nasdaq. Estimation is done by simulated maximum likelihood using techniques that are computationally efficient, robust, straightforward to implement, and easy to adapt to different models. The models are evaluated using standard, easily interpretable time-series tools. The results are broadly similar across the three data sets. The tests provide no evidence that even the simple single-factor models are unable to capture the dynamics of volatility adequately; the problem is to get the shape of the conditional returns distribution right. None of the models come close to matching the tails of this distribution. Including a second factor provides only a relatively small improvement over the single-factor models. Fitting this aspect of the data is important for option pricing and risk management.     

A Fuzzy clustering approach to the key sectors of the Spanish economy

The search for key sectors in an economy has been and still is one of the more recurrent themes in input–output analysis. When using clustering techniques, sectors can only belong to a group, having a particular performance. But, actually, the same sector could be important from different perspectives at the same time, to a different degree. So, a fuzzy clustering approach is needed. In this work we propose a multidimensional approach to classify the productive sectors of the Spanish input–output table for 1995, based on three groups of variables: those related to their productive integration, others measuring their specific weight in the economy and finally some showing their economic dynamic. We also incorporate into the analysis the technological level, which being a categorical variable presents special methodological problems. All these questions are tackled applying a robust and fuzzy clustering analysis, which gives as a result a classification of sectors illustrating the role that each one plays in the Spanish economy.     

Non-maximizing output behavior for firms with a cost-constrained technology

In many public service industries, firms are constrained by a cost (budget) and characterized by non-maximizing output behavior, due to bureaucratic behavior, for instance. This paper proposes a model based on the assumption that firms with a cost constraint do not maximize service levels due to resource preferences. It derives the exact relationships between services delivered, (shadow) input prices, cost constraints, and optimal input quantities. From these relationships, allocative efficiencies, technical efficiencies, output ray elasticities, and marginal cost can easily be derived.      

Decomposing productivity patterns in a conditional convergence framework

In this study we examine regional data on per worker GDP, disaggregated at sectoral level, by focusing our interest on the role of differences in the sectoral composition of activities, and in productivity gaps that are uniform across sectors, in explaining the catching-up process, which is realized through physical and human capital as well as technological knowledge accumulation. Our objective is to investigate how much of the interregional inequality in aggregate productivity per worker is imputable to each component. A methodology for identifying and analyzing sources of inequality from a decomposed perspective is developed in the growth framework by combining a shift-share based technique and a SUR model specification for the conditional-convergence analysis. The proposed approach is employed to analyze aggregate interregional inequality of per worker productivity levels in Italy over the period 1970–2000. With respect to the existing empirical results, our approach provides a more comprehensive and detailed examination of the contribution of each identified component in explaining the regional productivity gaps in Italy. It is argued that region-specific productivity differentials, uniform across sectors, explain a quite large share of differences in productivity per worker. However, sectoral composition plays a non negligible role, although decreasing since the end of 1980s, and very different productivity patterns emerge within geographical areas.

Determination of a hospital management policy using conjoint analysis in the analytic network process

When making management performance decisions, administrators frequently utilize balanced scorecard (BSC) to measure performance because the BSC does not excessively focus on financial measures and seeks a balance among customer perspectives, learning and growth, internal business processes and financial measures. A growing number of studies have applied the analytic hierarchical process (AHP) method to choose BSC metrics for management performance. These AHP methods assume that criteria are independent. Saaty (Decision making with dependence and feedback: the analytic network process. RWS Publications, Pittsburgh, 1996) presented the analytic network process (ANP) method to solve the limitations that criteria are independent. Conventional ANP methods involve complex calculations when the number of criteria increases. Hence, this study attempts to integrate conjoint analysis (CA) and the ANP method to simplify the ANP calculation procedure. Additionally, the decision of management policy is utilized with BSC to determine whether the situation at a hospital demonstrates the feasibility of integrating CA with ANP. Study results reveal that the proposed methodology increases the efficiency of decision making among policymakers and reduces associated risks     

The political uncertainty and stock market behavior in emerging democracy: the case of Taiwan

This paper examines the congressional effect between the pre- and post-democratization on the stock market by the asymmetric Generalized Autoregressive Conditional Heterosce desticity (GARCH) model in the period 1984–2004. The results found that the congressional effect is negative effect on stock returns but volatility is not significant. However, the democratic effect on stock returns is negative and increased of volatility. Moreover, the congressional effect on stock market returns following democratization significantly exceeds that before democratization, but have no significant effect for the volatility in the same circumstances. These results provide evidences consistent with the contention of liberalization (Hayek, Am. Econ. Rev. 35, 519–530, Individualism and Economic order, The university of Chicago press, Chicago, London, 1945, 1948; Popper, The open society and its Enemies, Princeton university, NJ, 1950).     

Incorporating a tracking signal into a state space model

It is a common practice to complement a forecasting method such as simple exponential smoothing with a monitoring scheme to detect those situations where forecasts have failed to adapt to structural change. It will be suggested in this paper that the equations for simple exponential smoothing can be augmented by a common monitoring statistic to provide a method that automatically adapts to structural change without human intervention. The resulting method, which turns out to be a restricted form of damped trend corrected exponential smoothing, is compared with related methods on the annual data from the M3 competition. It is shown to be better than simple exponential smoothing and more consistent than traditional damped trend exponential smoothing.