Tzu-Pu Chang

Decomposition of mutual fund underperformance

This article follows a three-stage data envelopment analysis (DEA) approach proposed by Fried et al. (2002) to decompose mutual fund underperformance, in order to obtain pure managerial performance. In the first stage, DEA is used to compute each funds performance. In the second stage, a stochastic frontier regression decomposes fund underperformance into characteristics (including fund and management attributes), managerial inefficiency, and statistical noise. In the third stage, DEA with slack-adjusted data is used to find out the pure performance. It is found that a funds performance significantly increases with its size, previous performance, managers tenure and education, while it decreases with the age of the fund and number of managed funds.

Total-Factor Energy Efficiency and Its Extensions: Introduction, Computation and Application

This chapter demonstrates how to use different types of DEA models to compute the total-factor energy efficiency (TFEE) scores, including CCR, Russell, QFI, SBM, DF, and DDF models. The TFEE is a disaggregate input efficiency index. Moreover, the TFEE framework which uses cross-section data can be extended to the total-factor energy productivity (TFEP) growth index by following Malmquist, Leunberger, and Malmquist-Leunberger models which use panel data. Finally, the regional data of Chinese regions during 2010–2011 with inputs and desirable as well as undesirable outputs are used for illustrating the computation of TFEE and TFEP scores.

Incorporating a leading indicator into the trading rule through the Markov-switching vector autoregression model

This article examines the profitability of trading rules based on the smoothed probability of Markov-switching models and executes two models in Taiwans case. The results present that both proposed models can earn excess returns over the buy-and-hold strategy and support that both can be used to trade. However, the univariate Markov-switching model, which only uses daily returns series does not successfully capture the trend in the stock market, especially during a bull market. This implies that high-frequency returns series contain lots of noises. In order to overcome this problem, the Markov-switching vector autoregression model that combines a leading indicator and returns is performed in this study. The results indicate a better trading pattern. We conclude that the leading indicator chosen from open interest in the future market increases useful information and reduces noises to improve model estimation, which can well identify the position of bull and bear markets.

Four-in-one waste recycling performance of local governments in Taiwan

Since 1998, the EPA (Environmental Protection Administration) of Taiwan has been promoting the four-in-one recycling system to reduce waste production and increase the collection of recyclable resources, but there has been no objective evaluation to assess the recycling performance of local governments. This paper introduces a useful indicator (recycling efficiency) for accurate and meaningful measures to achieve better recycling performance and efficiency, and analyses the recycling efficiency of 23 local governments in Taiwan during 1998–2004. The data envelopment analysis, with multiple inputs and outputs, is used in constructing a recycling efficiency indicator. This study takes up four input variables, including the budget for environmental protection per capita, waste-cleaning vehicles per capita, waste-cleaning persons per square kilometre, and participants in promoting activities per capita frequency, along with two outputs (recycling volume per capita and recycling rate). The local governments are divided into three groups, and four environmental variables are incorporated to analyse the effects. According to the results, Keelung City has the highest performance, and Kaohsiung City and Taipei County the least. The high-education population proportion, real mean household income, and urban population proportion all have a positive relation with recycling efficiency. These results may help the EPA and local governments form feasible and efficient recycling targets.

Energy and Pollution Efficiencies in China’s Regions

Along with rapid economic growth over the past decades, China is now facing a dual challenge of improving both energy and emission efficiencies. To trace the trends of energy and pollution efficiencies, this chapter proposes two indices: ecological total-factor energy efficiency (ETFEE), and ecological total-factor pollution efficiency (ETFPE) based on the Russell-based directional distance function. Using regional data from 2001 to 2011, the result shows that the ETFPE is always lower than the ETFEE for any area in China in the same year, indicating that the country is facing a more serious situation with respect to pollution control compared to energy saving.

The Context-Dependent Total-Factor Energy Efficiency of China’s Regions

This research introduces the context-dependent total-factor energy efficiency (CD-TFEE) and applies it to find the frontier levels of China’s regions in 2014. In this empirical example, CD-TFEE generates different results from the context-dependent data envelopment analysis (CD-DEA) proposed by Seiford and Zhu (Omega, 31(5):397–408, 2003). China’s regions in 2014 can be categorized into five levels of TFEE frontiers, and almost two-thirds of Chinese regions can target the domestic benchmarks to improve their energy efficiency.

The Linkage between Imported Energy and Trade in Taiwan

This study examines the Granger causality between Taiwans aggregate and disaggregate energy consumption and trade variables, including total imports (IM), total exports (EX), total energy consumption (ENERGY), oil and petroleum products consumption (OIL), coal and coal products consumption (COAL), natural gas consumption (GAS), export value of the industrial sector (EX_I), export value of heavy-chemical industrial products (EX_HI), and export value of non-heavy-chemical industrial products (EX_NHI) with monthly data during 1998–2009. Via applying Hsiaos version of the Granger causality method, the results find causality running from ENERGY to IM, EX, EX_I, and EX_HI. The impulse-response simulations show that the above relations have positive responses at the initial period. OIL Granger causes all trade variables. The impulse directions to IM and EX_NHI are negative, whereas others are positive. On the other hand, COAL responds to impulses in all trade variables. The impulse-response simulations show that these relations have positive responses at the initial period except for causality running from EX_HI and EX_NHI to COAL. GAS positively responds to impulses in EX, EX_I, and EX_HI at the initial period. The bidirectional Granger causality between pairs of variables (such as GAS and EX_NHI as well as GAS and EX_HI) is found.