論文：異方差隨機(jī)前沿模型對(duì)波動(dòng)性和經(jīng)營(yíng)效率的影響研究

論文：異方差隨機(jī)前沿模型對(duì)波動(dòng)性和經(jīng)營(yíng)效率的影響研究
——以兩岸銀行為例

摘 要：本文主要研究資產(chǎn)和收益的波動(dòng)性對(duì)兩岸銀行經(jīng)營(yíng)效率的影響。作為風(fēng)險(xiǎn)和經(jīng)營(yíng)不確定性的測(cè)度，波動(dòng)性會(huì)隨個(gè)體和時(shí)間的變化影響效率的水平和變異程度。當(dāng)證券資產(chǎn)波動(dòng)時(shí)，風(fēng)險(xiǎn)溢酬隨之變化，營(yíng)業(yè)利潤(rùn)的變化進(jìn)而影響經(jīng)營(yíng)計(jì)劃和經(jīng)營(yíng)效率。本文采用Wang’s (2002)的異方差隨機(jī)前沿模型進(jìn)行估計(jì)，這個(gè)模型的優(yōu)點(diǎn)在于除了常用的解釋變量之外，它還能允許無(wú)效率擾動(dòng)項(xiàng)的均值和方差因資產(chǎn)和收益的波動(dòng)而變化。我們的實(shí)證數(shù)據(jù)來(lái)源于一個(gè)提供國(guó)際銀行業(yè)全面資料的數(shù)據(jù)庫(kù)Bankscope，主要以臺(tái)灣和中國(guó)大陸的銀行為例進(jìn)行研究。所得結(jié)論與預(yù)期一致，即ROA的波動(dòng)性越大，銀行效率就會(huì)越低并且容易出現(xiàn)較大的變異。另一方面，股票收益率的波動(dòng)不會(huì)影響銀行效率，這意味著銀行管理人員應(yīng)該著重于改善和提高銀行的經(jīng)營(yíng)業(yè)績(jī)，而不是關(guān)注銀行的股價(jià)表現(xiàn)。
關(guān)鍵詞：波動(dòng)性；效率；成本函數(shù)；SFA，ROA
中圖分類號(hào)：G21; C67

Volatility and efficiency in a heteroscedastic stochastic frontier model:
the case of Taiwan and China banks

Abstract

We e*plore the role of asset and earning volatilities in determining the efficiency of international banks. As a measure of risk and operating uncertainty, volatility may influence both the levels and variabilities of efficiency across firms and over time. As equity volatility varies, changes in risk premiums may follow. Furthermore, the variability of operating profits may affect the level of planning difficulties and hence, will affect the operating efficiency. We adopt Wang’s (2002) heteroscedastic stochastic frontier model in our estimation. This model is an ideal framework for our study since it allows us to specify both the mean and variance of
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the properties of inefficiencies in U.S. bank holding companies derived from both stochastic and linear programming frontiers, they conclude that both methods produce “informative efficiency scores” and the stochastic frontier efficiency estimates are more closely related to risk-taking behavior, managerial competence and bank stock returns. Beccalli et al. (2006) make efficiency analysis comprising the sample of all banks publicly listed in France, Germany, Italy, Spain and UK; their results indicate that changes in cost efficiency are reflected in changes in stock prices. Altunbas et al. (2001) use a sample of German banks for empirical analysis, but find little evidence to suggest that privately owned banks are more efficient than their mutual and public-sector counterparts. Bonin et al. (2005) use an unbalanced panel consisting of 225 banks to investigate the effects of ownership, they find that foreign-owned banks are more cost-efficient and provide better service, in particular if they have a strategic foreign owner, while the remaining government-owned banks are less efficient in providing services. Berger et al. (2009) analyze the efficiency of Chinese banks over 1994-2003, their findings suggest that Big four banks are least efficient; foreign banks are most efficient; and minority foreign ownership is associated with significantly improved efficiency. While Lensink et al. (2008) draw a different conclusion; they use the SFA model for 2095 commercial banks in 105 countries and find that foreign ownership negatively affects bank efficiency.
The e*isting literature has very little empirical study on the relationship between stock return’s volatility and bank efficiency, not to mention some financial ratio’s volatility. In our paper, the empirical application is quite different. We adopt Wang’s (2002) heteroscedastic stochastic frontier model in the estimation, which allows us to specify both the mean and variance of the inefficiency turbulence and investigate the non-monotonic effects on efficiency.
The remaining of the paper is organized as follows. Section 2 introduces the theoretical Wang’s (2002) heteroscedastic stochastic frontier model. Section 3 specifies our empirical model and describes the data in details. Section 4 provides an empirical analysis and e*plains the relationship between volatility and bank efficiency. Section 5 concludes our study.

2.Method
The stochastic frontier approach, independently proposed by Aigner et al. (1977) and Meeusen and van den Broeck (1977), modifies the traditional assumption of deterministic production frontier. Those two works specify a composed error component which equal to the sum of two parts, i.e. a one-sided error that measures the non-negative inefficiency effects and random factors not controlled by the decision-making unit (DMU).
Furthermore, a number of studies e*tend SFA to accommodate panel data. Early e*tended panel data models consider the inefficiency effects as either time-invariant (e.g., Pitt and Lee, 1981; Schmidt and Sickles, 1984) or time-varying (e.g., Battese and Coelli, 1992; Lee and Schmidt, 1993). Moreover, Battese and Coelli (1995) apply single-stage ML procedure to investigate the determinants of inefficiency among DMUs and assume that inefficiency effects are a function of some DMU-specific factors. Recent efforts in modeling heteroscedasticity in inefficiency effects (uit) consider more fle*ible specification in two ways: Kumbhakar et al. (1991), Huang and Liu (1994), and Battese and Coelli (1995) assume the mean of uit (i.e., μit) would differ among DMUs. Caudill et al. (1995) assume μit is constant but allow the variance of uit () to be observation-specific.
More recently, Wang (2002) combines the feature of traditional models and those e*tended models above, and allow both μit and to be observation-specific. Suppose that total costs for the ith bank in year t represents as TCit. Yit and Pit are the input vector and the price of input vector, respectively. The heteroscedasticity stochastic frontier model specification for cost function can be presented as bellow:

where νit is the stochastic error term with i.i.d. normal distribution. Thi ……（未完，全文共30387字，當(dāng)前僅顯示5465字，請(qǐng)閱讀下面提示信息。收藏《論文：異方差隨機(jī)前沿模型對(duì)波動(dòng)性和經(jīng)營(yíng)效率的影響研究》）