1998年中国上市公司并购实践的效应分析

对上市公司的收购是中国股市近些年出现的新现象 ,本文以 1 998年深沪两市发生的全部 67家公司的并购为样本 ,以公司净资产收益率 (NROA)和主业利润率(CROA)为指标 ,将所有样本根据并购原因划分为 6类 ,以并购前 2年和并购后 3年的数据为基础 ,系统分析了并购的效应与得失。在此基础上 ,还讨论了国有企业与民营企业作为收购方的并购效应和并购的有偿转让与无偿划拨方式的不同结果。文章的结论是 :业绩较差的公司较愿出让控股权 ;多数并购是战略性的 ,获上市地位是主要的并购动力 ;并购后主业得到明显加强 ;市场化的战略性并购效果较好 ,有偿并购的效果也较好。     

我国商业银行操作风险度量模型的选择

操作风险涉及银行经营活动的所有领域、各个环节和所有人员，不同银行、不同业务、不同环节的操作风险特征都不相同。操作风险度量是对操作风险进行经济资本配置的基础，目前还没有普遍适用的操作风险度量方法，现有的一些主流模型没有充分考虑内部控制对操作风险的影响和操作风险的因果性特征。因此，操作风险度量模型应考虑到其特征，既要综合主观和客观两方面的因素，也要可以灵活地进行动态调整。考虑到我国商业银行操作风险管理的实际，在操作风险度量模型的选择上，可用内部控制评价结果调整的基本指标法和标准法作为自上而下的度量模型，用贝叶斯网络技术作为自下而上的度量模型。     

U.S. Monetary Regimes and Optimal Monetary Policy in the Euro Area

Monetary policy in the United States has been documented to have switched from reacting weakly to inflation fluctuations during the 1970s, to fighting inflation aggressively from the early 1980s onward. In this paper, I analyze the impact of the U.S. monetary policy regime switches on the Eurozone. I construct a New Keynesian two‐country model where foreign (U.S.) monetary policy switches regimes over time. I estimate the model for the U.S. and the Euro Area using quarterly data and find that the United States has switched between those two regimes, in line with existing evidence. I show that foreign regime switches affect home (Eurozone) inflation and output volatility and their responses to shocks, substantially, as long as the home central bank commits to a time‐invariant interest rate rule reacting to domestic conditions only. Optimal policy in the home country instead requires that the home central bank reacts strongly to domestic producer‐price inflation and to international variables, such as imported goods relative prices. In fact, I show that currency misalignments and relative prices play a crucial role in the transmission of foreign monetary policy regime switches internationally. Interestingly, I show that only marginal gains arise for the Euro Area when the European Central Bank (ECB) adjusts its policy according to the monetary regime in the United States. Thus, a simple time‐invariant monetary policy rule with a strong reaction to Producer Price Index (PPI) inflation and relative prices is enough to counteract the effects of monetary policy switches in the United States.     

New development: Unified scores of governance capacity using a Bayesian latent variable analysis

A plethora of governance measures has been developed to assess governance capacity in different countries. Choosing one indicator over another involves discretionary decision issues. This article presents a new unified measure of governance capacity for the administrative and political dimensions. The unified measure provides a more accurate understanding of governance than single indices.     

Systemic event prediction by an aggregate early warning system: An application to the Czech Republic

This work develops an early warning framework for assessing systemic risks and predicting systemic events over a short horizon of six quarters and a long horizon of 12 quarters on a panel of 14 countries, both advanced and developing. First, we build a financial stress index to identify the starting dates of systemic financial crises for each country in the panel. Second, early warning indicators for the assessment and prediction of systemic risk are selected in a two-step approach; we find relevant prediction horizons for each indicator by a univariate logit model followed by the application of Bayesian model averaging to identify the most useful indicators. Finally, we observe the performance of the constructed EWS over both horizons on the Czech data and find that the model over the long horizon outperforms the EWS over the short horizon. For both horizons, out-of-sample probability estimates do not deviate substantially from their in-sample estimates, indicating a good out-of-sample performance for the Czech Republic.     

Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals,financial stress and economic uncertainty

We develop models for examining possible predictors of growth of China's foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US–China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided.     

Forecasting macroeconomic variables in emerging economies

Forecasting macroeconomic variables in rapidly changing emerging economies presents a number of challenges. In addition to structural changes, the time-series data are usually available only for a short number of periods, and predictors are available in different lengths and frequencies. Dynamic model averaging (DMA), by allowing the forecasting model to change dynamically over time, permits the use of predictors with different lengths and frequencies for the purpose of forecasting in a rapidly changing economy. This study uses DMA to forecast inflation and growth in Vietnam, Thailand, Philippines, Sri Lanka and Ghana. We compare its forecasting performance with a wide range of other time-series methods. We find that the size and composition of the optimal predictor set changed, indicating changes in the economic relationships over time. We also find that DMA frequently produces more accurate forecasts than other forecasting methods for both inflation and economic growth in the countries studied.     

Real exchange rates: are they dominated by fundamental factors?

Using Bayesian model averaging, we determine which fundamental pair-wise differences suggested by the literature on optimum currency areas give the best explanation of medium-term variability of bilateral real exchange rates. The intercept in the best specification is statistically insignificant, implying that for a hypothetical pair of economies for which the differences were zero, the bilateral real exchange rate would not move. Thus, the ‘non-fundamental’ element of the medium-term real exchange rate variability is, in our sample at least, negligible on average. In other words, floating exchange rate does not in itself imply, on average, more real exchange rate variability in the medium term than an exchange rate peg.     

On estimating long-run effects in models with lagged dependent variables

A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment.Our analysis points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results.Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. While many of the underlying problems have been long-known in the literature, the continued widespread use of the associated empirical procedures suggests that researchers are unaware of the extent and severity of the estimation problems. This study aims to illustrate their practical importance for applied research.