2011年上半年国际金融市场运行综述

2011年上半年,美元总体走弱;美元短期利率下降,欧元、英镑短期利率上升,日元短期利率基本走平;主要国家中长期国债收益率先升后降;欧美主要股指振荡走高。     

基于KMV模型的中国短期融资券信用风险评级研究

针对短期融资券主体信用评级未能完全准确地反映出短期融资券信用风险的问题,本文引入信用风险计量的KMV模型,运用Matlab软件计算出短期融资券的违约距离,按照违约距离的大小通过聚类分析将样本划分为六组。在此基础上,以信用利差表示投资者对短期融资券信用风险的认可,将各组信用利差与其违约距离对应起来,对各组的信用利差进行方差分析,结果显示各组之间的差异非常显著,表明分组状况比较理想,按违约距离判断短期融资券的信用风险是合适的,实现了对短期融资券的信用风险评级。     

我国证券投资基金业绩持续性研究

基于2006~2009年的周度数据,本文首先使用修正的R/S方法实证分析开放式投资基金业绩的长期持续性;其次使用Spearman自相关系数方法计算了样本基金的短期持续性;并提出我国证券投资基金可能存在持续性时间阈的概念,利用样本数据对假设进行实证检验,计算出各只基金业绩持续性的时间阈,并分析了持续性时间阈对投资者的重要意义。     

丹麦风电的未来

丹麦是世界风电行业的领跑者。介绍了丹麦大力发展风电的驱动力,描述了风电发展给电力行业以及人民生活带来的影响,阐述了风电的社会经济效益。详细论述了丹麦通过负荷经济调度、电力进出口以及上下调制电能产量从而实现了电力消费的20％来自于风能。为实现欧盟的“20—20-20”目标,电网基础设施建设、灵活的生产与消费以及智能电网方案将为可再生能源尤其是风电的发展提供坚强的平台。负荷经济调度在新的方案下是实现社会经济效益最大化的有效措施。     

提高城区电网短期负荷预测准确率的方法

负荷预测是电力系统中的一个重要环节,是电力系统安全、经济、稳定运行的重要保障。针对我国大型城市中心城区负荷易受天气等突变因素影响而出现负荷曲线突变,导致短期负荷预测准确率降低的问题,以及城区负荷具有较明显的时间周期性特点,提出了通过考虑逐时、划分特定区域气象信息建立负荷预测模型,利用数据挖掘技术寻找相似日的方法,为提高短期负荷预测准确率提供一种思路。     

Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7

In this paper, we use survey data to analyze the accuracy, unbiasedness and efficiency of professional macroeconomic forecasts. We analyze a large panel of individual forecasts that has not previously been analyzed in the literature. We provide evidence on the properties of forecasts for all G7-countries and for four different macroeconomic variables. Our results show a high degree of dispersion of forecast accuracy across forecasters. We also find that there are large differences in the performances of forecasters, not only across countries but also across different macroeconomic variables. In general, the forecasts tend to be biased in situations where the forecasters have to learn about large structural shocks or gradual changes in the trend of a variable. Furthermore, while a sizable fraction of forecasters seem to smooth their GDP forecasts significantly, this does not apply to forecasts made for other macroeconomic variables.     

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and CI benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.     

Open economy forecasting with a DSGE-VAR: Head to head with the RBNZ published forecasts

We construct a DSGE-VAR model for competing head to head with the long history of published forecasts of the Reserve Bank of New Zealand. We also construct a Bayesian VAR model with a Minnesota prior for forecast comparison. The DSGE-VAR model combines a structural DSGE model with a statistical VAR model based on the in-sample fit over the majority of New Zealand’s inflation-targeting period. We evaluate the real-time out-of-sample forecasting performance of the DSGE-VAR model, and show that the forecasts from the DSGE-VAR are competitive with the Reserve Bank of New Zealand’s published, judgmentally-adjusted forecasts. The Bayesian VAR model with a Minnesota prior also provides a competitive forecasting performance, and generally, with a few exceptions, out-performs both the DSGE-VAR and the Reserve Bank’s own forecasts.     

Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models

We extend Diebold and Li’s dynamic Nelson-Siegel three-factor model to a broader empirical prospective by including the evaluation of the state space approach and by using nine different ratings for corporate bonds. We find that the dynamic Nelson-Siegel factor AR(1) model outperforms other competitors on the out-of-sample forecast accuracy, especially on the investment-grade bonds for the short-term forecast horizon and on the high-yield bonds for the long-term forecast horizon. The dynamic Nelson-Siegel factor state space model, however, becomes appealing on the high-yield bonds in the short-term forecast horizon, where the factor dynamics are more likely time-varying and parameter instability is more probable in the model specification.     

Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.