Co-dependence of extreme events in high frequency FX returns

In this paper, we investigate extreme events in high frequency, multivariate FX returns within a purposely built framework. We generalize univariate tests and concepts to multidimensional settings and employ these novel techniques for parametric and nonparametric analysis. In particular, we investigate and quantify the co-dependence of cross-sectional and intertemporal extreme events. We find evidence of the cubic law of extreme returns, their increasing and asymmetric dependence and of the scaling property of extreme risk in joint symmetric tails.     

The one-trading-day-ahead forecast errors of intra-day realized volatility

Two volatility forecasting evaluation measures are considered; the squared one-day-ahead forecast error and its standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility forecasting accuracy. Additionally, we explore the forecasting accuracy based on the squared distance of the forecast error standardized with its volatility. The statistical properties of the forecast errors point the standardized version as a more appropriate metric for evaluating volatility forecasts.We highlight the importance of standardizing the forecast errors with their volatility. The predictive accuracy of the models is investigated for the FTSE100, DAX30 and CAC40 European stock indices and the exchange rates of Euro to British Pound, US Dollar and Japanese Yen. Additionally, a trading strategy defined by the standardized forecast errors provides higher returns compared to the strategy based on the simple forecast errors. The exploration of forecast errors is paving the way for rethinking the evaluation of ultra-high frequency realized volatility models.     

基于BSP-HAR-RV模型的最优抽样频率研究

金融高频时间序列由于数量大,周期短,信息丰富从而可以很好的反映金融市场特征。通过绘出平均双幂变差已实现波动率散点图(BiPowe Realized Volatility Signature Plot,BSP),建立BSP-HAR-RV模型,改进以往国内通过列举法选择最优频率的方法。最后对TCL集团股票价格一年多的高频数据实证分析,验证模型结果,并将其在最优频率下测量得到的HAR-RV模型预测结果与以往广泛使用的5min、10min频率下得到的结果进行比较,发现最优抽样频率下模型预测能力较好,具有可行性。     

Election frequency,choice fatigue,and voter turnout

Influential scholars have argued that frequent elections lead to voter fatigue and can therefore be directly responsible for low turnout in countries characterized by frequent contests. However, other theories predict that frequent elections can even increase turnout. The existing empirical evidence is problematic as it simply correlates election frequency with turnout. By contrast, I exploit a natural experiment in the German state of Hesse, where voters from different municipalities faced the same electoral contest but experienced different election frequency, due to the staggered timing of some local elections. I find that when two elections are scheduled within a relatively short period of time, voter turnout at the later election is significantly reduced. This effect is stronger when the election is deemed less important in the eyes of the voters. Election frequency thus might also partly explain the wide turnout gap between first- and second-order elections, as suggested by Lijphart (1997).     

A Comparison of two Robust Estimation Methods for Business Surveys

Two alternative robust estimation methods often employed by National Statistical Institutes in business surveys are two‐sided M‐estimation and one‐sided Winsorisation, which can be regarded as an approximate implementation of one‐sided M‐estimation. We review these methods and evaluate their performance in a simulation of a repeated rotating business survey based on data from the Retail Sales Inquiry conducted by the UK Office for National Statistics. One‐sided and two‐sided M‐estimation are found to have very similar performance, with a slight edge for the former for positive variables. Both methods considerably improve both level and movement estimators. Approaches for setting tuning parameters are evaluated for both methods, and this is a more important issue than the difference between the two approaches. M‐estimation works best when tuning parameters are estimated using historical data but is serviceable even when only live data is available. Confidence interval coverage is much improved by the use of a bootstrap percentile confidence interval.     

Technological heterogeneity and corporate investment

We propose an importance-sampling procedure to improve the computational performance of the simulated method of moments (SMM) for the estimation of structural models with fixed parameter heterogeneity. The main advantage of the procedure is that it does not require to simulate observations every time that the structural parameters change during the minimization of the SMM criterion function. We illustrate the use of our method by estimating a neoclassical model of investment for a sample of US manufacturing companies, allowing the technological parameters to vary across firms.     

The analysis and delimitation of Central Business District using network kernel density estimation

Central Business District (CBD) is the core area of urban planning and decision management. The cartographic definition and representation of CBD is of great significance in studying the urban development and its functions. In order to facilitate these processes, the Kernel Density Estimation (KDE) is a very efficient tool as it considers the decay impact of services and allows the enrichment of the information from a very simple input scatter plot to a smooth output density surface. However, most existing methods of density analysis consider geographic events in a homogeneous and isotropic space under Euclidean space representation. Considering the case that the physical movement in the urban environment is usually constrained by a street network, we propose a different method for the delimitation of CBD with network configurations. First, starting from the locations of central activities, a concentration index is presented to visualize the functional urban environment by means of a density surface, which is refined with network distances rather than Euclidean ones. Then considering the specialties of network distance computation problem, an efficient way supported by flow extension simulation is proposed. Taking Shenzhen and Guangzhou, two quite developed cities in China as two case studies, we demonstrate the easy implementation and practicability of our method in delineating CBD.     

基于IMM-EKF的高动态“北斗”导航信号频率估计算法

高动态环境下的“北斗”导航信号含有较大的多普勒频率及其变化率，传统锁相环（PLL）在跟踪时难以保证较高的跟踪精度。在分析高动态环境下“北斗”信号模型的基础上，提出了一种基于交互式多模型-扩展卡尔曼滤波（IMM-EKF）的自适应滤波算法，对载波相位及其高阶分量进行估计。IMM-EKF采用多个跟踪模型来解决滤波过程中单个模型不准确的问题，并结合改进的Sage-Husa自适应算法，在线估计和修正过程噪声及测量噪声的统计特性，增强了滤波的稳定性。仿真结果表明，IMM-EKF相比于PLL和EKF，估计精度更高，算法稳定性更强。     

Poverty analysis using small area estimation: an application to conservation agriculture in Uganda

This article demonstrates the utility of small area estimation of poverty (SAEp) methods for researchers wishing to conduct a detailed welfare analysis as part of a larger survey of a small geographic area. This study applies SAEp methods as part of an impact assessment of a conservation agriculture production system in Eastern Uganda. Using SAEp, we estimate Foster–Greer–Thorbecke rural poverty indices, estimate the effects of per‐acre farm profit increases to poor households on the indices, and compare the findings to estimates of net returns from a field‐level evaluation of conservation agriculture for maize farmers. Results suggest that increasing the farm profits of the bottom 30% of households by $1.60 per‐acre per‐season would reduce rural poverty incidence by 1 percentage point. Available data on the net returns to conservation agriculture indicate that even these modest increases are achievable for few adopting households.