The market price of risk of the variance term structure

In this paper I examine the market price of risk of the variance term structure. To this end, the S&P 500 option implied variance term structure is used as a proxy for aggregate variance risk. Principal component analysis shows that time variation in the variance term structure over the 1996–2012 period can be explained mainly by two factors which capture changes in the level and slope. The market price of risk of each factor is estimated in the cross-section of stock returns. The slope of the variance term structure is the most significant factor in the cross-section of stocks returns and carries a negative risk premium. The slope factor has also some predictive ability over long horizon equity returns.     

CLOSED FORM PRICING FORMULAS FOR DISCRETELY SAMPLED GENERALIZED VARIANCE SWAPS

Most of the existing pricing models of variance derivative products assume continuous sampling of the realized variance processes, though actual contractual specifications compute the realized variance based on sampling at discrete times. We present a general analytic approach for pricing discretely sampled generalized variance swaps under the stochastic volatility models with simultaneous jumps in the asset price and variance processes. The resulting pricing formula of the gamma swap is in closed form while those of the corridor variance swaps and conditional variance swaps take the form of one‐dimensional Fourier integrals. We also verify through analytic calculations the convergence of the asymptotic limit of the pricing formulas of the discretely sampled generalized variance swaps under vanishing sampling interval to the analytic pricing formulas of the continuously sampled counterparts. The proposed methodology can be applied to any affine model and other higher moments swaps as well. We examine the exposure to convexity (volatility of variance) and skew (correlation between the equity returns and variance process) of these discretely sampled generalized variance swaps. We explore the impact on the fair strike prices of these exotic variance swaps with respect to different sets of parameter values, like varying sampling frequencies, jump intensity, and width of the monitoring corridor.     

Assessing time-varying stock market integration in Economic and Monetary Union for normal and crisis periods

In this paper, we examine the stock market integration process amongst 17 Economic and Monetary Union (EMU) countries from January 2002 to June 2013 over a normal period as well as for the Global Financial Crisis (GFC) and Eurozone Debt Crisis (EDC) periods. We classify the economies in three groups (A, B and C) based on their GDP to examine whether the economic size influences financial integration. Seven indicators are used for the purpose, namely, beta convergence, sigma convergence, variance ratio, asymmetric DCC, dynamic cointegration, market synchronisation measure and common components approach. The results suggest that large-sized EMU economies (termed as Group A) exhibit strong stock market integration. Moderate integration is observed for middle-sized EMU economies with old membership (termed as Group B). Small-sized economies (termed as Group C) economies seemed to be least integrated within the EMU stock market system. The findings further suggest presence of contagion effects as one moves from normal to crisis periods, which are specifically stronger for more integrated economies of Group A. We recommend institutional, regulatory and other policy reforms for Group B and especially Group C to achieve higher level of integration.     

供给侧改革背景下我国城市建设用地最优规模分析

本文基于边际等值原理，综合城市经济效益、社会效益、环境效益以及建设效益，构建多因子城市建设用地最优规模决策模型，并结合我国城市建设用地供给实际进行分析。结果显示：2001～2015年，我国城市发展经历了从建设用地短缺到最优及至过剩的过程，对建设用地增长的强依赖逐渐转变为弱依赖。城市建设用地增长与边际建设效益弱相关；与边际经济效益之间呈库兹涅茨倒U型曲线关系；与边际社会效益呈正相关、边际环境效益呈负相关。目前我国城市建设用地已经达到最优规模，其中建设用地供给与经济发展失调，是导致净效益下降的主要动因。     

Efficient estimation of forecast uncertainty based on recent forecast errors

Multi-step-ahead forecasts of the forecast uncertainty of an individual forecaster are often based on the horizon-specific sample means of his recent squared forecast errors, where the number of past forecast errors available decreases one-to-one with the forecast horizon. In this paper, the efficiency gains from the joint estimation of forecast uncertainty for all horizons in such samples are investigated. If the forecast uncertainty is estimated by seemingly unrelated regressions, it turns out that the covariance matrix of the squared forecast errors does not have to be estimated, but simply needs to have a certain structure, which is a very useful property in small samples. Considering optimal and non-optimal forecasts, it is found that the efficiency gains can be substantial for longer horizons in small samples. The superior performance of the seemingly-unrelated-regressions approach is confirmed in several empirical applications.     

Forecast combinations under structural break uncertainty

This paper proposes two new weighting schemes that average forecasts based on different estimation windows in order to account for possible structural change. The first scheme weights the forecasts according to the values of reversed ordered CUSUM (ROC) test statistics, while the second weighting method simply assigns heavier weights to forecasts that use more recent information. Simulation results show that, when structural breaks are present, forecasts based on the first weighting scheme outperform those based on a procedure that simply uses ROC tests to choose and forecast from a single post-break estimation window. Combination forecasts based on our second weighting scheme outperform equally weighted combination forecasts. An empirical application based on a NAIRU Phillips curve model for the G7 countries illustrates these findings, and also shows that combination forecasts can outperform the random walk forecasting model.     

基于光能利用率模型的河南省冬小麦单产估算研究

[目的]快速、准确估算空间尺度上作物产量,对于评价农田生态系统对气候变化的响应、制定科学合理的粮食政策、对外粮食贸易等具有重要意义。河南省冬小麦产量占全国1/4,准确估算河南省冬小麦产量对维护国家粮食安全具有重要作用。单产估算作为农作物估产中的关键技术,也是作物估产的难点之一。[方法]文章首先利用VPM(Vegetation Photosynthesis Model)估算冬小麦NPP(Net Primary Product),结合收获指数、冬小麦收获部分的含水量、含碳量、NPP分配到地上或地下部分比例等一系列符合该研究区的经验指数,进行河南省冬小麦单产估算研究,并分析了引起模拟误差的原因。[结果]模拟单产较实测单产低估4.4%(实测单产为6 810kg/hm~2,模拟单产为6 519kg/hm~2),但两者之间存在显著相关关系,两者相关系数的平方R2=0.70(n=50,p0.01)。通过与MODIS-GPP产品获得的冬小麦单产数据比较,基于VPM模型的模拟结果优于MODIS-GPP产品。[结论]基于VPM可快速、准确估算河南省空间尺度冬小麦单产,该方法具有较好的适用性。     

Socio-economic and biophysical determinants of land degradation in Vietnam: An integrated causal analysis at the national level

Recognizing the socio-economic and biophysical causes of land degradation at the national level is important for cause-targeted strategies when designing policies for combating land degradation. This study aims to identify the biophysical and socio-economic factors that significantly affect land degradation across Vietnam and to interpret the causalities underlying the effects. The dependent variables considered in the study are spatial, the extent and intensity of degradation in three land-use zones (agriculture, forest and severely degraded abandonment). The hypothesized explanatory variables are common economic and demographic drivers and bio-physical factors such as soil, terrain constraints, and neighborhood land-use structures that are often neglected in many large-scale land degradation assessments. Instead of using a single inferential statistic technique, we used multi-linear regression and binary logistic regression in a complementary manner to increase the detectability and credibility of the degradation cause analyses. The results showed agricultural production growth had strong and consistent effects on land degradation extent and intensity. Population growth, especially in rural areas, had a strong effect on the extent of overall land degradation. The importance of a neighboring forest was revealed for its ability to reduce land degradation intensity in abandoned, unproductive lands. The concrete faceting of the causal analysis for each land-use zone as social–ecological stratum allowed us to combine the defined social–ecological contexts, contemporary theories, and hypotheses in the field to clarify the causal factors of a complex phenomenon like land degradation. The study demonstrates these contemporary inferential statistics can be complementarily used to sufficiently detect and understand land degradation causes at the national level. The results suggest implications for national land management policy: internalizing land degradation costs in the farming system evaluation for payment for ecosystem services policy, restricting forest conversion, and improving extension services and education in agrarian communities.     

四川省重点生态功能区有机农业生产效率研究——基于三阶段DEA模型的实证分析

[目的]有机农业为我国农业绿色发展起到良好的带动作用,但我国有机农业发展中,西部贫困地区较为滞后。针对西部贫困地区有机农业生产效率进行定量分析,揭示影响有机农业生产效率的主要因素,旨在提高该地区有机农业生产效率,丰富区域视角下的有机农业生产研究。[方法]采用三阶段DEA模型对2014年四川省重点生态功能区有机农业生产效率进行量化分析,识别环境变量对有机农业生产效率的影响。[结果]环境因素和随机误差对四川省重点生态功能区有机农业生产效率产生了显著的影响。环境因素中,农民人均纯收入的增加有利于促使农机、耕地等资源的有效利用,但同时也导致化肥投入冗余增加;城镇化率的提高会降低耕地、劳动力、化肥、农药等使用效率,从而对有机农业生产效率产生不利影响;财政支出占GDP比重的增加有利于提高农机使用效率,但同时也导致化肥和农药投入冗余增加;粮食播种面积占耕地面积比重对有机农业生产效率产生了不利影响。剔除环境因素和随机误差的影响后,四川省重点生态功能区各县平均技术效率和规模效率有所下降,纯技术效率则略有提升。基于纯技术效率和规模效率,四川省重点生态功能区各县有机农业生产效率可分为"双高型""高低型""低高型"和"双低型"等4种类型。[结论]三阶段DEA模型有效剔除了环境因素和随机误差对有机农业生产效率的影响,估计的效率值更真实地反映了四川省重点生态功能区有机农业生产效率水平。针对四川省重点生态功能区各县有机农业生产效率的不同特征,应结合地方实际,通过扩大要素投入规模或改进要素配置效率和使用效率,提高有机农业生产效率。