广义帕累托分布模型:风险管理的工具

在广义帕累托分布模型中,门限值过小,极限定理不成立,得到的估计是有偏的;反之,门限值过大,可以分析的数据减少,分析的偏差减少,但估计的方差增加.如何适当选取门限值是一个非常关键的问题.通过对尾部分布何时服从广义帕累托分布进行检验,可得到相应的门限值和VaR,这对金融资产管理具有重要意义.     

基于向量误差修正模型的城市产业集聚效应分析——以天津市为例

本文利用向量误差修正模型研究城市产业集聚效应。以天津市为例对城市产业集聚形成的增长极、竞争优势、空间聚集与扩散、要素转移和结构调整效应进行了实证检验,结果显示天津市产业集聚形成的五种效应存在且正在发挥作用,一定程度上提高了资源配置效率,扩大了规模经济的竞争优势,并加速了要素转移和结构调整步伐。     

要素价格扭曲、行业异质性与R&D投入关系研究——来自中国制造业28个细分行业的证据

从中观视角研究要素价格扭曲对制造业R&D投入的影响及行业差异,厘清行业异质性背景下要素价格扭曲影响R&D投入的理论机制,运用制造业细分行业数据,结合非线性面板门槛模型进行深入分析。结果显示：要素价格扭曲对制造业R&D资本投入和R&D人力投入均均具有显著抑制效应,行业异质性因素在二者间发挥重要调节作用。进一步,运用面板门槛模型对行业异质性因素的作用进行探究。结果表明,行业特征因素如市场竞争、资本密集度、企业平均规模以及对外开放度在不同门槛值区间,要素价格扭曲对R&D投入的影响效应均存在显著差异。研究结论为全面深化市场化改革、提高要素资源配置效率、促进制造业自主创新转型升级,进而推动创新型国家建设具有一定启示意义。     

基于随机森林算法的洪水灾害风险评估研究

根据流域灾害系统理论,在考虑致灾因子、孕灾环境和承灾体的基础上,选取9个风险评价指标,运用样本数据进行人工识别风险并得到训练样本,采用随机森林算法构建基于随机森林的洪水灾害风险评估模型。然后采用随机森林自评估工具,分析建立的洪水灾害风险评估模型的误差和指标,同时构建支持向量机模型作为对比方案,并采用五折交叉验证方法对基于随机森林算法的洪水灾害风险评估模型和支持向量机模型进行验证。最后以海河流域邱庄段为研究对象,分别运用基于随机森林算法的洪水灾害风险评估模型和基于支持向量机模型对相同的数据集进行评估和对比,结果显示,12 h内降雨总量、洪水持续时间和土壤含水量是引发洪水的主要因素,而基于随机森林算法的洪水灾害风险评估的训练精度及测试精度均高于支持向量机模型。     

商业银行利率风险计量模型及实证分析--非平移收益曲线条件下的随机免疫策略

传统的久期理论建立在收益曲线平移等严格假设条件上，因而其在实践中的有效性大大降低了。根据Markowitz（1959）等理论可推导出：资产价格的总风险包括收益的方差和全久期向量两部分；假若商业银行采取现金中性（cash neutrality）的资产交易策略，风险计量模型可转换为线性规划问题，从而可以构建基于利率风险最小化模型的随机免疫策略。也就是说，引入随机免疫的理念来替代经典的免疫理论，通过实证分析得出：无现金交易条件下的随机免疫策略能够降低利率风险。     

Threshold cointegration and nonlinear adjustment between goods and services inflation in the United States

In this paper, we model the long-run relationship between goods and services inflation for the United States over the period 1968:1–2003:3. Our empirical methodology makes use of recent developments on threshold cointegration that consider the possibility of a nonlinear relationship between the two inflation series. According to our results, the null hypothesis of linear cointegration would be rejected in favor of a two-regime threshold cointegration model. Consequently, we could expect a cointegrating relationship only when the divergence between services inflation and goods inflation is above the threshold point estimate.     

House Prices and Stock Prices: Different Roles in the US Monetary Transmission Mechanism*

We analyze the role of house prices and stock prices in the monetary‐policy transmission mechanism in the US, using a structural vector autoregressive model. If we allow the interest rate and asset prices to react simultaneously to news, we find different roles for house prices and stock prices in the monetary transmission mechanism. Following a contractionary monetary‐policy shock, stock prices fall immediately, while the response in house prices is more gradual. Regarding the systematic response in monetary policy, stock prices play a more important role than house prices. As a consequence, house prices contribute more than stock prices to fluctuations in gross domestic product and inflation.     

Common dynamics of nonenergy commodity prices and their relation to uncertainty

The purpose of this article is to improve the empirical evidence on commodity prices in various dimensions. First, we attempt to identify the extent of comovements in 44 monthly nonenergy commodity price series in order to ascertain whether the increase in comovement is a recent term phenomenon. Second, we attempt to determine the role of uncertainty in determining comovements among nonenergy prices in the short run. We diagnose the overall comovement using a dynamic factor model estimated by principal components. A factor-augmented vector autoregressive approach is used to assess the relationship of fundamentals, financial and uncertainty variables with the comovement in commodity prices. We find a greater synchronization among raw materials since December 2003. Since that date, uncertainty has played an important role in determining short-run fluctuations in nonenergy raw material prices.     

Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model

Accurate probabilistic forecasting of wind power output is critical to maximizing network integration of this clean energy source. There is a large literature on temporal modeling of wind power forecasting, but considerably less work combining spatial dependence into the forecasting framework. Through the careful consideration of the temporal modeling component, complemented by support vector regression of the temporal model residuals, this work demonstrates that a DVINE copula model most accurately represents the residual spatial dependence. Additionally, this work proposes a complete set of validation mechanisms for multi-h-step forecasts that, when considered together, comprehensively evaluate accuracy. The model and validation mechanisms are demonstrated in two case studies, totaling ten wind farms in the Texas electric grid. The proposed method outperforms baseline and competitive models, with an average Continuous Ranked Probability Score of less than 0.15 for individual farms, and an average Energy Score of less than 0.35 for multiple farms, over the 24-hour-ahead horizon. Results show the model’s ability to replicate the power output dynamics through calibrated and sharp predictive densities.     

Analyzing quantile spillover effects among international financial markets

This paper investigates the quantile-based spillover effects among 17 stock markets from January 1993 to January 2022, utilizing a quantile approach based on the variance decomposition of a quantile vector autoregression (QVAR) model. Compared with the traditional mean-based spillover measures, this new quantile approach allows for a nuanced investigation of spillovers at every quantile and capture spillovers under extreme events. The results show that: (1) the total spillover is high and exhibits strong time-varying characteristics, and the tail spillover is higher and more complex in scale and direction; (2) the spillover at each quantile level shows an upward trend, especially during the 2008 crisis and the COVID-19 epidemic; (3) developed countries (or regions) are the net exporters of stock market spillovers, while the developing countries are the net importers; and (4) the 17 stock markets constitute different local financial networks, which may be related to economic conditions and geographical location.