Objective methods for graphical structural learning

Graphical models are used for expressing conditional independence relationships among variables by the means of graphs, whose structure is typically unknown and must be inferred by the data at hand. We propose a theoretically sound Objective Bayes procedure for graphical model selection. Our method is based on the Expected-Posterior Prior and on the Power-Expected-Posterior Prior. We use as input of the proposed methodology a default improper prior and suggest computationally efficient approximations of Bayes factors and posterior odds. In a variety of simulated scenarios with varying number of nodes and sample sizes, we show that our method is highly competitive with, or better than, current benchmarks. We also discuss an application to protein-signaling data, which wieldy confirms existing results in the scientific literature.     

Price connectedness between green bond and financial markets

We study price connectedness between the green bond and financial markets using a structural vector autoregressive (VAR) model that captures direct and indirect transmission of financial shocks across markets. Using heteroskedasticity to identify the structural VAR model parameters, our empirical findings reveal that the green bond market is closely linked to the fixed-income and currency markets, receiving sizeable price spillovers from those markets and transmitting negligible reverse effects. We also show that, in contrast, the green bond market is weakly tied to the stock, energy and high-yield corporate bond markets. These findings have implications in terms of portfolio and risk management decisions for environmentally aware investors holding positions in green bonds.     

Risk,uncertainty, and leverage

Using mostly theoretical models and traditional risk/uncertainty measures (VIX index, panic, precaution, scary bad news, etc.), the current literature tries to clarify the risk/uncertainty-deleveraging pattern. The findings are not sufficient to explain the dynamic empirical relationship between modern risk/uncertainty indicators and leverage. We fill this gap in the literature by using US quarterly data, from 1985:1 to 2018:4, Granger causality tests, and a structural vector autoregression model. We find that commercial bank leverage rises when geopolitical risk and macroeconomic, policy, and equity uncertainty increase. Client-based business relationships of banks and high government borrowing from banks during crises periods are responsible for this relationship. We find that the leverage of broker-dealers and shadow banks declines when Chicago risk and macroeconomic, policy, financial, and equity uncertainty increase. We argue that the vulnerability of broker-dealers and shadow banks to the risk/uncertainty of the entire market system is responsible for this relationship.     

FX Intervention in the New Keynesian Model

We develop an open-economy New Keynesian Model with foreign exchange (FX) intervention in the presence of a financial accelerator and shocks to risk appetite in international capital markets. We obtain closed-form solutions for optimal monetary and FX intervention policies assuming the central bank cannot commit to future policies, and we compare the solution to that under policy commitment. We show how FX intervention can help reduce the volatility of the exchange rate, of inflation, and of the output gap, thus mitigating welfare losses associated with shocks in the international capital markets. We also show that, when the financial accelerator is strong, there is a risk of indeterminacy (self-fulfilling currency and inflation movements) although FX intervention can reduce this risk and thus reinforce the credibility of the inflation targeting regime. Model simulations match well the impact of a VIX shock obtained by local projections on a panel of inflation targeting emerging markets.     

Forecasting Foreign Exchange Markets Using Google Trends: Prediction Performance of Competing Models

Abstract

Foreign exchange markets affect a variety of humans and businesses worldwide and there is a wide array of literature aimed at providing more accurate forecasts of their movement. In an attempt to quantify human expectations, Google query search terms related to foreign exchange markets are used to help explain and predict foreign exchange rates between the United States’ dollar and ten other currencies during the time period of January 2004 and August 2018. We find evidence that, while Google Trends can be helpful in prediction, it is necessary to implement some sort of shrinkage or sparsity scheme on the coefficients.     

中国对虾出口贸易的影响因素及潜力分析——基于贸易引力模型

选取中国与主要对虾出口市场 2003-2017年的对虾贸易面板数据。运用引力模型对各影响因素进行回归分析。结果表明。出口市场的经济水平、中国的对虾产量以及 APEC成员对中国对虾的出口有着促进作用。中国与各出口市场的地理距离以及对虾疾病 EMS对对虾的出口有着较弱的阻碍作用。并根据引力模型测算了各出口市场的贸易潜力。其中美国等 12个国家和地区属于潜力再造型出口市场。加拿大属于潜力开拓型出口市场。日本、澳大利亚等15个国家和地区属于潜力巨大型出口市场。根据分析及测算结果分别从企业和政府的角度提出了促进对虾出口贸易发展的政策建议。     

城市轨道交通相邻多车站动态协同限流研究

为应对城市轨道交通车站高峰期客流压力,限流已经成为常态应对措施,而经常采用的限流策略大多针对单个车站,缺少对相邻多个车站的协同考虑。基于客流在车站间的传播效应,以乘客总延误时间最小为目标,以站台及列车能力为约束条件,构建城市轨道交通相邻多车站协同限流决策模型,同时为实现实时动态控制,引入滚动时域控制方法,优化多车站动态协同限流流程。以北京地铁8号线为例,经过相邻若干车站运营数据进行限流策略计算。结果表明,该模型限流可以在不造成车站服务人数损失的同时,将乘客总延误时间降低21%。     

考虑换乘费用的城市公共交通票价优化

换乘所产生的附加费用,如时间和票价等,导致配流影响因素产生变化,如果沿用传统方式将降低预测精度和可靠性。通过引入换乘次数和方式等因子计算出行等待、乘车、换乘及风险评估预留时间等,定义广义出行费用与计算方法;建立双层规划模型求解最优票价,最后通过算例分析弹性出行需求、换乘费用、票价之间的关系。计算结果表明,换乘费用对出行需求的影响小于票价优化对出行需求的影响,优化票价随换乘费用增加而加速降低,为公共交通票价优化提供研究依据。     

科学型企业商业模式:中国情境的复合案例研究

以中国企业为研究对象，运用复合案例研究方法分析与构建了科学型企业商业模式概念模型，分离出科学平台和价值平台两个独特要素，进而构建以科学平台-价值平台互动为核心的P2P商业模式运作模型，并根据不同类型科学型企业，分析模型的有效性、适用性以及模型在不同类型科学型企业中的共性与差异，最后总结出科学型企业商业模式特点及企业管理对策。     

锚企业与科技新城共生模式研究:以阿里巴巴和杭州未来科技城为例

以阿里巴巴和杭州未来科技城为例，采用Logistic模型探讨锚企业与科技新城共生模式，结果表明：总体而言，阿里巴巴和未来科技城属于互利共生模式，阿里巴巴成长会促进未来科技城发展，而未来科技城发展也会反哺阿里巴巴的成长。2013-2016年两者共生系数之间的差值不断缩小，表明阿里巴巴和杭州未来科技城的共生模式从偏利共生转向互利共生。