Invoice currency choice,nonlinearities and exchange rate pass-through

ABSTRACT

Estimating time-varying thresholds as a proxy for exporter’s predicted exchange rates, this study proposes a new approach to analyse possible asymmetric behaviour of exchange rate pass-through (ERPT) or pricing-to-market (PTM) in Japanese exports between yen appreciation and depreciation periods. Constructing the industry-specific nominal effective exchange rate on a contract (invoice) currency basis, we perform the multivariate threshold near-vector autoregressive (near-MTVAR) estimation and reveal a strong tendency of symmetric ERPT in the short-run, between yen appreciation and depreciation periods. From the 2000s, however, Japanese machinery exporters increased the degree of PTM even in the long-run, while other industries raised the degree of long-run ERPT, reflecting the difference of product differentiation across industries. This evidence has significant implications for the recent unresponsiveness of the Japanese trade balance to the large depreciation of the yen.     

Time-varying risk of rare disasters,investment, and asset pricing

We extend an equilibrium business cycle/asset pricing model of production and capital accumulation by introducing a time-varying risk of rare disasters. It predicts that investment is much more volatile than output, which provides theoretical support for the empirical data. Furthermore, the model-generated stationary distribution of the investment-output ratio fits the data remarkably well. Both of them exhibit negative skewness, which means that there is a small probability that this ratio can be very low. Given the observations of the investment-output ratio, we obtain the values of the jump intensity implicit in the historical data and find those recession periods coincide with a rapid increase in the probability of a disaster. Finally, the model shows that the existence of adjustment costs generates a procyclical price of capital and contributes to resolving the equity premium puzzle.     

Risk dependence and cointegration between pharmaceutical stock markets: The case of China and the USA

This paper analyses risk-integration and the degree of dependence between the Values-at-Risk (VaRs) estimates for the two major pharmaceutical stock markets in the world: USA and China. To do this, we study the dependence and fractional cointegration properties among risks. Using daily returns for an eleven-year period, we estimated the VaRs obtained for pharmaceutical market portfolios in China (Shanghai) and the USA (NYSE) using the market model and considering both long and short trading positions. We conclude that the Shanghai pharmaceutical market is riskier than NYSE, although is predictable and losses in both markets exhibit tail dependence between VaR estimates. Particularly, there is lower tail VaR dependence for long position and upper tail dependence for short positions, both being small and fairly constant. On the other hand, we have not found fractional cointegration between risks, suggesting that China’s pharmaceutical sector is not integrated into the global pharmaceutical market.     

A Copula Nonlinear Granger Causality

We propose a new copula nonlinear Granger causality test that is more robust than the current available linear and nonlinear Granger causality tests when there exists an asymmetric and nonlinear directional dependence. To perform the statistical test of the copula nonlinear causality, the Gaussian Copula Marginal Regression (GCMR) model and copula directional dependence (Kim and Hwang, 2017) are employed in this paper. By using GCMR and two-sample permutation test with rank sum statistic for the copula nonlinear Granger causality, we can confirm that the result of the proposed copula nonlinear Granger causality test is a reliable test through the simulated data and real data both for small and large sample sizes.     

利用时变噪声贝叶斯卡尔曼滤波的室内移动目标定位

基于射频识别的指纹滤波定位技术是当前室内定位中常使用的技术之一。针对该技术存在的卡尔曼滤波算法不能准确适应环境噪声变化，致使定位精度不高的问题，提出了一种适应时变噪声的贝叶斯卡尔曼滤波算法。所提算法结合Sage-Husa滤波模型和贝叶斯模型，实现了过程和测量协方差矩阵的最优化，有效地降低了噪声，提高了指纹滤波定位的精度。实验结果表明，与变分贝叶斯卡尔曼滤波和Sage-Husa滤波相比，无障碍情况下，基于改进算法的定位精度提高了6%以上；有障碍干扰下，则提高了14.6%以上。     

The cross-country impact of ECB policies: Asymmetries in – Asymmetries out?

Since its inception, the adequacy of the Eurozone to be an Optimal Currency Area has been questioned, and, along with it, the homogeneous transmission and impact of the monetary impulses across the member countries. We provide a comprehensive assessment of the transmission mechanism’s functioning, its symmetry, impact on target variables, and evolution, addressing all the questions which have remained unanswered in the previous literature, while adding evidence on the impact of non-standard policy measures. We do so by adopting a Bayesian Time-Varying Parameters FAVAR model that fixes the flaws present in past research. The empirical analysis shows that the occurrence of the two crises significantly altered policy transmission, with both the interest rate and credit channel being consistently affected. It also shows that while they provided effective stimuli to the economies, the unconventional measures implemented were not able to fix those asymmetries. Policy-wise, our findings suggest that authorities must push towards consistent innovation on the fiscal side, while gaining more confidence with regards to the new monetary toolkit.     

The time-varying effect of monetary policy in China

A sound understanding of monetary transmission mechanism is valuable because it helps the central bank to determine the proper course of monetary policy to balance growth and inflation. As China’s domestic financial markets deepen and develop further towards a market-based system, the country’s monetary policy instrument and transmission should continue to improve for managing economic conditions. Using a short-term key interest rate as standard monetary policy tool and time-varying parameter techniques, this study empirically demonstrates that China’s monetary policy framework is in the midst of transitioning to a market-based approach.     

时变信道下ADF协作车载通信误码率分析

智能车载协作系统中车辆快速移动使得无线通信信道具有时变特性，为有效评估系统的误码性能，给出了符合车载时变信道的一阶自回归（AR1）模型，提出了一种基于AR1模型的自适应解码转发（ADF）协作误码率分析方法。该方法通过AR1模型的多普勒频偏相关系数来刻画时变信道特性,根据中继译码结果自适应选择是否协作转发，提升了智能交通系统的可靠性。此外，利用矩生成函数（MGF）推导出ADF协作下多进制正交幅度调制（M-QAM）信号误码率封闭表达式，并分析了车载移动速度和信道状态信息(CSI)估计精度对误码性能的影响。数值仿真结果表明，车载系统能通过增加CSI估计精度，有效地减少车载快速移动引起的误码平顶值。该方法相对于放大转发（AF）协作通信方式，平均误码性能提高约8.7 dB。     

基于相依违约的上市公司担保风险度量研究

本文运用基于相依违约的混合模型度量上市公司担保风险,并进行了实证研究。结果表明:此模型能很好的预测上市公司对外担保的违约概率,可对上市公司信用进行评级;在敏感性分析中,违约概率对波动率、无风险利率和相依结构比较敏感,这能为风险管理提供一定的参考。     

金融危机对证券市场波动溢出的影响研究

不同证券市场之间的波动存在时变、非对称、非线性相关的特性,尤其是在极端事件影响下,证券市场之间往往会表现出尾部相关的特性。以次贷危机为背景,利用时变Copula模型研究了证券市场间的波动溢出。结果发现无论是金融安全时期还是金融危机时期,均存在美国证券市场对中国证券市场的波动溢出,并且在金融危机期间这种波动溢出效应有增强的趋势。