多重信息维度下金融市场风险的高频计量——基于超高频数据ACD模型和UHF-GARCH模型、

超高频数据是交易的实时记录,是所有信息在股市上的最精确的表现。考虑使用高频数据来测度金融风险无疑能够提高风险测度的准确性。本文在现有研究成果的基础上,将交易者行为特征、交易量、买卖价差、交易速度等信息维度纳入金融高频风险的计量中,并与没有考虑这些信息维度时测度的准确性进行对比,结果表明,考虑多重信息维度的模型能够更准确地测度金融市场风险。     

地区创业氛围及其作用机理——一个族群水平的创业分析架构

创业氛围是一个地区所有族群成员关于创业的心智能力、价值观念和习俗惯例的集体和外在的表现。创业氛围是内生的,它是由创业活动衍生的创业学习、心理体验和社会互动的结果。创业氛围通过提升创业平均收益率和改变族群成员创业风险偏好,来提高创业惯例复制频率,进而提高地区创业活动水平。创业氛围与创业活动之间存在一个以知识累积和报酬激励为动力的正反馈演化路径。中西部地区促进创业活动的关键是,着力培育创业氛围并实现其临界突破。     

Measuring the effectiveness of US monetary policy during the COVID-19 recession

The COVID-19 recession that started in March 2020 led to an unprecedented decline in economic activity across the globe. To fight this recession, policy makers in central banks engaged in expansionary monetary policy. This paper asks whether the measures adopted by the US Federal Reserve (Fed) have been effective in boosting real activity and calming financial markets. To measure these effects at high frequencies, we propose a novel mixed frequency vector autoregressive (MF-VAR) model. This model allows us to combine weekly and monthly information within a unified framework. Our model combines a set of macroeconomic aggregates such as industrial production, unemployment rates, and inflation with high-frequency information from financial markets such as stock prices, interest rate spreads, and weekly information on the Fed's balance sheet size. The latter set of high-frequency time series is used to dynamically interpolate the monthly time series to obtain weekly macroeconomic measures. We use this setup to simulate counterfactuals in absence of monetary stimulus. The results show that the monetary expansion caused higher output growth and stock market returns, more favorable long-term financing conditions and a depreciation of the US dollar compared with a no-policy benchmark scenario.     

基于交易成本的消费者网络购物意愿的实证研究

首先对网络购物中的交易成本相关研究进行文献综述,在此基础上,建立网络购物中消费者交易成本影响因素的相关研究假设,运用PLS—SEM模型的研究方法,采用问卷调研获取的数据,对消费者网络购物意愿进行了实证分析。研究表明,购买频率和不确定性显著地影响交易成本,不确定的影响作用较大,并最终对购买意愿产生影响。     

试论唐诗宋词中的高频字

一般论者常认为宋词中"风、花、春、月"等高频字的出现,体现了宋词用字和意象阴柔化、女性化的倾向.但是当我们把这些字放在整个古汉语高频字背景之下,对比唐诗和宋词高频字的异同时,却发现它们都重点关注人与自然,对自然物象类高频字的选择倾向是基本相同的,且表示判断、否定类高频字的大量出现,也只说明古汉语语法上的共性.唐诗宋词在文体本性是相似的,只有在对比诗词文本高频字的演变时,才体现出宋词相较唐诗独具的个性和倾向.     

基于物联网的快捷超市购物系统设计

现今的网络购物依然无法取代人们日常的超市购物活动。因此,通过在现有的电子商务购物平台基础上,结合二维码、射频识别等物联网技术,设计一套应用于传统生鲜超市的电子购物系统,可以帮助人们快速、便捷地完成传统超市购物的过程。该系统利用二维码存储商品订单信息,方便快捷,便于传递;利用射频标签标识用户商品,自动识别,便于查找。     

Why you should use high frequency data to test the impact of exchange rate on trade

This study suggests that testing the impact of exchange rate on trade should be done using high-frequency data. Using different data frequencies for identical periods and specifications between the US and Canada, we show that low-frequency data might suppress and distort the evidence of the impact of exchange rate on trade in the short run and the long run.     

Are high–frequency traders informed?

Are high–frequency traders (HFTs) informed? To address this question, we examine HFTs' activity in the call auction environment, where speed-related trading is limited and signal processing capacity becomes more relevant. To model the call market, we consider the Kyle (1989) rational expectations framework for strategic trading. The test we propose for detecting informed HFTs in this market assesses potential deviations of the informativeness of HFTs' aggregate (net) demand, from the informativeness of the aggregate demand submitted by the rest of the traders. Data from the Euronext Paris preopening phase indicate that informed HFTs are present in the market just before the opening. Our results provide useful guidance for the assessment of the influence of HFTs’ quotes on price quality, an important issue for market regulators and policy makers.     

Empirical Evidence of the Leverage Effect in a Stochastic Volatility Model: A Realized Volatility Approach

Increasing attention has been focused on the analysis of the realized volatility, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data (from 1-min to 60- min) based on the trust region method. Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration. Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency and the volatility persistence becomes weaker at the lower sampling frequency.     

基于SC-FDMA的认知无线电网络的研究

物理层数据传输方案的选择是认知无线电（CR）技术能在实际中广泛应用的重要基础。给出了将SC—FDMA（单载波频分多址）技术应用于CR网络中的传输方案,并与传统的NC—OFDM（非连续的OFDM）技术进行分析比较。仿真结果和相应分析表明：在CR网络中,总体而言SC—FDMA的性能比NC—OFDM更为优良,特别是在严重的频率选择性衰落环境中,或者是当认知用户的可用频谱资源（子载波）比较稀少时,SC—FDMA的性能优势更加明显。因此SC—FDMA是一种很有前景的技术,在不久的将来有希望取代NC—OFDM,成为CR网络的物理层传输方案。