浅析比特币的发展

随着计算机网络的不断发展,比特币成为一种流行的虚拟货币。本文通过阐述比特币的概念与特征,分析比特币的货币职能和风险,以及各国对待比特币的态度,同时提出相应的政策建议。     

A new method to verify Bitcoin bubbles: Based on the production cost

As the first kind of digital cryptocurrency, the Bitcoin price cycle provides an opportunity to test bubble theory in the digital currency era. Based on the existing asset bubble theory, we verified the Bitcoin bubble based on the production cost with the application of VAR and LPPL models, and this method achieved good predictive power. The following conclusions are reached: (1) PECR is constructed to depict the deviation degree between the price and production cost, while BC is used to illustrate the bubble size in the price, and both are effective measures; (2) the number of unique addresses is a suitable measure of the use value of Bitcoin, and this result has passed the Granger causality test; (3) PECR and BC are verified via the LPPL model, and the next large bubble is expected in the second half of 2020. Considering that Bitcoin will see 'output halved' in May 2020, this prediction is a high-probability event.     

Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach

This paper proposes a new volatility-spillover-asymmetric conditional autoregressive range (VS-ACARR) approach that takes into account the intraday information, the volatility spillover from crude oil as well as the volatility asymmetry (leverage effect) to model/forecast Bitcoin volatility (price range). An empirical application to Bitcoin and crude oil (WTI) price ranges shows the existence of strong volatility spillover from crude oil to the Bitcoin market and a weak leverage effect in the Bitcoin market. The VS-ACARR model yields higher forecasting accuracy than the GARCH, CARR, and VS-CARR models regarding out-of-sample forecast performance, suggesting that accounting for the volatility spillover and asymmetry can significantly improve the forecasting accuracy of Bitcoin volatility. The superior forecast performance of the VS-ACARR model is robust to alternative out-of-sample forecast windows. Our findings highlight the importance of accommodating intraday information, spillover from crude oil, and volatility asymmetry in forecasting Bitcoin volatility.     

Investor sentiment and Bitcoin relationship: A quantile-based analysis

This paper applies a quantile-based analysis to investigate the causal relationships between Bitcoin and investor sentiment by considering the possible effects of the ongoing COVID-19 pandemic. Such an analysis allows investigating the predictive power of investor sentiment (Bitcoin) on Bitcoin (investor sentiment) at different levels of the distributions. Results emphasize that only Bitcoin returns/volatility have significant predictive power on the investor sentiment whether investors are fear or greed before and over the COVID-19 period. Moreover, the COVID-19 crisis has no effect on the causal relationship between the two variables. Further analysis shows an asymmetric causality observed only during the pandemic period. Furthermore, the quantile autoregressive regression model shows a significant positive relationship between investor sentiment and Bitcoin returns.     

美、欧央行监管比特币的做法及其对我国的启示

2015年12月5日,中国人民银行、银监会、证监会等五部委联合印发了《关于防范比特币风险的通知》,这对于保护社会公众的财产权益,保障人民币的法定货币地位,防范洗钱风险,维护金融稳定,具有重要的现实意义。美、欧央行对比特币的监管已先行一步,对我国具有一定的启示和借鉴。本文简要介绍了我国比特币交易情况及监管现状,归纳总结了美、欧央行监管比特币做法,提出我国应借鉴美、欧央行做法制定应急预案,防范比特币风险。     

Stock market reactions to upside and downside volatility of Bitcoin: A quantile analysis

We investigate how sensitive developed and emerging equity markets are to volatility dynamics of Bitcoin during tranquil, bear, and bull market regimes. Intraday price fluctuations of Bitcoin are represented by three measures of realized volatility, viz. total variance, upside semivariance, and downside semivariance. Our empirical analysis relies on a quantile regression framework, after orthogonalizing raw returns with respect to an array of relevant global factors and accounting for structural shifts in the series. The results suggest that developed-market returns are positively related to the realized variance proxy across various market conditions, while emerging-market returns are positively (negatively) correlated with realized variance during bear (normal and bull) market periods. The upside (downside) component of realized variance has a negative (positive) influence on returns of either market category, and the dependence structure is highly asymmetric across the return distribution. Additionally, we document that developed and emerging markets are more sensitive to downside volatility than to upside volatility when they enter tranquil or bull territory. Our results offer practical implications for policymakers and investors.     

Dynamic volatility modelling of Bitcoin using time-varying transition probability Markov-switching GARCH model

Bitcoin (BTC), as the dominant cryptocurrency, has attracted tremendous attention lately due to its excessive volatility. This paper proposes the time-varying transition probability Markov-switching GARCH (TV-MSGARCH) models incorporated with BTC daily trading volume and daily Google searches singly and jointly as exogenous variables to model the volatility dynamics of BTC return series. Extensive comparisons are carried out to evaluate the modelling performances of the proposed model with the benchmark models such as GARCH, GJRGARCH, threshold GARCH, constant transition probability MSGARCH and MSGJRGARCH. Results reveal that the TV-MSGARCH models with skewed and fat-tailed distribution predominate other models for the in-sample model fitting based on Akaike information criterion and other benchmark criteria. Furthermore, it is found that the TV-MSGARCH model with BTC daily trading volume and student-t error distribution offers the best out-of-sample forecast evaluated based on the mean square error loss function using Hansen’s model confidence set. Filardo’s weighted transition probabilities are also computed and the results show the existence of time-varying effect on transition probabilities. Lastly, different levels of long and short positions of value-at-risk and the expected shortfall forecasts based on MSGARCH, MSGJRGARCH and TV-MSGARCH models are also examined.     

Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants

In recent years, Bitcoin exchange rate prediction has attracted the interest of researchers and investors. Some studies have used traditional statistical and econometric methods to understand the economic and technology determinants of Bitcoin, few have considered the development of predictive models using these determinants. In this study, we developed a two-stage approach for exploring whether the information hidden in economic and technology determinants can accurately predict the Bitcoin exchange rate. In the first stage, two nonlinear feature selection methods comprising an artificial neural network and random forest are used to reduce the subset of potential predictors by measuring the importance of economic and technology factors. In the second stage, the potential predictors are integrated into long short-term memory (LSTM) to predict the Bitcoin exchange rate regardless of the previous exchange rate. Our results showed that by using the economic and technology determinants, LSTM could achieve better predictive performance than the autoregressive integrated moving average, support vector regression, adaptive network fuzzy inference system, and LSTM methods, which all use the previous exchange rate. Thus, information obtained from economic and technology determinants is more important for predicting the Bitcoin exchange rate than the previous exchange rate.     

A Statistical analysis of spatial point patterns A case study

In the project "statistical image analysis" of CWI we have studied some spatial point patterns that originated from biological observations. These observations were the positions of so called EGF-receptors on the surface of human carcinoma cells.
We propose a stochastic model for these point patterns. Since the EGF-receptors appear in clusters on the cell surface, we have opted for the Poisson-cluster-process as the model. We estimated the three parameters in this process by means of a method described by Diggle. We also did some work in assessing the statistical reliability of our estimates.     

A less sensitive linear detector for the change point based on kernel smoothing method

In this paper, a new detecting procedure for the change point in the mean is proposed based on a linear kernel smoother. It uses a discontinuous kernel function and has a relative high constant efficiency in an interval of shift value and thus is less sensitive than the simple CUSUM, EWMA, FMA and Shiryayev-Roberts procedures. It also generalizes the FMA procedure.     

基于时间点的股票期权会计确认探讨

股票期权作为一种长期激励机制已得到广泛认可,但其会计处理程序却相对落后,这为高级经理人提供了以权谋私的机会.确认是会计处理的第一步,它决定了会计事项的属性.本文对国内外股票期权确认理论加以对比,结合会计确认的基础理论,分析不同时间点股票期权持有人在企业中的地位及其付出劳动的性质,提出了基于时间点的股票期权会计确认分析方法.     

基于因子分析的北京城市功能空间布局研究

论文以北京市为例,采用因子分析法,对北京市内18区县反映城市发展状况指标进行定量综合评估,以明确北京市各区县城市发展的主导因素,为确定各区县发展优势及功能定位提供科学依据.并结合北京市现有城市功能空间分布特点,提出在城市服务核心区(主城区)和生态涵养区(山区)之间的城市功能拓展区内,建立朝阳-通州、丰台、石景山、海淀4个发展区.通过在各发展区内形成人流、物流循环,分担主城区人口、功能过于集中的压力.并通过功能区之间的产业连接,形成环状产业发展轴,构成北京国际化大都市发展骨架.     

美国城市增长管理的方法与启示

介绍美国主要的三类城市增长管理方法,即管理和控制城市增长区间和特点的方法;保护自然资源、环境质量和生态特征的方法;提供有效的公共基础设施的方法.分别对每一类方法进行了系统的介绍,进而指出对中国城市增长管理和土地利用管制的启示.     

一种基于TOPSIS的多属性决策方法研究

文章给出了一种基于TOPSIS的多属性决策方法,并通过实例检验了该方法的有效性。     

A simulation study of estimation methods in the analytic hierarchy process

This paper uses a simulation analysis to investigate the statistical accuracy and rank preservation capability of the AHP estimation methods. The methods under study consist of: the eigenvalue, mean transformation, row geometric mean, column geometric mean, harmonic mean and simple row average. The methods are compared under three distributions for error term—gamma, lognormal and uniform—and under two types of input matrices of various sizes.     

对我国房地产市场泡沫的判定及预控机制研究

我国房价这几年一路飙升,严重超出居民的收入增长速度.对于我国目前的房地产市场是否出现了泡沫问题,经济学界扣政府部门及房地产开发商展开了激烈地争论.本文主要根据房地产市场的相关指教对我国的楼市进行了实证分析,得出我国房地产市场的部分城市确实出现了泡沫.针对泡沫产生的根源,提出了当前如何使我国的楼市健康发展的相关建议.     

基于心理资本的员工主动离职倾向研究

心理资本指个体所拥有的积极心理资源,其构成部分包括自我效能感、希望、乐观和坚韧性.通过实证分析得出:心理资本与员工的主动离职呈负相关;性别在员工的主动离职倾向上的差异不显著;职位在员工的主动离职倾向上的差异显著.根据心理资本的投资性和收益特性特点,要减低员工的主动离职倾向,企业和个人都可以通过特定方式进行心理资本的投资与开发.     

英汉比较结构的一致性与差异性探讨——基于凸显理论

各种语言间的＂比较＂存在着认知的相似性和表现的差异性,比较范畴的研究对语言研究,尤其是语法研究和语言对比研究具有重要的意义。利用认知语法中的凸显理论对英汉比较句式的生成机制进行探讨,发现：英汉比较句式中的比较项都在生成过程中得到了凸显;英汉比较句式的语序即被比较项的位置存在着差异,这种差异主要缘于英汉比较句式在语义生成过程中凸显方式的区别;英汉在语言形式上的这种差异体现了英汉两个民族的不同认知模式。     

基于LiDAR技术的立交桥检测方法综述

文章针对立交桥这种结构复杂的城市交通重要组成部分,分析了利用LiDAR数据对立交桥进行三维提取的3种相关技术,综合分析了国内外的研究进展。