Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach

In order to challenge the existing literature that points to the detachment of Bitcoin from the global financial system, we use daily data from August 17, 2011–February 14, 2020 and apply a risk spillover approach based on expectiles. Results show reasonable evidence to imply the existence of downside risk spillover between Bitcoin and four assets (equities, bonds, currencies, and commodities), which seems to be time dependent. Our main findings have implications for participants in both the Bitcoin and traditional financial markets for the sake of asset allocation, and risk management. For policy makers, the findings suggest that Bitcoin should be monitored carefully for the sake of financial stability.     

Bitcoin price and its marginal cost of production: support for a fundamental value

This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.     

日本泡沫经济制度性原因分析及启示

20世纪90年代被称为日本经济“失去的10年”。日本经济一蹶不振,丧失了整整10年的发展。不少学者认为,90年代日本经济萧条意味着日本“又一次战败”即在经济战场上的“战败”。日本经济战败是败在了“制度”或“体制”方面,败给了成功地实行了“制度创新”或“体制创新”的美国。分析引发日本泡沫经济的制度性原因、探索日本经济走出衰退的途径,从中可寻找出对我国经济发展的有益启示。     

How do economic policy uncertainty and geopolitical risk drive Bitcoin volatility?

We investigate how economic policy uncertainty (EPU) and geopolitical risks (GPR) impact Bitcoin volatility with respect to factors related to type and nationality of uncertainty, investigated period, relationship horizon and extreme conditions. Applying ARDL model and quantile regression for monthly data from August 2010 to September 2021, we reveal that June 2014 corresponds to a key date that marks a reversal in the investigated relationship. Furthermore, we show that the relationship between uncertainty and bitcoin volatility changes according to different factors. US uncertainty has short run effects on Bitcoin volatility, while China’s uncertainty has rather long run effects. Moreover, Bitcoin volatility responds in the same manner to US EPU and GPR, while, it responds differently to China's EPU and GPR. In extreme quantiles, we find that Bitcoin hedges against US EPU and GPR. Further, Bitcoin hedges against either individual or joint effects of US uncertainty, but not both.     

中国房地产泡沫到底有多大

利用Ramsey模型，采取资本边际收益率法，实证测试了2000～2004年我国房地产的泡沫度，得出了期间我国房地产经历了从负泡沫、无泡沫到正泡沫演变的结论。     

房地产价格风险：国际教训、中国的现状及路径选择

房地产泡沫会增加金融风险,房价大幅缩水会导致银行破产,股市下跌。日本、美国、英国、泰国等都爆发过与房地产相关的银行危机。目前,中国房地产金融具有市场风险、财务风险、道德风险和信用风险等潜在风险。应利用税收手段限制投机性购房和短期炒作行为,鼓励中小户型住房的消费,加强城镇廉租住房制度建设,防范“假按揭”风险,完善惩戒机制,加强利率风险管理,完善住房置业担保制度,进一步研究制定房贷保险制度。     

房地产价格波动与投机行为--对中国14城市的实证研究

本文首先构建了适合中国的房地产投机理论模型,然后利用该模型对中国14个城市房地产价格波动与投机行为的关系进行实证研究.对14城市的时间序列数据研究结果表明各城市房地产投机水平都很高,个别城市更加突出.通过对14城市的截面数据分析,可以看出可支配收入对房地产价格没有显著影响,这些城市房地产价格的上升,主要是由投机来推动的,而且整体投机度非常高,说明房地产价格极大的偏离长期均衡值,市场出现了非理性繁荣,政府和产业部门应该采取适当的措施,积极引导消费者和投资者,为建立均衡的房地产市场提供良好的宏观和微观环境.     

房地产泡沫问题研究

自1998年开始，我国住房制度改革逐步深入，特别是随着福利化分房的结束，住房商品化的开始，房地产业进入了持续快速发展的新时期。山东省房地产产业发展迅速，成为影响GDP增长的重要因素。但与此同时，房地产价格也连续快速攀升。本论文以济南市为例，在收集整理大量有关房地产泡沫资料的基础上，得出了自己对于济南房地产市场的判断：总体发展健康，但同时需要进一步加强防范措施避免泡沫的形成。     

美国证券市场兴衰的非理性因素剖析

美国股市泡沫的膨胀和崩溃，无法完全用传统的金融理论和各种定价模型来说明，一些非理性因素对股市泡沫的形成和膨胀也有着重要的甚至决定的作用。从上世纪美国发生的四次股灾说明，非理性预期是股市泡沫形成的基础，其推动机制是庞氏骗局效应。此外，一些社会心理和行为，突发事件和随机因素，都对股市的兴衰起着推波助澜的作用。     

Modelling the dynamics of Bitcoin and Litecoin: GARCH versus stochastic volatility models

We examine and compare a large number of generalized autoregressive conditional heteroskedastic (GARCH) and stochastic volatility (SV) models using series of Bitcoin and Litecoin price returns to assess the model fit for dynamics of these cryptocurrency price returns series. The various models examined include the standard GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, t-distributed and moving average innovations. We report that the best model for Bitcoin is SV-t while it is GARCH-t for Litecoin. Overall, the t-class of models performs better than other classes for both cryptocurrencies. For Bitcoin, the SV models consistently outperform the GARCH models and the same holds true for Litecoin in most cases. Finally, the comparison of GARCH models with GARCH-GJR models reveals that the leverage effect is not significant for cryptocurrencies, suggesting that these do not behave like stock prices.