融资流动性对商业银行资产配置行为的动态影响

使用14家上市银行2007~2013年季度数据建立面板向量自回归(PVAR)模型,运用脉冲响应函数分析融资流动性对银行资产配置行为的动态影响。研究结果表明：存款成本和银行间融资成本的上升,会激励银行增加风险资产,减少流动性储备,不利于银行防范结构性流动性风险;存款流失会削弱银行贷款扩张的动力,提升流动性偏好;同业融资依赖性的上升短期内可以改善银行资产的流动性,长期则会加大资金借短贷长的问题,为流动性危机埋下隐患。     

On the drivers of technical analysis profits in cryptocurrency markets: A Distributed Lag approach

The cryptocurrency literature on technical analysis has largely ignored drivers of technical analysis return adjusted by transaction costs (i.e., adjusted returns). To that end, we propose a Heterogeneous Autoregressive Distributed Lag Model of Returns (HARDL-R) to examine the impact from EPU, VIX, and SP500 returns to adjusted returns. We provide evidence that these three drivers matter during bubble periods compared to non-bubble periods. When not differentiating bubble periods, we find that VIX is the only driver influencing the dynamics of adjusted returns from 2016 to 2021. These findings remain relatively stable after controlling for the volume of transactions.     

The entry and exit dynamics of the cryptocurrency market

This paper presents an analysis of the entry and exit dynamics of the cryptocurrency market that focuses on the growth of initial coin offerings during 2015–2020. We used two different datasets: one includes long-lived cryptocurrencies, while the other includes the whole cryptocurrency system at our disposal–that is, it considers the entering and exiting cryptocurrencies. Comparing the dynamics between both datasets with the index cohesive force approach, we assessed how the growth of the initial coin offerings and the exiting cryptocurrencies affected the connectedness of the market. Our results show that the expansion of the cryptocurrency system gave rise to a strong collective movement during 2018–2019. Afterwards, the group pressure, due to the bubble of the initial coin offerings, decreased in favour of the largest cryptocurrencies. Lastly, we observed changes in the hierarchical order of the most influential cryptocurrencies. In particular, Ethereum became the most influential cryptocurrency, at the detriment of Bitcoin.     

Return spillover analysis across central bank digital currency attention and cryptocurrency markets

Many central banks have now developed their digital currencies in response to the challenges posed by the proliferation of decentralised digital cryptocurrencies. However, little is known about the effects of the introduction of central bank digital currencies (CBDCs) on extant digital cryptocurrencies. This paper, therefore, aims to identify both the time- and frequency-domain spillover effects among cryptocurrency markets and a newly developed central bank digital currencies attention index (CBDCAI) by using two TVP-VAR-based spillover models. Our results demonstrate that CBDC attention significantly impacts cryptocurrency markets. Also, most investors in cryptocurrency markets are more likely to trade in the short term. The results of this study contribute to helping investors and investment institutions effectively avoid investment risks, reduce losses, and predict the return of some cryptocurrencies. Also help policymakers better understand the impact of markets and policies, and provide a reference for them to formulate policies.     

Conditional tail-risk in cryptocurrency markets

In this paper we use $CoVaR$ to estimate the conditional tail-risk in the markets for bitcoin, ether, ripple and litecoin and find that these cryptocurrencies are highly exposed to tail-risk within cryptomarkets, while they are not exposed to tail-risk with respect to other global assets, like the U.S. equity market or gold. Although cryptocurrency returns are highly correlated one with the other, we find that idiosyncratic risk can be significantly reduced and that portfolios of cryptocurrencies offer better risk-adjusted and conditional returns than individual cryptocurrencies. These results indicate that portfolios of cryptocurrencies could offer attractive returns and hedging properties when included in investors’ portfolios. However, when we account for liquidity, the share of crypto assets in investors’ optimal portfolio is small.     

Taming the blockchain beast? Regulatory implications for the cryptocurrency Market

This paper uses a unique dataset of 120 regulatory events from five classes to test the relevance of the regulatory framework for cryptocurrency value. Time-series market-wide estimates and panel estimates for 300 individual coins and tokens show statistically and economically significant impact of anti-money laundering and issuance regulation. Tighter regulation and more active role of government decrease cryptocurrency prices, evidencing that potentially lower risks and wider adoption commonly attributed to the establishment of the regulatory framework do not compensate for respective efficiency and consumer utility losses. The market is generally efficient in reflecting regulatory information in cryptocurrency prices.     

On the topology of cryptocurrency markets

Cryptocurrency markets are characterised by high volatility, high returns and comparative immaturity relative to equity and commodity markets. Topological Data Analysis (TDA) persistence norms are effective tools for the analysis of noisy dynamical systems like the cryptocurrency markets. We show how information from the shape of daily return data adds additional inference on activity within the cryptocurrency markets. TDA persistence norms embed volatility and connectedness between coins as well as incorporating information from uncertainty indexes, financial market performance and commodity returns. Our TDA measures are robust to noise and are consistent across a raft of alternative coin selections. Further, we exposit how persistence norms peak to forewarn of crashes and stay low as markets face exogenous shocks. We demonstrate the clear advantages of TDA for the study of cryptocurrency markets and develop the next steps for exploiting the potential of TDA for application to cryptocurrency markets.     

Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach

We construct the complete network of tail risk spillovers among major cryptocurrencies using the Least Absolute Shrinkage and Selection Operator (LASSO) quantile regression. We capture important features of the network, including major risk-driving and major risk-receiving currencies, and the evolution of the tail dependence among the currencies over time. Importantly, we reveal a striking finding that the right tail dependence among the cryptocurrencies is significantly stronger than the left tail counterpart. This unique characteristic may have contributed to the rise in popularity of cryptocurrencies over the last few years. Our portfolio analysis reveals that diversification in cryptocurrency investment can be accomplished simply by employing the naïve equal-weighted scheme even when transaction costs are taken into account.     

流动性的度量及其与资产价格的关系

本文将流动性划分为货币流动性、银行系统流动性和市场流动性三个层次,总结了相应的可操作的度量方法,并通过中国数据进行了度量,从一个侧面论证了货币流动性是市场流动性的基础,以及市场流动性高时资产价格一般也较高的观点。基于货币流动性的基础性地位,本文进一步考察了货币流动性与资产价格的关系,发现超额货币流动性不仅影响股票的名义回报,还影响股票的真实回报;货币流动性在长期内受到股票真实回报的反作用,但这种作用可能是相对微小的。     

Asset Liquidity and Stock Liquidity: International Evidence

This study examines the relationship between asset liquidity and stock liquidity across 47 countries. In support of the valuation uncertainty hypothesis, we find that firms with greater asset liquidity on average have higher stock liquidity. More importantly, our study shows that asset liquidity plays a more significant role in resolving valuation uncertainty in countries with poor information environment. For example, we find that the asset–stock liquidity relationship is stronger in countries with poor accounting standards. We further find evidence that after the adoption of IFRS, the improved accounting information environment results in a weaker asset–stock liquidity relation, but only in countries with a strong legal regime. Finally, our study shows that the positive asset–stock liquidity relationship may be attributed to transparency and/or liquidity reasons.     

Good volatility,bad volatility,and the cross section of cryptocurrency returns

This paper examines the predictability of realized volatility measures (RVM), especially the realized signed jumps (RSJ), on future volatility and returns. We confirm the existence of volatility persistence and future volatility is more strongly related to the volatility of past positive returns than to that of negative returns in the cryptocurrency market. RSJ-sorted cryptocurrency portfolios yield statistically and economically significant differences in the subsequent portfolio returns. After controlling for cryptocurrency market characteristics and existing risk factors, the differences remain significant. The investor attention explains the predictability of realized jump risk in future cryptocurrency returns.     

交易所大宗交易流动性折价研究——来自沪深交易所的经验证据

随着证券市场规模的扩大及机构投资者规模的壮大，机构投资者对市场流动性的需求日益剧增，大宗交易制度是满足投资者流动性需求的制度性创新。由于大宗交易的数量较大，其交易价格有别于正常交易规模的价格。本文利用沪深交易所的大宗交易数据实证探讨大宗交易价格及其影响因素。研究结果表明，大部分大宗交易价格低于当日收盘价格，呈现流动性折价现象，折价率达到1．27％：研究还发现，折价水平还受交易数量、正常交易时间段股票流动性水平、市场流动性水平及股价波幅等因素影响。     

Liquidity and market makers: a pseudo-experimental analysis with ultrahigh frequency data

An analysis is given of the effect of market makers on liquidity using a transaction-level database. For this purpose, the focus is on a financial market where a change in regulations created explicitly the category of market maker in 1997 and that date is used to construct a pseudo-experiment. In contrast with other studies that use ultrahigh frequency data, the days to be analysed are selected using a statistical procedure to match observations before and after the change in regulation. The propensity score is used to perform the matching. After choosing the days, an estimate of an ordered probit model is made to explain the intraday behaviour of price changes. The coefficient estimates from the ordered probit model are used to calculate a measure of liquidity based on the steepness of the response function of price changes to volume. The results show that liquidity, measured in this way, has not been affected by the introduction of the market makers.     

Liquidity Commonality in the London Stock Exchange

Abstract:  A number of events such as the international market crash of October 1987 and the 1997 East Asian crisis show that individual firm liquidity is affected by market-wide factors. However, research in systematic liquidity is still at an embryonic stage and given the gap in the literature, the paper offers first time evidence (to the best of our knowledge) on the presence of systematic liquidity in the UK using FTSE100 and FTSE250 stocks. The unique setting of the London Stock Exchange as regards changes in trading regimes, allows an original answer as to whether changes in the nature of market making from obligatory to non-obligatory, affect commonality in liquidity. Results indicate that commonality is quite strong for FTSE100 stocks at individual and portfolio level, while for the FTSE250 it is strong only at portfolio level. Overall commonality is on average similar across trading regimes, irrespective of the nature of the provision of liquidity.     

Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis

This paper investigates the relationship between investor attention and the major cryptocurrency markets by wavelet-based quantile Granger causality. The wavelet analysis illustrates the interdependence between investor attention and the cryptocurrency returns. Multi-scale quantile Granger causality based on wavelet decomposition further demonstrates bidirectional Granger causality between investor attention and the returns of Bitcoin, Ethereum, Ripple and Litecoin for all quantiles, except for the medium. Among them, the Granger causality from investor attention to the returns is relatively very weak for Ethereum. In the short term, the Granger causality from these cryptocurrency returns to investor attention seems symmetric, but in the medium- and long- term, the causality shows some asymmetry. The Granger causality from investor attention to these cryptocurrency returns is asymmetric and varies across cryptocurrencies and time scales. Specifically, investor attention has a relatively stronger impact on the cryptocurrency returns in bearish markets than that in bullish markets in the short term.     

A comparative analysis of cryptocurrency returns and economic policy uncertainty pre- and post-Covid-19

This study analyzes the impact of economic policy uncertainty (EPU) on cryptocurrency returns for a sample of 100 highly capitalized cryptocurrencies from January 2016 to May 2021. The results of the panel data analysis and quantile regression show that increases in global EPU have a positive impact on cryptocurrency returns for lower cryptocurrency returns quantiles and an adverse impact for upper quantiles. In line with the existing literature, the Covid-19 pandemic resulted in higher returns for cryptocurrencies. Inclusion of a Covid-19 dummy in the models strengthened the impact of EPU on cryptocurrency returns. Furthermore, the relationship between the change in EPU and cryptocurrency returns was direct in the pre-Covid-19 period but inverse in the post-Covid-19 period. These results imply that cryptocurrencies act more like traditional financial assets in the post-Covid-19 era.     

Cryptocurrency,confirmatory bias and news readability – evidence from the largest Chinese cryptocurrency exchange

We investigate whether Chinese cryptocurrency investors show confirmatory bias when processing authority‐related news. Authority‐related news is defined as news that is related to government authority (including central bank) policies or talk. By using data from the largest cryptocurrency exchange in China, we find that investors’ response to authority‐related news is negative and significant in general. Moreover, we find that the abnormal trading volume and standard deviation of abnormal trading volume are significantly higher for authority‐related news with higher readability, suggesting investors respond to the more readable authority‐related news with more trading behaviour.     

Liquidity Trading in Market Microstructure Theory

Liquidity trading is an important component of market microstructure models. In most cases, its role is primarily to ensure existence of equilibrium and therefore that trading occurs among asymmetrically informed agents. While most market microstructure models allege that agents trade based upon rational expectations, the rationality of the type of liquidity trading assumed in these models remains to be verified. Specifically, liquidity traders are often assumed to submit price-inelastic orders for reasons exogenous to the model at hand. But whether price-inelastic trading is consistent with rational utility maximizing behavior remains to be shown.     

流动性过剩、主要表现和宏观调控

在对流动性和流动性过剩的内涵进行分析的基础上,文章从货币超额供给、外汇占款持续快速增加、银行业金融机构货币沉淀快速扩大、价格泡沫日益显现和各因素的综合表现等五个方面分析了流动性过剩的主要表现。提出了流动性过剩宏观调控的对策建议：明确流动性过剩宏观调控应关注的方向、充分发挥市场在资源配置中的决定性作用、完善中央银行的宏观货币调控体系和进一步推进人民币国际化。