The fair value of a token: How do markets price cryptocurrencies?

With the rise of cryptocurrency tokens as a new asset class, the question of the fair evaluation of a cryptocurrency token has become a question of increasing importance. We estimate the pricing kernel with which users price factors affecting their token holdings. We investigate how traditional risk factors such as market risk are evaluated, as well as how blockchain specific risk factors are priced in. In order to do so, we introduce an asset pricing model and modify its properties to make it applicable to cryptocurrency markets. We group the risk factors into market related and Bitcoin- and Ethereum blockchain specific risk factors. We find that blockchain specific risk factors are priced in. There is evidence that risk factors have moved from Bitcoin to Ethereum specific risk factors with an increasing importance of market factors, providing evidence for a decoupling of on-chain and off-chain trading activity.     

区块链在场外衍生品市场组织中的应用研究

因其非标准化合约和非集中交易特征,场外衍生品市场存在交易效率低下,透明度低,交易对手方信用风险管理与监管困难等诸多难题。解决这些问题的传统思路是标准化、中心化,但这又牺牲了场外衍生品个性化、定制化特征。区块链技术有助于在缺乏中心化可信权威的条件下建立信任机制并实现交易,可有效解决制约场外衍生品市场发展的核心问题。基于区块链的场外衍生品交易基础设施的关键技术包括:权威认证共识机制、智能衍生品合约、统一信用评价体系、系统风险监测模型等。经论证,应用区块链技术可有效改善场外衍生品市场的运行组织,提高交易效率和透明度,改善交易对手信用风险管理,加强监管穿透和防范系统性风险。     

The effects of randomizing the opening time on the performance of a stock market under stress

To tackle challenges from derivatives trading and illiquidity, reduce manipulation and improve price discovery, many exchanges have started opening at random times. We investigate how randomization has affected the performance of the Tel Aviv Stock Exchange at trade opening and at the expiration of stock-index derivatives. Randomization has improved price discovery and reduced excess volatility and price distortion, especially on expiration dates. Although preopening prices do not converge to full information values, post-randomization, opening prices on expiration days are at least as accurate as on other days. Spot market trading systems significantly impact the effects of derivatives on spot prices.     

Price discovery and trade fragmentation in a multi-market environment: Evidence from the MTS system

This paper proposes new metrics for the process of price discovery on the main electronic trading platform for euro-denominated government securities. Analysing price data on daily transactions for 107 bonds over a period of 27 months, we find a greater degree of price leadership of the dominant market when our measures (as opposed to the traditional price discovery metrics) are used. We also present unambiguous evidence that a market’s contribution to price discovery is crucially affected by the level of trading activity. The implications of these empirical findings are discussed in the light of the debate about the possible restructuring of the regulatory framework for the Treasury bond market in Europe.     

On spillover effects between cryptocurrency-linked stocks and the cryptocurrency market: Evidence from Australia

This paper categorizes Australian listed cryptocurrency-linked stocks (CLS) by their involvement as a user, developer and diffuser, and investor of blockchain technology and cryptocurrencies based on company announcements and published information on the company websites. By distinguishing CLS engagement with blockchain technology, we examine their returns and volatility spillover with the cryptocurrency market over the period 1 September 2017 to 7 June 2018, spanning important episodes and dynamics in the cryptocurrency market in 2017-2018, and the emergence of Australian CLS. Utilizing the Diebold and Yilmaz (2012) spillover methodology, we find significant unidirectional return spillover and weak volatility spillover from the cryptocurrency market to CLS, after controlling return dynamics of the Australian dollar, Gold and commodity. However, CLS with high involvement in blockchain technology displays stronger connectedness to the cryptocurrency market through return spillover relative to low involvement CLS. Our findings indicate that investors incorporate the price dynamics of cryptocurrencies into their trading decisions for CLS.     

The rise of decentralized cryptocurrency exchanges: Evaluating the role of airdrops and governance tokens

While most cryptocurrency financial activity is conducted on centralized exchanges, decentralized finance (DeFi) has experienced a particular surge with roughly 90,000 users at the start of 2020 to 4.28 million by the end of 2021. Based on data collected from a popular crypto-asset data aggregation service and manually collected data, we document the rapid growth in decentralized exchanges and their differences in volume and price dynamics from centralized exchanges. Next, we investigate the role of airdrops and governance tokens as mechanisms for expanding the base of users and driving up the value of an exchange. While our results do not have a causal interpretation, they provide preliminary evidence that both mechanisms are effective for expanding and strengthening networks, particularly for decentralized exchanges. We also exploit two event studies that suggest the growth in decentralized exchanges is not driven by speculation, but at least partially by value-creating cybersecurity benefits.     

The Effect of Tick Size on Price Clustering and Trading Volume

Proposals have been made for some stock exchanges to reduce the size of their trading tick in order to lower transactions costs and, as a result, attract more trading volume and firm listings. We investigate the impact of tick size on price clustering and trading volume when the minimum price change varies with price level. Controlling the firm specific variables, we find that a smaller trading tick tends to exacerbate price clustering. Furthermore, a reduction in tick size is more likely to increase trading volume if the shares are heavily traded. These results suggest that previous studies on other stock markets may have overstated the benefits of a smaller trading tick to traders.     

Will Blockchain Be a Big Deal? Reasons for Caution

The initial enthusiasm for implementing blockchain in financial markets has been dampened considerably by its collision with economic realities. Though the author warns against avoiding the Panglossian trap of viewing ours as the best of all possible worlds, he reminds us that trusted institutions have evolved and emerged in a competitive environment as a means of economizing on transaction costs. Such costs arise from the nature of transactions, including crucially the information environment in which they take place. Though new technologies such as blockchain have the potential to reduce some of these costs, they often do so without fundamentally changing the underlying economic conditions that give rise to them. And as a result, institutions such as banks and exchanges that technologists scoff at may well prove surprisingly competitive and durable in the face of technological challengers. The most successful implementation of blockchain—Bitcoin—solves a very basic transactional challenge peculiar to cryptocurrency: the double spend problem. But it does so in a very expensive way, and many other transactions pose far more complex challenges. The three cautionary tales provided by the author—the first involving securities and derivatives trading and clearing, the second commodity trading, and the third proposals to “equitize” assets—all demonstrate the need to confront “Chesterton's Fence” when evaluating the potential of blockchain in any particular application. In other words, to understand the value of a new technology for a given set of functions, one must understand the economic forces that have shaped the processes and institutions that currently perform those functions. When such forces are considered, it often becomes apparent that new technologies like blockchain will not prove superior to existing practices—and may even create adverse unintended consequences that offset and perhaps even eliminate its beneficial effects.     

Trading costs and price discovery

The price discovery roles of a set of related markets or securities have been investigated in many different settings where trading costs effect is often commingled with other trading arrangement factors. In Hong Kong, regular futures and mini futures contracts as well as their underlying spot asset are all traded on a same electronic trading platform. The trading arrangements thus provide us with a unique setting where we can isolate the impacts of transaction costs on price discovery. Using Hasbrouck’s (J Finance 50:1175–1199, 1995) information share approach, it is found that in Hong Kong, the regular futures contracts market plays a dominant role in price discovery while the mini futures and cash index markets play minor roles. The results in this paper provide an unequivocal support to the trading costs hypothesis.     

Price discovery on the S&P 500 index markets: An analysis of spot index,index futures,and SPDRs

This paper investigates the price discovery function in three S&P 500 index markets: the spot index, index futures, and S&P Depositary Receipts markets. Four hypotheses regarding market structure and security design are proposed to differentiate the price discovery function performed by the three index instruments. Using matched synchronous intraday trading data, Johansen's maximum likelihood estimator is employed to disclose the cointegration relationships among the three markets. Results indicate that the three price series are a cointegrated system with one long-run stochastic trend. Estimated coefficients of the vector error correction model suggest that price adjustment takes place in the spot index market and for SPDRs, but not in the futures market. When the common stochastic trend is decomposed, it is found that the futures market serves the dominant price discovery function. The leverage hypothesis and the uptick rule hypothesis explain its superior price discovery function.     

Do cryptocurrencies feel the music?

The aim of this paper is to examine the influence of global mood on cryptocurrencies trading activity. Following an original way, we construct a real-time language-free proxy of sentiment on the basis of songs investors choose to listen to. We use a sample of daily data spanning a period of four years and we find that our music-based measure is a reliable proxy of sentiment being responsive to mood swings of individuals. Interestingly, we show that music sentiment is negatively and significantly correlated with contemporaneous trading volume and price volatility, does not suffer from a recall bias, is not driven by any specific cryptocurrency and is robust to listeners' preferences. The effect is significantly persistent to up to four lags for price volatility and reverts at day 3 with respect to trading volume.     

Herding and the information content of trades in the Australian dollar market

This study shows that the information content of FX transactions depends on the identity of market participants. Using spot FX transactions of a major Australian bank, we find that central banks have the greatest price impact, followed by non-bank financial institutions (NBFIs) such as hedge funds and mutual funds. Trades by non-financial corporations have the least impact on dealer pricing. In the interbank market, dealers with greater private information tend to choose direct trading which has lower post-trade transparency. Indirect trading via brokers is partially revealed to the market and has little price impact. The price impact largely comes from institutions in the top quartile of the trading volume. Furthermore, NBFIs have the greatest propensity for herding, followed by interbank dealers. Non-financial corporations do not herd in their trades. Except for central banks, the differential impact of market participants can largely be explained by their propensity for herding.     

Exploring the relationship between cryptocurrencies and hedge funds during COVID-19 crisis

In this paper, we investigated the relationship between cryptocurrency market and hedge funds in two different ways. First, we focus on the dependence between Cryptocurrency hedge funds and conventional hedge funds strategies using VAR and VECM models, while analyzing the impact of COVID-19 on the hedge funds' values. Secondly, we choose between ARDL and ARDL-ECM models to study the effects of cryptocurrency price changes on Crypto- Currency hedge funds' values during COVID-19 crisis. Our empirical findings demonstrate that there is substantial interactions between Crypto-Currency and conventional hedge funds. The COVID-19 pandemic has significant negative impact on the performance of the following hedge funds: Event Driven, Relative Value and Distressed Debt fund strategies, this has reflected in a significant drop in their values during this critical period. However, we demonstrate that COVID-19 pandemic did not affect the relationship between crypto-currency hedge funds and both bitcoin and Ethereum. These findings hold profound implications for hedge funds managers, cryptocurrency market main players and policy makers. Our study is crucial in forecasting the performance of these markets especially during global pandemics.     

Toward an ontology‐driven blockchain design for supply‐chain provenance

An interesting research problem in our age of Big Data is that of determining provenance. Granular evaluation of provenance of physical goods (e.g., tracking ingredients of a pharmaceutical or demonstrating authenticity of luxury goods) has often not been possible with today's items that are produced and transported in complex, interorganizational, often internationally spanning supply chains. Recent adoptions of the Internet of Things and blockchain technologies give promise at better supply‐chain provenance. We are particularly interested in the blockchain, as many favored use cases of blockchain are for provenance tracking. We are also interested in applying ontologies, as there has been some work done on knowledge provenance, traceability, and food provenance using ontologies. In this paper, we make a case for why ontologies can contribute to blockchain design. To support this case, we analyze a traceability ontology and translate some of its representations to smart contracts that execute a provenance trace and enforce traceability constraints on the Ethereum blockchain platform.     

The impact of blockchain related name changes on corporate performance

This paper examines the impact of blockchain and crypto-related name changes on corporate and financial performance of the corporations. We document several pieces of evidence suggesting that companies who partake in such “crypto-exuberant” naming practices become more volatile and offer substantial and persistent stock market premiums as a reward for their corporate identity change. However, the retroactive name changes harm firm's short-term profitability and have a dampening effect on financial leverage of the company. This paper advances the Dotcom effect literature by providing novel results on the changing traditional pathways of price discovery and information flows after the announcement of corporate name changes to blockchain-related names. The identified contagion channels display that crypto-exuberant companies become more susceptible to cryptocurrency markets, which should interest regulators and investors.     

Which cryptocurrency data sources should scholars use?

Inspired by Alexander and Dakos (2020), we shed more light on the adequacy of data in the cryptocurrency literature by analysing the scaling properties and underlying processes of the main cryptocurrency databases (Coinmarketcap, Coingecko, BraveNewCoin and Cryptocompare) and exchange platforms (Coinbase, Bitstamp, Bittrex, Cexio and Exmo). Our results show that coin-ranking sites, such as Coinmarketcap, Coingecko and BraveNewCoin (i) include most of the cryptocurrency trading activity and (ii) are essentially characterised by the same underlying processes as the main exchange platforms (Coinbase and Bitstamp) and alternative coin-ranking sites (Cryptocompare), regardless of the possible issues arising from the aggregation of different exchanges to compute a unique cryptocurrency price. Therefore, we state that these databases are appropriate to conduct research. At any rate, we observe that all the databases analysed in this paper show the same underlying process for most liquid cryptocurrencies; consequently, scholars could use any of them for their studies, as long as they consider the different trading activity included by each database. This result is supported by an empirical analysis focused on weak-form market efficiency, since we report the same degree of efficiency regardless of the database and exchange platform. Nevertheless, we recognise the need for further research, given the gap in the literature and the black-box method used by coin-ranking sites to compute a unique cryptocurrency price.     

Trading Volume-Induced Spatial Autocorrelation in Real Estate Prices

Spatial dependence is often seen as a problem in econometrics rather than in economics. This study seeks to find an economic explanation for spatially correlated real estate prices. We posit spatial dependence as a process to discover price information from neighboring property transactions. Weaker spatial dependence is expected when price information in the immediate vicinity of a subject property is abundant. In the context of apartment buildings, in addition to the more commonly known horizontal dependence, there is also spatial dependence in the vertical dimension within the same building. Based on more than 18,000 transactions of highly homogeneous apartment units in Hong Kong, we found that the trading volume of a building depresses horizontal spatial dependence, but raises vertical spatial dependence. This not only confirmed the role of trading volume in the real estate price discovery process, but also questioned the validity of constant spatial autocorrelation assumption adopted in many studies.     

中国利率市场的价格发现——对国债现货、期货以及利率互换市场的研究

本文采用信息份额模型和基于向量自回归(VAR)模型的格兰杰因果检验,研究了国债现货、国债期货和利率互换三个市场之间的价格发现机制。信息份额模型表明,从整体来看利率互换相对于国债期货和国债现货都具有信息优势,而国债期货相对于国债现货具有信息优势。另外,国债期货的价格发现能力相对于另外两个市场都在随时间增强。格兰杰因果检验结果显示,利率互换在价格发现中单向引领国债期货以及国债现货,国债期货单向引领国债现货。所有结果一致表明, 利率互换和国债期货这两种利率衍生产品在引导中国利率市场价格发现中发挥了重要作用。     

An investigation of price discovery in informationally-linked markets: equity trading in Malaysia and Singapore

Using transactions data for the Kuala Lumpur Stock Exchange and the Stock Exchange of Singapore (SES) for a major Malaysian conglomerate, Sime Darby Berhad, and intraday exchange rate data, we investigate whether and to what extent each exchange contributes to price discovery. Results indicate that the price series are cointegrated. The raw data appear to indicate the presence of arbitrage opportunities, but none exist after taking exchange rate changes into account. Using the common long-memory factors of Gonzalo and Granger (1995, Journal of Business and Economic Statistics 13, 1–9), we show that while the majority of the price discovery (approximately 70%) occurs in the home country (Malaysia), the 26–32% of the price discovery attributable to the SES is statistically significant and exceeds Singapore’s share of the trading volume. Further, we find evidence of strong error correction of Singapore prices to Malaysian prices, but only weak error correction of Malaysian prices to Singapore prices.