Decentralization in financial markets operates at multiple layers, encompassing both the asset level, through tokenization, and the infrastructure level, via novel trading protocols such as Automated Market Makers (AMMs). This paper critically examines these twin dimensions of decentralization, focusing on the introduction of tokenized equities and the adoption of AMM mechanisms in securities market design. We find that the first tokenized equities struggle to gain adoption, resulting in poor liquidity (high price impact) and substantial price dislocations from the underlying shares. In contrast, we show that AMMs can significantly reduce transaction costs, particularly for large size trades in actively traded assets, using both matching and simulation mechanisms between centralized exchanges and AMMs. Our results suggest that while not all decentralized innovations face an easy path to adoption, some have significant potential for application to traditional asset classes.

We demonstrate that off-exchange (wholesaler) executions provide significant cost savings to retail investors. Wholesaler concentration has raised regulatory concerns; however, we show that the largest wholesalers offer the lowest costs due to economies of scale. The entry of a new large wholesaler reduces incumbent scale economies, resulting in higher execution costs. Most retail brokers route to multiple wholesalers and actively monitor their performance, rewarding those offering lower execution costs with more volume. While retail investors benefit from the current landscape across all stocks, those trading small stocks benefit the most.

ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

We examine how stock market closing price mechanisms affect liquidity, price ef- ficiency, and market integrity. Using hand-collected data on every major mechanism change in 45 markets during 17 years, we find that replacing simple mechanisms such as the last traded price with a closing auction typically improves market quality. However, auction design substantially impacts auction effectiveness – price stabilization features and randomized closing times are beneficial, whereas transparent indicative closing prices are often detrimental. The effects vary with the level of market development and liquidity, suggesting that when designing optimal closing mechanisms, there is no “one size fits all”.

Bitcoin is the largest blockchain, and provides the underlying UTXO architecture used by many other cryptocurrencies. We identify an inherent bias embedded in this architecture (the Note -breaker mechanism) which forces users to 'spend' the entire content of a wallet address in order to make a payment, receiving 'change' into a unique new address. This inflates both the apparent volume transacted and network users, as well as minimizing the apparent fees of transacting. We develop an innovative Transaction Identification Methodology (TIM) to quantify the economic value of transactions from raw blockchain data. Using four different algorithms across three stages, we achieve 95% accuracy in quantifying the degree of bias in these measures. Validated across more than 430 million Bitcoin transactions involving 600 million wallet addresses, our methodology reveals that the Notebreaker mechanism inflates transaction volumes 8 times, makes the actual costs of blockchain transactions appear 3-7 times more expensive than what is commonly reported, and inflates wallet counts - a common heuristic of unique adopter counts. We provide a remediation strategy to make Bitcoin blockchain data a more accurate represen-tation of reality, and provide a daily data set of these remediated volumes and transaction fees.

Transparency of data recorded on distributed ledgers has been hailed as a key benefit of blockchains, such as Bitcoin. Our examination of 389 million Bitcoin wallet addresses involving 183 million unique users shows that the way the “Notebreaker” wallet mechanism handles transactions introduces remarkable opacity into otherwise “transparent” data. Utilizing a novel algorithm that achieves 90% accuracy at separating “economic value” from “security value” in Notebreaker blockchains, we show that transaction volumes are inflated 7-8 times and transaction fees are inflated 7-15 times what is commonly believed when compared to economic transfer undertaken. A common heuristic for unique adopter counts – address counts – is also overstated. Our study identifies a key weakness in public Bitcoin data and the danger of extrapolating adoption levels of blockchains utilizing Notebreaker.

Transparency of data recorded on distributed ledgers has been hailed as a key benefit of blockchains, such as Bitcoin. Our examination of 389 million Bitcoin wallet addresses involving 183 million unique users shows that the way the “Notebreaker” wallet mechanism handles transactions introduces remarkable opacity into otherwise “transparent” data. Utilizing a novel algorithm that achieves 90% accuracy at separating “economic value” from “security value” in Notebreaker blockchains, we show that transaction volumes are inflated 7-8 times and transaction fees are inflated 7-15 times what is commonly believed when compared to economic transfer undertaken. A common heuristic for unique adopter counts – address counts – is also overstated. Our study identifies a key weakness in public Bitcoin data and the danger of extrapolating adoption levels of blockchains utilizing Notebreaker.

We analyze a cryptocurrency market structure setting where trading resembles equity markets, but spreads are unconstrained, assets have limited fundamental value and tick sizes are extremely small, facilitating undercutting. Using a high frequency dataset, we find that a significant tick size increase in this market reduces undercutting, encouraging traders to post more and larger limit orders and market orders. Increased liquidity provision also lowers quoted, effective and realized spreads for both institutional and retail sized trades and decreases short-term volatility. These results demonstrate that increasing extremely small tick sizes for unconstrained spreads leads to enhanced market quality. Our findings thus confirm theoretical predictions of a convex shape relationship between tick size and spread and verify that optimal tick size is non-zero. We contribute to the optimal tick size debate surrounding the US pilot study and provide evidence in support of a dynamic tick size, where the minimum tick size is linked to the share price and liquidity of the stock. JEL classification: G14 G15 C580

This paper analyses the effect of a tick size on a major cryptocurrency exchange where spreads are unconstrained, assets have limited fundamental value and tick sizes are extremely small, which facilitates undercutting. Using a unique high frequency dataset surrounding a significant increase in tick sizes on the cryptocurrency exchange Kraken, we find that undercutting decreases, leading to traders posting more and larger limit orders. Transaction costs and short-term volatility both decrease, contrary to previous findings in equity markets. We show that when spreads are unconstrained, market quality can be improved by increasing extremely small tick sizes. Our findings contribute to the optimal tick size debate, with particular implications for cryptocurrency and foreign exchange markets, where tick sizes are typically very small. JEL classification: G14 G15 C580

We examine the investibility of Bitcoin by exploring the trading dynamics and market microstructure of Bitcoin on three US cryptocurrency exchanges using high frequency intraday data of individual trades and quotes. Although all exchanges offer continuous trading, we find that the highest trading activity, highest volatility and lowest spreads coincide with US market trading hours, suggesting that most trades are non-algorithmic and executed by retail investors. We further find that average quoted and effective spreads for Bitcoin are lower than spreads on major equity exchanges, implying that Bitcoin is highly investible for retail size transactions.

The authors examine the relation between price returns and volatility changes in the Bitcoin market using a daily database denominated in US dollar. The results for the entire period provide no evidence of an asymmetric return-volatility relation in the Bitcoin market. The authors test if there is a difference in the return-volatility relation before and after the price crash of 2013 and show a significant inverse relation between past shocks and volatility before the crash and no significant relation after. This finding shows that, prior to the price crash of December 2013, positive shocks increased the conditional volatility more than negative shocks. This inverted asymmetric reaction of Bitcoin to positive and negative shocks is contrary to what one observes in equities. As leverage effect and volatility feedback do not adequately explain this reaction, the authors propose the safe-haven effect (Baur, Asymmetric volatility in the gold market, 2012). They highlight the benefits of adding Bitcoin to a US equity portfolio, especially in the pre-crash period. Robustness analyses show, among others, a negative relation between the US implied volatility index (VIX) and Bitcoin volatility. Those additional analyses further support the findings and provide useful information for economic actors who are interested in adding Bitcoin to their equity portfolios or are curious about the capabilities of Bitcoin as a financial asset.

This paper explores the financial asset capabilities of bitcoin using GARCH models. The initial model showed several similarities to gold and the dollar indicating hedging capabilities and advantages as a medium of exchange. The asymmetric GARCH showed that bitcoin may be useful in risk management and ideal for risk averse investors in anticipation of negative shocks to the market. Overall bitcoin has a place on the financial markets and in portfolio management as it can be classified as something in between gold and the American dollar on a scale from pure medium of exchange advantages to pure store of value advantages.

This paper sets out to explore the hedging capabilities of bitcoin by applying the asymmetric GARCH methodology used in investigation of gold. The results show that bitcoin can clearly be used as a hedge against stocks in the Financial Times Stock Exchange Index. Additionally bitcoin can be used as a hedge against the American dollar in the short-term. Bitcoin thereby possess some of the same hedging abilities as gold and can be included in the variety of tools available to market analysts to hedge market specific risk.