nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒11‒16
six papers chosen by
Thanos Verousis

  1. Non-Normal Identification for Price Discovery in High-Frequency Financial Markets By Sebastiano Michele Zema
  2. Classification of flash crashes using the Hawkes(p,q) framework By Alexander Wehrli; Didier Sornette
  3. Price response functions and spread impact in correlated financial markets By Juan C. Henao-Londono; Sebastian M. Krause; Thomas Guhr
  4. Realized volatility, jump and beta: evidence from Canadian stock market By Gajurel, Dinesh; Chowdhury, Biplob
  5. Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity By Isao Yagi; Yuji Masuda; Takanobu Mizuta
  6. Trading Strategies of a Leveraged ETF in a Continuous Double Auction Market Using an Agent-Based Simulation By Isao Yagi; Shunya Maruyama; Takanobu Mizuta

  1. By: Sebastiano Michele Zema
    Abstract: The possibility to measure the relative contribution of agents and exchanges to the price formation process in high-frequency financial markets acquired increasingly importance in the financial econometric literature. In this paper I propose to adopt fully data-driven approaches to identify structural vector error correction models (SVECM) typically used for price discovery. Exploiting the non-Normal distributions of the variables under consideration, I propose two novel variants of the widespread Information Share (IS) measure which are able to identify the leaders and the followers in the price formation process. The approaches will be illustrated both from a semiparametric and parametric standpoints, solving the identification problem with no need of increasing the computational complexity which usually arises when working at incredibly short time scales. Finally, an empirical application on IBM intraday data will be provided.
    Keywords: Information Shares; Structural VECM; Microstructure noise; Independent Component Analysis; Directed acyclic graphs.
    Date: 2020–10–26
  2. By: Alexander Wehrli (ETH Zürich); Didier Sornette (ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Swiss Finance Institute; Southern University of Science and Technology; Tokyo Institute of Technology)
    Abstract: We introduce a novel modelling framework - the Hawkes(p,q) process - which allows us to parsimoniously disentangle and quantify the time-varying share of high frequency financial price changes that are due to endogenous feedback processes and not exogenous impulses. We show how both flexible exogenous arrival intensities, as well as a time-dependent feedback parameter can be estimated in a structural manner using an Expectation Maximization algorithm. We use this approach to investigate potential characteristic signatures of anomalous market regimes in the vicinity of "flash crashes" - events where prices exhibit highly irregular and cascading dynamics. Our study covers some of the most liquid electronic financial markets, in particular equity and bond futures, foreign exchange and cryptocurrencies. Systematically balancing the degrees of freedom of both exogenously driving processes and endogenous feedback variation using information criteria, we show that the dynamics around such events are not universal, highlighting the usefulness of our approach (i) post-mortem for developing remedies and better future processes - e.g. improving circuit breakers or latency floor designs - and potentially (ii) ex-ante for short-term forecasts in the case of endogenously driven events. Finally, we test our proposed model against a process with refined treatment of exogenous clustering dynamics in the spirit of the recently proposed autoregressive moving-average (ARMA) point process.
    Keywords: Flash crash; Hawkes process; ARMA point process; High frequency financial data; Market microstructure; EM algorithm; Time-varying parameters
    JEL: C01 C40 C52
    Date: 2020–11
  3. By: Juan C. Henao-Londono; Sebastian M. Krause; Thomas Guhr
    Abstract: Recent research on the response of stock prices to trading activity revealed long lasting effects, even across stocks of different companies. These results imply non-Markovian effects in price formation and when trading many stocks at the same time, in particular trading costs and price correlations. How the price response is measured depends on data set and research focus. However, it is important to clarify, how the details of the price response definition modify the results. Here, we evaluate different price response implementations for the Trades and Quotes (TAQ) data set from the NASDAQ stock market and find that the results are qualitatively the same for two different definitions of time scale, but the response can vary by up to a factor of two. Further, we show the key importance of the order between trade signs and returns, displaying the changes in the signal strength. Moreover, we confirm the dominating contribution of immediate price response directly after a trade, as we find that delayed responses are suppressed. Finally, we test the impact of the spread in the price response, detecting that large spreads have stronger impact.
    Date: 2020–10
  4. By: Gajurel, Dinesh (University of New Brunswick); Chowdhury, Biplob (Tasmanian School of Business & Economics, University of Tasmania)
    Abstract: Inclusion of jump component in the price process has been a long debate in finance literature. In this paper, we identify and characterize jump risks in the Canadian stock market using high-frequency data from the Toronto Stock Exchange. Our results provide a strong evidence of jump clustering - about 90% of jumps occur within first 30 minutes of market opening for trade, and about 55% of jumps are due to the overnight returns. While average intraday jump is negative, jumps induced by overnight returns bring a cancellation effect yielding average size of the jumps to zero. We show that the economic significance of jump component in volatility forecasting is very nominal. Our results further demonstrate that market jumps and overnight returns bring significant changes in systematic risk (beta) of stocks. While the average effect of market jumps on beta is not significantly different than zero, the effect of overnight returns on beta is significant. Overall, our results suggest that jump risk is non-systematic in nature.
    Keywords: financial markets, stock price process, jumps, volatility, systematic risk
    JEL: C58 G12
    Date: 2020
  5. By: Isao Yagi; Yuji Masuda; Takanobu Mizuta
    Abstract: Many empirical studies have discussed market liquidity, which is regarded as a measure of a booming financial market. Further, various indicators for objectively evaluating market liquidity have also been proposed and their merits have been discussed. In recent years, the impact of high-frequency traders (HFTs) on financial markets has been a focal concern, but no studies have systematically discussed their relationship with major market liquidity indicators, including volume, tightness, resiliency, and depth. In this study, we used agent-based simulations to compare the major liquidity indicators in an artificial market where an HFT participated was compared to one where no HFT participated. The results showed that all liquidity indicators in the market where an HFT participated improved more than those in the market where no HFT participated. Furthermore, as a result of investigating the correlations between the major liquidity indicators in our simulations and the extant empirical literature, we found that market liquidity can be measured not only by the major liquidity indicators but also by execution rate. Therefore, it is suggested that it could be appropriate to employ execution rate as a novel liquidity indicator in future studies.
    Date: 2020–10
  6. By: Isao Yagi; Shunya Maruyama; Takanobu Mizuta
    Abstract: A leveraged ETF is a fund aimed at achieving a rate of return several times greater than that of the underlying asset such as Nikkei 225 futures. Recently, it has been suggested that rebalancing trades of a leveraged ETF may destabilize the financial markets. An empirical study using an agent-based simulation indicated that a rebalancing trade strategy could affect the price formation of an underlying asset market. However, no leveraged ETF trading method for suppressing the increase in volatility as much as possible has yet been proposed. In this paper, we compare different strategies of trading for a proposed trading model and report the results of our investigation regarding how best to suppress an increase in market volatility. As a result, it was found that as the minimum number of orders in a rebalancing trade increases, the impact on the market price formation decreases.
    Date: 2020–10

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