nep-mst New Economics Papers
on Market Microstructure
Issue of 2018‒11‒05
six papers chosen by
Thanos Verousis
University of Essex

  1. Exchange rate volatility: Trader's beliefs and the role of news By Roy Trivedi, Smita
  2. A High-Frequency Analysis of Bitcoin Markets By Alexander Brauneis; Roland Mestel; Ryan Riordan; Erik Theissen
  3. Price Discovery and the Accuracy of Consolidated Data Feeds in the U.S. Equity Markets By Brian F. Tivnan; David Slater; James R. Thompson; Tobin A. Bergen-Hill; Carl D. Burke; Shaun M. Brady; Matthew T. K. Koehler; Matthew T. McMahon; Brendan F. Tivnan; Jason Veneman
  4. Intraday Seasonalities and Nonstationarity of Trading Volume in Financial Markets: Individual and Cross-Sectional Features By Michelle B Graczyk; Silvio M D Queir\'os
  5. A Temporal Analysis of Intraday Volatility of Nifty Futures on the National Stock Exchange By Singh, Ritvik; Gangwar, Rachna
  6. Information Transmission under Increasing Political Tension – Evidence for the Berlin Produce Exchange 1887-1896 By Martin T. Bohl; Alexander Pütz; Pierre L. Siklos; Christoph Sulewski

  1. By: Roy Trivedi, Smita
    Abstract: The study of financial market volatility has focused on the unexpected and expected components of news (Vortelinos, 2015; Omrane and Hafner, 2015). We incorporate the role of biases arising from the 'availability' of recent outcomes to the traders, in influencing trading decisions. The theory of heuristics (Tversky and Kahneman, 1974) is used to build on the theory of trader's biases which helps to understand the reasons behind market volatility. Empirically the model is tested with five minute data on USD/INR and time stamped news from the US and Indian markets. We find that volatility is likely to be in higher ranges with increase in trader's biases, corresponding to unexpected news component. GARCH analysis of returns of average bid-ask rates shows that unexpected news, expected news and bias corresponding to expected news lead to increased volatility.
    Keywords: Exchange rate volatility, Unexpected and expected news, Trader's biases
    JEL: B49 C32 F31
    Date: 2018–09–30
  2. By: Alexander Brauneis (Department of Finance and Accounting, Alpen-Adria University Klagenfurt); Roland Mestel (Institute of Banking and Finance, Karl-Franzens-University Graz); Ryan Riordan (Smith School of Management, Queens University); Erik Theissen (Institute of Banking and Finance, University of Graz; Finance Area, University of Mannheim)
    Abstract: We study Bitcoin (BTC) exchanges on three continents, Bitfinex, Bitstamp and GDAX. We use a high frequency dataset that contains both transactions data and snapshots of the BTC to US dollar (BTCUSD) order book. The BTCUSD market is highly liquid in terms of bid-ask spreads and order book depth. While spreads are low, we find large differences between the three exchanges in terms of transaction and posted prices. The price differences fall over our sample period meaning that markets are becoming more integrated. We show that exchanges play an increasingly important role in the transfer of BTC. At the end of 2017, exchanges processed roughly 30% of BTC transfers at the end of our sample period this increases to 90%.
    Date: 2018–10–25
  3. By: Brian F. Tivnan; David Slater; James R. Thompson; Tobin A. Bergen-Hill; Carl D. Burke; Shaun M. Brady; Matthew T. K. Koehler; Matthew T. McMahon; Brendan F. Tivnan; Jason Veneman
    Abstract: Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor, the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor, or SIP, we focus here on its accuracy. Relying on Trade and Quote data, we provide various measures of SIP latency relative to high-speed data feeds between exchanges, known as direct feeds. We use first differences to highlight not only the divergence between the direct feeds and the SIP, but also the fundamental inaccuracy of the SIP. We find that as many as 60 percent or more of trades are reported out of sequence for stocks with high trade volume, therefore skewing simple measures such as returns. While not yet definitive, this analysis supports our preliminary conclusion that the underlying infrastructure of the SIP is currently unable to keep pace with the trading activity in today's stock market.
    Date: 2018–10
  4. By: Michelle B Graczyk; Silvio M D Queir\'os
    Abstract: We study the intraday behaviour of the statistical moments of the trading volume of the blue chip equities that composed the Dow Jones Industrial Average index between 2003 and 2014. By splitting that time interval into semesters, we provide a quantitative account of the non-stationary nature of the intraday statistical properties as well. Explicitly, we prove the well-known U-shape exhibited by the average trading volume-as well as the volatility of the price fluctuations-experienced a significant change from 2008 (the year of the sub-prime financial crisis) onwards. That has resulted in a faster relaxation after the market opening and relates to a consistent decrease in the convexity of the average trading volume intraday profile. Simultaneously, the last part of the session has become steeper as well, a modification that is likely to have been triggered by the new short-selling rules that were introduced in 2007 by the Securities and Exchange Commission. The combination of both results reveals that the has been turning into a t. Additionally, the analysis of higher-order cumulants namely the skewness and the kurtosis-shows that the morning and the afternoon parts of the trading session are each clearly associated with different statistical features and hence dynamical rules. Concretely, we claim that the large initial trading volume is due to wayward stocks whereas the large volume during the last part of the session hinges on a cohesive increase of the trading volume. That dissimilarity between the two parts of the trading session is stressed in periods of higher uproar in the market.
    Date: 2018–10
  5. By: Singh, Ritvik; Gangwar, Rachna
    Abstract: This paper aims to establish trends in intraday volatility in context of the Indian stock market and analyze the impact of development in the Indian economy on its stock market volatility. One minute tick data of Nifty 50 futures from Jan 1, 2011 to Aug 31, 2018 was used for the purpose of this research. Volatility was computed for each day of week and various time intervals. Our analysis shows evidence of the expected U-shaped pattern of intraday volatility (higher at the beginning and end of the day). We also observed a decline in the hourly volatility over the time period studied. However, sufficient evidence to determine the impact of development in the Indian economy on volatility in the stock market was not found.
    Keywords: Risk Analysis,Intraday Volatility,National Stock Exchange of India,Nifty Futures,Temporal Analysis
    JEL: G10 G13 G15
    Date: 2018
  6. By: Martin T. Bohl; Alexander Pütz; Pierre L. Siklos; Christoph Sulewski
    Abstract: This article studies the effects of increasing political uncertainty on the functioning of futures markets. For this purpose, we utilize a unique natural experiment, namely the discussions around and the final coming into force of the German Exchange Act of 1896. Using static and time-varying vector error correction models, the empirical analysis shows that, although early futures markets exhibit a high degree of operational efficiency, increasing political tensions were related to a declining dominance of the futures market in the price discovery process. In summary, we provide a strong illustration of the negative consequences of misplaced regulatory attempts caused by strong political interests.
    Keywords: Early commodity futures markets, Berlin Produce Exchange, Uncertainty, Price discovery, Regulation
    JEL: N23 N44 G14 G28 Q14 Q18
    Date: 2018–10

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