nep-mst New Economics Papers
on Market Microstructure
Issue of 2018‒12‒24
five papers chosen by
Thanos Verousis

  1. Who Influences the Fundamental Value of Commodity Futures in Japan? By Kentaro Iwatsubo; Clinton Watkins
  2. The Price of BitCoin: GARCH Evidence from High Frequency Data By Pavel Ciaian; d'Artis Kancs; Miroslava Rajcaniova
  3. Cojumps and Asset Allocation in International Equity Markets By Arouri, Mohamed El Hedi; M’saddek, Oussama; Nguyen, Duc Khuong; Pukthuanthong, Kuntara
  4. Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy By Sonam Srivastava; Ritabratta Bhattacharya
  5. High and Low Intraday Commodity Prices: A Fractional Integration and Cointegration Approach By Yaya, OlaOluwa S; Gil-Alana, Luis A.

  1. By: Kentaro Iwatsubo (Graduate School of Economics, Kobe University); Clinton Watkins (Graduate School of Economics, Kobe University)
    Abstract: We present evidence on asymmetric information content in the trades of six investor groups transacting in the gold, platinum, gasoline and rubber futures markets on the Tokyo Commodity Exchange. Microstructure theory suggests that traders with greater information on the efficient price should be more profitable in the long run. Foreign investors have the greatest influence over the efficient price in the gold market, investment funds in the platinum market and retail investors in the gasoline market. Both trade and non-trade related innovations have an equal influence on the efficient price of rubber, with trades by investment funds having the largest information content in this market. We relate differences in the relative influence of investor groups to differences in market interconnectedness, the nature of the commodity and associated fundamental information.
    Keywords: Commodities, Futures, Market microstructure, Asymmetric information, Investor behaviour
    JEL: C22 G14 G15 Q02
    Date: 2018–12
  2. By: Pavel Ciaian; d'Artis Kancs; Miroslava Rajcaniova
    Abstract: The present paper analyses the BitCoin price formation accounting for the both transaction demand and speculative demand. We apply a GARCH model to high frequency data for the period 2013–2018. In line with the theoretical model, our empirical results confirm that the BitCoin transaction demand and speculative demand for BitCoin have a statistically significant impact on the BitCoin price formation. The BitCoin price responds negatively to higher BitCoin velocity, whereas the BitCoin stock, interest rate and the size of the BitCoin economy exercise an upward pressure on the BitCoin price.
    Keywords: Virtual currencies, BitCoin returns, volatility, price formation, GARCH.
    JEL: E31 E42 G12
    Date: 2018–12–14
  3. By: Arouri, Mohamed El Hedi; M’saddek, Oussama; Nguyen, Duc Khuong; Pukthuanthong, Kuntara
    Abstract: This paper examines the patterns of intraday cojumps between international equity markets as well as their impact on international asset holdings and portfolio diversification benefits. Using intraday index-based data for exchange-traded funds as proxies for international equity markets, we document evidence of significant cojumps, with the intensity increasing during the global financial crisis of 2008-2009. The application of the Hawkes process also shows that jumps propagate from the US and other developed markets to emerg- ing markets. Correlated jumps are found to reduce diversification benefits and foreign asset holdings in minimum risk portfolios, whereas idiosyncratic jumps increase the diversification benefits of international equity portfolios. In contrast, the impact of higher-order moments induced by idiosyncratic and systematic jumps on the optimal composition of international portfolios is not significant.
    Keywords: Cojumps; Foreign asset holdings; International diversification.
    JEL: G11 G12 G15
    Date: 2017–01
  4. By: Sonam Srivastava; Ritabratta Bhattacharya
    Abstract: Financial markets change their behaviours abruptly. The mean, variance and correlation patterns of stocks can vary dramatically, triggered by fundamental changes in macroeconomic variables, policies or regulations. A trader needs to adapt her trading style to make the best out of the different phases in the stock markets. Similarly, an investor might want to invest in different asset classes in different market regimes for a stable risk adjusted return profile. Here, we explore the use of State Switching Markov Autoregressive models for identifying and predicting different market regimes loosely modeled on the Wyckoff Price Regimes of accumulation, distribution, advance and decline. We explore the behaviour of various asset classes and market sectors in the identified regimes. We look at the trading strategies like trend following, range trading, retracement trading and breakout trading in the given market regimes and tailor them for the specific regimes. We tie together the best trading strategy and asset allocation for the identified market regimes to come up with a robust dynamically adaptive trading system to outperform simple traditional alphas.
    Date: 2018–12
  5. By: Yaya, OlaOluwa S; Gil-Alana, Luis A.
    Abstract: This paper examines the behaviour of high and low prices of four commodities, namely crude oil, natural gas, gold and silver, and of the corresponding ranges using both daily and intraday data at various frequencies. For this purpose, it applies fractional integration and cointegration techniques; in particular, an FCVAR model is estimated to capture both the long-run equilibrium relationships between high and low commodity prices, referred to as the range, and the long-memory properties of their linear combination. Fractional cointegration in found in all cases, with the range showing stationary and nonstationary patterns and changing substantially across the frequencies. The findings may assist investors in improving their trading strategies since high and low prices serve as entry and exit signals in the market.
    Keywords: Commodity prices, intraday, fractional integration, fractional cointegration, FCVAR
    JEL: C22 C32 G11 G15
    Date: 2018–12–05

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