nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒04‒13
eight papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Autocorrelation of returns in major cryptocurrency markets By Eugene Tartakovsky; Ksenia Plesovskikh; Anastasiia Sarmakeeva; Alexander Bibik
  2. Speculative Pressure By John Hua; Adrian Fernandez-Perez; Ana-Maria Fuertes; Joelle Miffre
  3. A wavelet analysis of inter-dependence, contagion and long memory among global equity markets By Avishek Bhandari
  4. The Fed's enhanced swap lines and new interventions in the Treasury market By Richhild Moessner; William Anthony Allen
  5. Effects of MiFID II on stock price formation By Mike Derksen; Bas Kleijn; Robin de Vilder
  7. Stock Market Volatility Analysis: A Case Study of TUNindex By NEIFAR, MALIKA
  8. The Persistence of Stock Market Returns during the Presidential elections in Nigeria By Yaya, OlaOluwa S; Adekoya, Oluwasegun B.; Adesiyan, Femi

  1. By: Eugene Tartakovsky; Ksenia Plesovskikh; Anastasiia Sarmakeeva; Alexander Bibik
    Abstract: This paper is the first of a series of short articles that explore the efficiency of major cryptocurrency markets. A number of statistical tests and properties of statistical distributions will be used to assess if cryptocurrency markets are efficient, and how their efficiency changes over time. In this paper, we analyze autocorrelation of returns in major cryptocurrency markets using the following methods: Pearson's autocorrelation coefficient of different orders, Ljung-Box test, and first-order Pearson's autocorrelation coefficient in a rolling window. All experiments are conducted on the BTC/USD, ETH/USD, ETH/BTC markets on Bitfinex exchange, and the XBT/USD market on Bitmex exchange, each on 5-minute, 1-hour, 1-day, and 1-week time frames. The results are represented visually on charts. Statistically significant autocorrelation is persistently present on the 5m and 1H time frames on all markets. The tests disagree on the 1D and 1W time frames. The results of this article are fully reproducible. Used datasets, source code, and a runnable Jupyter Notebook are available on GitHub.
    Date: 2020–03
  2. By: John Hua (Griffith University [Brisbane]); Adrian Fernandez-Perez (AUT - Auckland University of Technology); Ana-Maria Fuertes (CASS Business School); Joelle Miffre (Audencia Business School)
    Abstract: The paper investigates the information content of speculative pressure across futures classes. Long-short portfolios of futures contracts sorted by speculative pressure capture a significant premium in commodity, currency and equity markets but not in fixed income markets. Exposure to commodity, currency and equity index futures' speculative pressure is priced in the broad cross-section after controlling for momentum, carry, global liquidity and volatility risks. The findings are confirmed by robustness tests using alternative speculative pressure signals, portfolio construction techniques and subsamples inter alia. We argue that there is an efficient hedgers-speculators risk transfer in commodity, currency and equity index futures markets.
    Keywords: Speculative pressure,Risk premium,Pricing,Futures markets
    Date: 2020–04–01
  3. By: Avishek Bhandari
    Abstract: This study attempts to investigate into the structure and features of global equity markets from a time-frequency perspective. An analysis grounded on this framework allows one to capture information from a different dimension, as opposed to the traditional time domain analyses, where multiscale structures of financial markets are clearly extracted. In financial time series, multiscale features manifest themselves due to presence of multiple time horizons. The existence of multiple time horizons necessitates a careful investigation of each time horizon separately as market structures are not homogenous across different time horizons. The presence of multiple time horizons, with varying levels of complexity, requires one to investigate financial time series from a heterogeneous market perspective where market players are said to operate at different investment horizons. This thesis extends the application of time-frequency based wavelet techniques to: i) analyse the interdependence of global equity markets from a heterogeneous investor perspective with a special focus on the Indian stock market, ii) investigate the contagion effect, if any, of financial crises on Indian stock market, and iii) to study fractality and scaling properties of global equity markets and analyse the efficiency of Indian stock markets using wavelet based long memory methods.
    Date: 2020–03
  4. By: Richhild Moessner; William Anthony Allen
    Abstract: In March 2020, the Federal Reserve enhanced its existing swap lines with foreign central banks, and introduced additional temporary swap lines with other central banks, in order to support the smooth functioning of U.S. dollar funding markets during the coronavirus epidemic. The Federal Reserve also announced purchases of US Treasuries and agency mortgage bonds in order to support the smooth functioning of the Treasury and mortgage-backed securities market. We analyse the motivations for and the effects of these measures.
    Keywords: Central bank swap lines, government bonds
    JEL: E52 E58
    Date: 2020–03
  5. By: Mike Derksen; Bas Kleijn; Robin de Vilder
    Abstract: On January 3, 2018 MiFID II regulations came into effect. This paper compares properties of European stocks for 2017 and 2018. The introduced tick size regime impacted the microstructure in accordance with existing literature on tick size changes. Remarkably, the modification of the microstructure also impacted volatility and transacted volume. Furthermore, it is shown that closing auction volumes increased heavily since MiFID II, leading to higher absolute returns in the auctions. Before MiFID II, high closing auction returns reverted overnight, but after MiFID II this reversion disappeared, showing that closing prices became more efficient.
    Date: 2020–03
  6. By: Yuliia Puzanova; M. Hakan Eratalay
    Abstract: This paper analyses the effect of real estate news sentiment on the stock returns of Swedbank and SEB Bank, which are leading banks in Sweden and the Baltic region. For this purpose, we have selected sentiments from news about real estate in the markets of these banks in Sweden, Estonia, Latvia, and Lithuania between 4 January 2016 and 19 February 2019. Estimation results showed that sentiments about the housing market affect stock returns for the banks, and showed the presence of the asymmetric effects of positive and negative news. We also found that there is a difference in the stock returns of these banks in terms of when and to what extent they react to news coming from the Baltic States and Sweden. Moreover, we found that the number of negative news affects the stock returns of the banks more than the strength of the news. We also apply several GARCH specifications to show that negative and positive news explain the asymmetric effects in the volatility processes to some extent. The asymmetric effects in the volatilities are captured much better by the GJR-GARCH and NA-GARCH models, implying that these effects are generated by idiosyncratic shocks rather than the sentiments in the news.
    Keywords: sentiment analysis, real estate market, Sweden , Baltics, Latvia, Lithuania, Estonia
    Date: 2020
    Abstract: Volatility is directly associated with risks and returns. This study aims to examine the volatility characteristics on Tunisian stock market index (5 days a weak TUNindex) that include clustering volatility, leptokurtosis, and leverage effect. The first objective is then to use the GARCH type models to estimate volatility of the daily returns series, consisting of 2191 observations from 01/02/2011 to 19/11/2019, with no significant weekdays effect. We use both symmetric and asymmetric models. The main findings suggest that the symmetric GARCHM and asymmetric TGARCH /APGARCH models can capture characteristics of TUNindex whereas EGARCH reveals no significant support for leverage effect existence. Looking at news impact curves, GJR model appears to be relatively better than other models. However, the volatility of stock returns is more affected by the past volatility than the related news from the previous period. The second objective is to use GARCHM- X S models to capture the effect of macro-economic instability via exchange rate growth and exchange rate volatility. For policy, GARCHM-XS2 turned to be the best model. The macroeconomic environment should be favourable to ensure growth in the stock market. Policies to reduce volatility in the the economy (more stable exchange rate) are a necessity for stock market.
    Keywords: Tunisia, Stock Market, Tunindex, Volatility, Symmetric and Asymmetric GARCH Models, GARCH, TGARCH, GARCH-M, EGARCH, GARCHM-XS, Leverage Effect., Risk Premium, Stability.
    JEL: C22 D8 D81 D82 E44 E47 O16
    Date: 2020–03–17
  8. By: Yaya, OlaOluwa S; Adekoya, Oluwasegun B.; Adesiyan, Femi
    Abstract: Following empirical evidences that political activities impact stock market performance, this present paper examines efficiency and volatility of Nigerian stock market during presidential elections. We use a 5-month event window approach to obtain the data for each election period. This implies that for each election period, we obtain the daily stock price index for the election month (4 weeks) and two months (8 weeks) before and after it. Our fractional integration technique reveals that the stock price index was persistent during most of the election years, with the exemptions of 2011 and 2019 election year, while 2015 election period recorded the highest volatility. However, accounting for structural breaks following the approach of Enders and Lee (2012a,b) that inculcates nonlinear smooth breaks in the Fourier function, the stock market seemed to be efficient only during the 1999, 2011 and 2019 presidential election periods. The 2011 and 2019 are periods when the elections produced candidates that ran for a two-term each. On the other hand, the highest stock market volatility is still maintained at the 2015 election which was also interestingly the year that the recent 2015/2016 recession in the country kick-started. Our findings have important policy implications for potential investors.
    Keywords: Nigerian stock market; Market efficiency; Volatility; Fractional integration; Presidential election
    JEL: C22
    Date: 2020–03–31

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