nep-fmk New Economics Papers
on Financial Markets
Issue of 2018‒07‒16
five papers chosen by

  1. Positive Stock Information In Out-Of-The-Money Option Prices By Konstantinos Gkionis; Alexandros Kostakis
  2. A Multi-Criteria Financial and Energy Portfolio Analysis of Hedge Fund Strategies By Allen, D.E.; McAleer, M.J.; Singh, A.K.
  3. Herding behavior in cryptocurrency markets By Obryan Poyser
  4. Multifractal characteristics and return predictability in the Chinese stock markets By Xin-Lan Fu; Xing-Lu Gao; Zheng Shan; Zhi-Qiang Jiang; Wei-Xing Zhou
  5. Non-linear Time Series and Artificial Neural Networks of Red Hat Volatility By Jos\'e Igor Morlanes

  1. By: Konstantinos Gkionis (Queen Mary University of London); Alexandros Kostakis (Alliance Manchester Business School, University of Manchester)
    Abstract: We examine whether the option market leads the stock market with respect to positive in addition to negative price discovery. We document that out-of-themoney (OTM) option prices, which determine the Risk-Neutral Skewness (RNS) of the underlying stock return’s distribution, can embed positive information regarding the underlying stock. A long-only portfolio of stocks with the highest RNS values yields significant positive alpha in the post-ranking week during the period 1996-2014. This outperformance is mainly driven by stocks that are relatively underpriced but are also exposed to greater downside risk. These findings are consistent with a trading mechanism where investors choose to exploit perceived stock underpricing via OTM options due to their embedded leverage, rather than directly buying the underlying stock to avoid exposure to its potential downside. Due to the absence of severe limits-to-arbitrage for the long-side, the price correction signalled by RNS is very quick, typically overnight.
    Keywords: Option-Implied Information, Price Discovery, Risk-Neutral Skewness, Stock Underpricing, Downside Risk
    JEL: G12 G13 G14
    Date: 2018–05–30
  2. By: Allen, D.E.; McAleer, M.J.; Singh, A.K.
    Abstract: The paper is concerned with a multi-criteria portfolio analysis of hedge fund strategies that are concerned with nancial commodities, including the possibility of energy spot, futures and exchange traded funds (ETF). It features a tri-criteria analysis of the Eurekahedge fund data strategy index data. We use nine Eurekahedge equally weighted main strategy indices for the portfolio analysis. The tri-criteria analysis features three objectives: return, risk and dispersion of risk objectives in a Multi-Criteria Optimisation (MCO) portfolio analysis. We vary the MCO return and risk targets, and contrast the results with four more standard portfolio optimisation criteria, namely tangency portfolio (MSR), most diversied portfolio (MDP), global minimum variance portfolio (GMW), and portfolios based on minimising expected shortfall (ERC). Backtests of the chosen portfolios for this hedge fund data set indicate that the use of MCO is accompanied by uncertainty about the a priori choice of optimal parameter settings for the decision criteria. The empirical results do not appear to outperform more standard bi-criteria portfolio analyses in the backtests undertaken on the hedge fund index data.
    Keywords: MCO, Portfolio Analysis, Hedge Fund Strategies, Multi-Criteria Optimisation, Genetic Algorithms, Spot prices, Futures pricees, Exchange Traded Funds (ETF)
    JEL: G15 G17 G32 C58 D53
    Date: 2018–06–11
  3. By: Obryan Poyser
    Abstract: There are no solid arguments to sustain that digital currencies are the future of online payments or the disruptive technology that some of its former participants declared when used to face critiques. This paper aims to solve the cryptocurrency puzzle from a behavioral finance perspective by finding the parallelism between biases present in financial markets that could be applied to cryptomarkets. Moreover, it is suggested that cryptocurrencies' prices are driven by herding, hence this study test herding behavior under asymmetric and symmetric conditions and the existence of different herding regimes by employing the Markov-Switching approach.
    Date: 2018–06
  4. By: Xin-Lan Fu (ECUST); Xing-Lu Gao (ECUST); Zheng Shan (ECUST); Zhi-Qiang Jiang (ECUST); Wei-Xing Zhou (ECUST)
    Abstract: By adopting Multifractal detrended fluctuation (MF-DFA) analysis methods, the multifractal nature is revealed in the high-frequency data of two typical indexes, the Shanghai Stock Exchange Composite 180 Index (SH180) and the Shenzhen Stock Exchange Composite Index (SZCI). The characteristics of the corresponding multifractal spectra are defined as a measurement of market volatility. It is found that there is a statistically significant relationship between the stock index returns and the spectral characteristics, which can be applied to forecast the future market return. The in-sample and out-of-sample tests on the return predictability of multifractal characteristics indicate the spectral width $\Delta {\alpha}$ is a significant and positive excess return predictor. Our results shed new lights on the application of multifractal nature in asset pricing.
    Date: 2018–06
  5. By: Jos\'e Igor Morlanes
    Abstract: We extend the empirical results published in article "Empirical Evidence on Arbitrage by Changing the Stock Exchange" by means of machine learning and advanced econometric methodologies based on Smooth Transition Regression models and Artificial Neural Networks.
    Date: 2018–06

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