nep-fmk New Economics Papers
on Financial Markets
Issue of 2017‒01‒08
three papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Optimal liquidation in a Level-I limit order book for large tick stocks By Antoine Jacquier; Hao Liu
  2. Regime Shifts in Excess Stock Return Predictability: An Out-of-Sample Portfolio Analysis By Giulia Dal Pra; Massimo Guidolin; Manuela Pedio; Fabiola Vasile
  3. The Relationship between Stock Market Volatility and Trading Volume: Evidence from South Africa By Pramod Kumar Naik; Rangan Gupta; Puja Padhi

  1. By: Antoine Jacquier; Hao Liu
    Abstract: We propose a framework to study the optimal liquidation strategy in a limit order book for large-tick stocks, with spread equal to one tick. All order book events (market orders, limit orders and cancellations) occur according to independent Poisson processes, with parameters depending on price move directions. Our goal is to maximise the expected terminal wealth of an agent who needs to liquidate her positions within a fixed time horizon. Assuming that the agent trades (through sell limit order or/and sell market order) only when the price moves, we model her liquidation procedure as a semi-Markov decision process, and compute the semi-Markov kernel using Laplace method in the language of queueing theory. The optimal liquidation policy is then solved by dynamic programming, and illustrated numerically.
    Date: 2017–01
  2. By: Giulia Dal Pra; Massimo Guidolin; Manuela Pedio; Fabiola Vasile
    Abstract: We analyze the recursive, out-of-sample performance of asset allocation decisions based on financial ratio-predictability under single-state linear and regime-switching models. We adopt both a statistical perspective to analyze whether models based on the dividend-price, earning price, and book-to-market ratios can forecast excess equity returns, and an economic approach that turns predictions into portfolio strategies. The strategies consist of a portfolio switching approach, a mean-variance framework, and a long-run dynamic model. We report an interesting disconnect between a statistical perspective, whereby the ratios yield a modest forecasting power, and a portfolio approach, by which a moderate predictability is occasionally sufficient to yield significant portfolio out performance, especially before transaction costs and when regimes are taken into account. However, also when regimes are considered, predictability gives high payoffs only to long-horizon, highly risk-averse asset managers. Moreover, different strategies deliver different performance rankings across predictors. Finally, we find evidence inconsistent with the notion that increasing sophistication in the way portfolio decisions are modeled, delivers a superior performance.
    Keywords: predictability, Markov switching, economic value, optimal portfolio choice
    Date: 2016
  3. By: Pramod Kumar Naik (Department of Economics, The Central University of Rajasthan, India); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa.); Puja Padhi (Department of Humanities and Social Sciences, Indian Institute of Technology, Bombay, India)
    Abstract: This paper revisits the relationship between equity trading volume and returns volatility for the Johannesburg Stock Exchange (JSE) of South Africa using daily data over the period of 6th July 2006 to 31st August 2016. Further, we analyzed an after-crisis period, i.e., 1/04/2008 to 8/31/2016, in order to verify the findings immediately after the sub-prime crisis. EGARCH and Granger causality models were employed to analyse the volume-volatility relationship. Also the level of volatility persistence has been compared before and after the inclusion of trading volume in the volatility model as an exogenous variable. The analysis shows that the JSE exhibits volatility asymmetry implying that the return volatility responds more to the bad news than the good news. The relationship between trading volume and market volatility is found to be positive and contemporaneous supporting the mixture of distribution hypothesis. But lagged volume is found to be statistically insignificant in explaining volatility. We also uncover that the volatility persistence remains high even after the inclusion of trading volume as an explanatory variable in the volatility model. The above set of results also holds for the post-crisis sub-sample. Furthermore, the pairwise Granger causality tests indicate a feedback relationship between volume and volatility only in the case of the sub-sample. But for the full sample we find a unidirectional causality between volume and volatility, with trading volume Granger causes market volatility.
    Keywords: Asymmetric volatility, Trading volume, EGARCH, South Africa, Volatility persistence
    JEL: G11 G12 C58
    Date: 2016–12

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