nep-fmk New Economics Papers
on Financial Markets
Issue of 2017‒10‒22
five papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Behavioral Finance Option Pricing Formulas Consistent with Rational Dynamic Asset Pricing By Svetlozar Rachev; Stoyan Stoyanov; Frank J. Fabozzi
  2. Time-Varying Efficiency of Developed and Emerging Bond Markets: Evidence from Long-Spans of Historical Data By Lanouar Charfeddine; Karim Ben Khediri; Goodness C. Aye; Rangan Gupta
  3. Squaring Venture Capital Valuations with Reality By William Gornall; Ilya A. Strebulaev
  4. Time-Varying Price Discovery and Autoregressive Loading Factors: Evidence from S&P 500 Cash and E-Mini Futures Markets By Hou, Yang; Li, Steven
  5. Price Discovery in the Stock Index Futures Market: Evidence from the Chinese stock market crash By Hou, Yang; Nartea, Gilbert

  1. By: Svetlozar Rachev; Stoyan Stoyanov; Frank J. Fabozzi
    Abstract: We derive behavioral finance option pricing formulas consistent with the rational dynamic asset pricing theory. In the existing behavioral finance option pricing formulas, the price process of the representative agent is not a semimartingale, which leads to arbitrage opportunities for the option seller. In the literature on behavioral finance option pricing it is allowed the option buyer and seller to have different views on the instantaneous mean return of the underlying price process, which leads to arbitrage opportunities according to Black (1972). We adjust the behavioral finance option pricing formulas to be consistent with the rational dynamic asset pricing theory, by introducing transaction costs on the velocity of trades which offset the gains from the arbitrage trades.
    Date: 2017–10
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1710.03205&r=fmk
  2. By: Lanouar Charfeddine (College of Business and Economics, Qatar University); Karim Ben Khediri (CEROS, Université Paris Nanterre, France and FSEG Nabeul, University of Carthage, Tunisia); Goodness C. Aye (Department of Economics, University of Pretoria, South Africa); Rangan Gupta (University of Pretoria, Pretoria, South Africa)
    Abstract: Bonds have become an important part of investment portfolios for individuals as well as for institutions, particularly after the recent financial crisis. This paper empirically investigates the Adaptive Market Hypothesis (AMH) in two of the most established bond markets in the world: the US and UK and two emerging markets: South Africa and India, using monthly data series spanning very long time periods. We examine the long memory properties of the series using GPH, ELW and FELW and multiple structural breaks technique to examine possibility of structural breaks. We then examine the weak-form efficiency of government bond markets, using a time varying approaches namely the state-space generalized autoregressive conditional heteroscedasticity in mean (GARCH-M) to date the time varying behavior of bond market efficiency. Results show that efficiency of these markets has been changing over time, depending on the prevailing economic, political and market conditions. Further, we observe that the degree of the weak-form efficiency of these markets has been gradually improving recently. In particular, the US government bond market has been highly efficient, showing the highest degree of market efficiency among the four bond markets. Overall, our results suggest that the AMH provides a better description of the behavior of government bond returns than the Efficient Market Hypothesis (EMH).
    Keywords: Adaptive market hypothesis, Bond Market, GARCH-M, Long memory, Market Efficiency, State-space Model, Time-varying
    JEL: C22 G12
    Date: 2017–10
    URL: http://d.repec.org/n?u=RePEc:pre:wpaper:201771&r=fmk
  3. By: William Gornall; Ilya A. Strebulaev
    Abstract: We develop a valuation model for venture capital-backed companies and apply it to 135 U.S. unicorns – private companies with reported valuations above $1 billion. We value unicorns using financial terms from legal filings and find reported unicorn post-money valuation average 50% above fair value, with 15 being more than 100% above. Reported valuations assume all shares are as valuable as the most recently issued preferred shares. We calculate values for each share class, which yields lower valuations because most unicorns gave recent investors major protections such as a IPO return guarantees (14%), vetoes over down-IPOs (24%), or seniority to all other investors (32%). Common shares lack all such protections and are 58% overvalued. After adjusting for these valuation-inflating terms, almost one-half (65 out of 135) of unicorns lose their unicorn status.
    JEL: G13 G24 G32 M13
    Date: 2017–10
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:23895&r=fmk
  4. By: Hou, Yang; Li, Steven
    Abstract: The error correction coefficients, known as the loading factors, are a key component for price discovery measurement. To date, only constant loading factors have been considered for the price discovery measurement. This paper attempts to consider the autoregressive loading factors and their implications for the price discovery measurement. Based on the minute-by-minute data from the S&P 500 cash and E-mini futures markets, this paper reveals that the loading factors are indeed autoregressive. Furthermore, we propose three AR(1) processes for the loading factors and assess their performance in price discovery measurement compared to the constant loading factor model. Overall, this research provides supporting empirical evidence for using autoregressive loading factors for the price discovery measurement.
    Keywords: Price Discovery, Information Share, S&P 500 E-mini Futures, AGDCC GARCH, Loading Factor, Error Correction Coefficient
    JEL: G13 G14 G15
    Date: 2017–10–17
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:81999&r=fmk
  5. By: Hou, Yang; Nartea, Gilbert
    Abstract: This paper examines time-varying price discovery of the Chinese stock index futures market during a stock market crash in 2015. We find that the index futures market plays a long-run leading role in terms of its higher static and dynamic generalised information share (GIS) than both the Shanghai and Shenzhen A share markets during the market turbulence. The expected trading volume in each market improves GIS of that market. The importance of trading activities by the majority of investors in increasing market efficiency during a crash is underscored. Government intervention on futures trading impairs price discovery in the futures market.
    Keywords: Generalised Information Share, Price Discovery, GARCH model, Chinese stock market crash, Chinese stock index futures
    JEL: G13 G14 G15
    Date: 2017–10–17
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:81995&r=fmk

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