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Call Warrants on the China Security Market: Pricing Biases and Investors Confusion

Call Warrants on the China Security Market: Pricing Biases and Investors Confusion. Wei Fan Xinyi Yuan (School of Management, UESTC Chengdu 610054). Main Point. Abstract 1. Introduction 2. Data and Descriptive Statistics 3. The Model and Implementation

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Call Warrants on the China Security Market: Pricing Biases and Investors Confusion

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  1. Call Warrants on the China Security Market: Pricing Biases and Investors Confusion Wei Fan Xinyi Yuan (School of Management, UESTC Chengdu 610054) School of Management, UESTC

  2. Main Point • Abstract • 1. Introduction • 2. Data and Descriptive Statistics • 3. The Model and Implementation • 4. “Under the Lower Bound” Puzzle • 5. Arbitrage or Speculation? • 6. Conclusions www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  3. Abstract • This paper examines the price behavior of call warrants . For the whole period, the observed market prices are higher than the Black-Scholes model prices by 80.38% (using the 180-day history volatility) and 140.50% (using the EGARCH model volatility). • We pay attention to the anomalous phenomenon named “under the lower bound”. Our findings indicate that trading mechanism (short-selling constraints and T+1 trading mechanism) prevent investors driving the prices to a reasonable level. www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  4. 1.Introduction • Call warrants give their holders the right to buy the underlying shares for a certain price. These warrants in China are issued to facilitate the share reform. • On August 22, 2005, the first warrant (Baogang JTB1 ) was issued on the Shanghai Stock Exchange. As of January 2007, according to Goldman Sachs, China warrant market is the biggest in the world in terms of annual trading volume, surpassing that of Hong Kong. www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  5. 1.Introduction • Many countries’ warrant markets have been investigated: • Kremer and Roenfeldt (1993):U.S market • Schulz and Trautmann (1989, 1994):Germany market • Mikami (1990), Kuwahara and Marsh (1992):Japan market • Veld (1992): Dutch market • Vichienhotu (2005) : Thai market • China market: • Lin Hai, Zheng Zhenlong and Peng Bo(2005) • Xinyi Yuan, Wei Fan, and Qiang Liu(2007 b) www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  6. 2.Data and Descriptive Statistics • Sample period: from August 22, 2005 to March 31, 2007 • No.:17 call warrants daily data • Classify: • Class A (Exercise Style): • Bermudan Style:16 European Style:1 • Class B (Issuer Type): • Equity Warrant:7 Covered Warrant:10 • Class C (Moneyness): • 1-1.4: 1 1.4-1.8: 1 ＞1.8: 11 www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  7. 2.Data and Descriptive Statistics • Statistics for the volatility of the underlying stocks during the sample period show: big volatility • The average volatility is about 0.5. • Theoretically speaking, as volatility increase, the warrant value will increase. www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  8. 3. The Model and Implementation As to the volatility parameter, we consider two kinds of volatility models here : History volatility(Hull(2005)) Stochastic volatility derived from Exponential Generalized Autoregression Conditional Heteroscedasticity (EGARCH) model (Nelson (1991)) www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  9. 3. The Model and Implementation • History volatility: • Where ui is the return in the ith interval and it can be written as follows: • EGARCH (p,q) model: www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  10. 3. The Model and Implementation • Dividing the whole sample into seven sub-sample sections. • We calculate the bias between the Black-Scholes model prices and the market prices among the whole sample section and different sub-sample sections . • Bias is defined as follows: www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  11. www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  12. 3. The Model and Implementation • We find that the market prices of the warrants are much higher than the B/S model prices whether we use the 180-day history volatility or the EGARCH volatility. • The average market price is higher than the model price by 80.38% (using the 180-day history volatility) and 140.50% (using the EGARCH volatility). www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  13. 3. The Model and Implementation • From the 3rd quarter 2005 to the 1st quarter 2007, the bias between market price and model price is diminishing • It states that: the price behavior is abnormal, however, it is getting rational gradually. As to the reason, among the investors of warrants, the majority are small, individual investors, who may be confused how to price warrants and are still learning this new derivative product in China. www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  14. 4. “Under the Lower Bound” Puzzle • Definition: Since late 2006, some warrants price (11 of 17) are not only lower than the model price, but also under the lower bound, namely S-Xe-rt (Merton (1973)), such as: www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  15. 4. “Under the Lower Bound” Puzzle • Possible reasons: • liquidity of the warrant market (false); • transaction cost (false); • short-selling constraints (true); • T+1 trading mechanism of the underlying stock (true). www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  16. 5 Arbitrage or Speculation? • Case 1:If the investors hold the underlying stock : theoretically speaking, they should substitute warrant for stock or buy the warrant. • Case 2:Warrant price is still under the lower bound on the last trading date, even in the last minutes: • 1:Not having stock: speculation • 2.Having stock: arbitrage www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  17. 6. Conclusions • This paper provides a China market sample to examine the price performance of the call warrants in a Black-Scholes valuation framework. It is found that : • The observed market prices are higher than the model prices commonly. • The prices of warrants are getting rational gradually. • The” under the lower bound” puzzle results from the trading mechanism, including short-selling constraints and T+1 trading mechanism. • Arbitrage chances exist in some specific cases when call warrant price is under the lower bound. www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

  18. Thank you very much! www.swingtum.com/institute/IWIFSchool of Management, UESTC IWIF-II

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