Shackling Short Sellers: The 2008 Shorting Ban

Instability and excessive volatilities in the various financial and non-financial markets have increased the importance of risk hedging in investments. So in this paper, the hypothetical options pricing have been done based on fractional Heston model in Iran’s gold market. In fact power option pricing has been driven using fast Fourier transformation (FFT), and maximum likelihood method. The study period has been classified to periods of three months and the profit and loss resulting from this option in each period has been calculated using discount rate based on the consumer price index. Findings indicated in most periods of three months, the model’s determination has been done correctly and the estimated price of power option has prevented the creation of arbitrage opportunity and caused a secure position of investment. But in the two periods related to 2018 due to high volatilities, the power option has created an arbitrage opportunity.

Power option pricing under the unstable conditions (Evidence of power option pricing under fractional Heston model in the Iran gold market)

Research Question: This paper examines the prevalence of noise trading and volatility asymmetry in the Chinese stock market. Motivation: Noise trader risk is a pervasive risk in the world's stock markets. It is driven by emotions and run counter to market stability. Noise trading has its practical repercussions. Hence, it is imperative for policymakers and investors to understand the behaviour and causes of noise risk to enhance market efficiency and optimize the financial decision-making process. Although most studies have confirmed the existence of noise in China's stock market, the volatility response findings have been mixed. Besides, prior studies found that China's stock market's volatility response behaves differently from its Western counterparts. Idea: In an attempt to examine the asymmetrical volatility response over different market conditions, we build our study on Feng et al. (2014) but over a different market sentiment period. Additionally, we combine our quantitative research with qualitative analysis. Hence, our paper verifies the existence of noise trading in China's stock market and dissects the plausible rationales behind the findings, keeping China’s unique historical developments and market conditions in mind. Data: Our sample data comprises the daily Shanghai Stock Exchange (SHSE) A-share index between 2nd January 2014 to 1st July 2019. Methods: We first employ a variance ratio method to test for noise trading evidence and subsequently develop an EGARCH-M model to detect yield asymmetry in the SHSE A-share market. Findings: Our result suggests that noise trading is prevalent in China's stock market and that market returns are more volatile in the face of good news than bad news. Hence, our findings are similar to Chen and Huang (2002) but contradict Feng et al. (2014). We attribute our findings to the investor's irrational investment psychology and behaviour, such as the widespread "catch up and kill down" operations among the noise traders and the market’s deficiencies. Contributions: Hence, our results provide important indications to investors and policymakers to assess the market conditions and devise optimal strategies.

Noise Trader Risk-Evidence from China’s Stock Market

The impact of noise on the market is one of main reasons for inefficiency of information and the deviation of stock price from its value. The measure of noise trading can realize the judgment of stock price. Through the variance ratio test method, this paper authenticated the existence of noise trading in Shanghai A-shares market. On this basis, this article established EGARCH-M models of stock price and trading volume, to authenticate the asymmetric effect of the impact of the information on stock prices and trading volume. In addition, this paper employed the fitting results of models to separate the noise trading, and then built the measure indexes of noise risk and noise components. Furthermore, this paper plotted the information impact curve of noise. This research has broken through the limitations of previous measure methods of noise trading.

Research on measure of noise trading in stock market based on EGARCH-M model

Since the introduction of margin trading in March 2010, the effect of margin trading on stock market volatility is always a hot topic. However, does margin trading stabilize stock market? What the effect in different lever period? The result of these studies is inconclusive. As one of the most important credit trading system, the margin trading provides a tool to avoid the market risks to investors. In Financial crisis, developed countries often take restrictive measures of margin trading to stabilize the market. From 2015 to 2016, the Chinese securities market has experienced a significant boom and crash, providing a precious natural opportunity for us to investigate the effect of margin trading in different lever period.The result shows that in the period of sharp leverage and de-leveraging, margin trading significantly heightened the stock market volatility. The finance load exchange heightened the stock market volatility, the securities load exchange exacerbated the stock market volatility. When the level in the reasonable interval, margin trading stabilized the stock market volatility. In this period, both finance and securities load exchange are stabilized the volatility. Therefore, regulatory authorities should be strengthen the supervision and regulation of the margin trading mechanism, the implementation of the policy of the margin trading, and it will play a positive effect to the market.

Research on the Effect of Margin and Short-selling on the Volatility of Stock Price

As a typical capital-intensive industry, real estate enterprises must keep plenty of cash flow. In recent years, the real estate market has stimulated constantly Chinese listed real estate companies to finance massively. As an important financing way of real estate enterprises, equity financing determined the overall financing situation of the real estate companies to a large degree. This paper employed dynamic data envelopment analysis method (DEA), to evaluate the equity financing efficiency of the real estate listed companies from 2008 to 2009. The empirical results showed that: the overall efficiency of equity financing of Chinese real estate listed companies was relatively low; the phenomenon of excess input was ubiquitous, the companies with high proportion of tradable shares were more likely to be DEA effective. At last, this paper provided measures to increase efficiency of equity financing of Chinese real estate listed companies.

Jin Feng

Papers

Research on measure of noise trading in stock market based on EGARCH-M model

Research on the Effect of Margin and Short-selling on the Volatility of Stock Price

Efficiency Evaluation on the Equity Financing of Chinese Real Estate Listed Companies Based on the Dynamic DEA Method