Implied Volatility in Singaporean Structured Warrants (2014-2015)

Abstract

Options traders regard implied volatility a vital variable to determine profitability in options trading and use it to estimate the underlying stock’s volatility in the future. While it cannot predict market direction, it has a reputation for forecasting — to a certain extent — potential for large swings by the underlying stock. Once implied volatility is calculated, traders can estimate how high and low the stock can swing by the option’s expiration, and this probable estimation aids in making informed trading decisions. In this paper, we examine the information content of implied volatility of structured warrants in Singapore Stock Exchange (SGX). Using a daily dataset for 252 trading days for a period between August 1, 2014 and July 31, 2015, we test whether implied volatility is an unbiased estimate of realized volatility, if implied volatility contains information on future realized volatility, scrutinize the efficiency of implied volatility and its predictive power compared to historical volatility. Our findings suggest that for although implied volatility does contain some relevant information about future volatility, it is a biased forecast of realized volatility, the efficiency threshold of implied volatility is nugatory, and its predictive power is not superior to historical volatility.

 

Co-authored with Ismail, Najmi and Mohamad, Azhar

Under review for the 29th Asian Finance Association        Conference, 2017.

Predicting Financial Distress based on Corporate Actions: Malaysian Evidence

Abstract

Despite abundance of literature in the area of financial distress prediction modelling, very little research has been conducted on the field of the ability of corporate actions in predicting financial distress. The current use of accounting information for financial distress prediction poses problem of information of not being current while corporate actions taken by firms are disclosed promptly. The objective of this study is to investigate which corporate actions can help to predict financial distress and to investigate whether prediction of financial distress using corporate actions can improve classification accuracy compared to accounting ratios. We also compare a model based on a combination of accounting ratios and corporate actions with models using these information separately. Using a Logistic Regression with a sample of 54 Malaysian Public Listed Companies we come up with three models- we find the use of corporate actions as financial distress predictors improves the classification accuracy of the model to 92.6 per cent, which is higher compared to the use of only accounting ratios or a combination of accounting ratios and corporate actions.  We also find that the frequency of new issue of capital and frequency of changes in Audit Committee are significant predictors of financial distress. Furthermore, we find that Working Capital to Total Assets Ratio and Asset Turnover Ratio are reliable predictors of financial distress among the accounting ratios.

  • Preliminary findings of this paper were presented at 3rd International Conference on Global Business & Social Entrepreneurship at Johor Bahru, Johor in 2016. The paper is presently submitted for consideration at a Scopus indexed journal. 
  • Co-authored with Dr. Azhar Mohamad and Mohamed Azad
  • In this study we analyze the predictive ability of a number of corporate actions taken by Malaysian public listed companies. We find that frequency of new issue of shares and changes in Audit Committee are statistically significant at predicting financial distress. We also find that the model using only corporate actions to predict financial distress can improve the classification rate of financially distressed firms to 92.6% compared with the baseline model of 66.7%.