Understanding Market Volatility Metrics

Alternative Measures of Volatility

Volatility refers the degree of variations that occur in trading prices over different timelines and is measured by the standard deviation of logarithmic returns. Historic volatility, also known as the realized volatility, is a measure of time series that evaluates market prices from the historical perspective. Implied volatility, as opposed to historic volatility, shows the future predictions of market prices derived from trends in market prices of a market-traded derivative with the current and historic data. Both methods of volatility are integral in conducting researchers pertaining to market values and their prediction and thus used in different occasions. For the realized volatility, some of the weaknesses it possesses are inclusive of inaccurate information to predict future outcomes, and decisions made by the approach do not focus on current and future market strategies.

Historical information may be distorted from different outcomes and thus becoming inaccurate to use as in projecting future interventions. The information may be irrelevant owing to the changes in market trends and practices, and reliance on the same can yield unwanted results and cause major financial losses. Therefore, it is recommended that the use of Historical Volatility practices be comprehended by implied volatility to eliminate some of these concerns. However, it is notable that the historical volatility also parades various benefits within the industry. One of these benefits is that investors get insights on their intended trading time frames from historical information and can thus make informed decisions on whether or not to invest their money on given firms. Since the information is based on historical outcomes, there is increased probability that the outcomes experienced over a given month can be repeated. Therefore investors can decide on whether or not to invest with large sums of money or not on the given firm.

Summary of Views from different authors

The idea of implied volatility being a vital tool in forecasting market price changes is integral in forecasting index volatility. Blair, Poon, & Taylor, (2010) argue that the high frequency index returns avail relevant information from sample estimates to enable investors understand the future predictions on various market trends as guided by the ARCH Model. Forecasts on financial performance will thus provide evidence on whether or not investments made can yield enough profits to be termed as successful or the future returns are not worth the investments from various stockholders. However, the scholar purport that the implied volatility is less accurate as compared to the simple historical volatility, with the argument being that the historical one has a longer period to be used for analysis while the implied one is based on forecasts that cannot have historical information to back it up. Therefore, the authors recommend that both historical and implied volatility be used in decision making for the investors.

It is important to note, however, that realized volatility based on historical perspectives is not perfect. According to Andersen & Benzoni (2008), the most common realized volatility measure emanates from the sum of return realizations that have been properly sampled and refined covering a specific period of time. Therefore, consistency in market trends will always show positivity with regards to investments that can be made in the industry. Therefore, within simple settings, the use of simple historical volatility measures to guide decision making is essential and can ensure positive results are obtained. However, as the authors condone, reliance on only the realized volatility measures to inform investment decisions can lead to poor decision making as the information can fail. Therefore, these scholars recommend development of investment frameworks that use different tools to evaluate the market prior to investing in the same.

References

• Andersen & Benzoni (2008). Realized Volatility
• Blair, B. J., Poon, S. H., & Taylor, S. J. (2010). Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns. In Handbook of Quantitative Finance and Risk Management (pp. 1333-1344). Springer, Boston, MA.