Volatility refers to the degree of variation of a trading price series over time. It is a statistical measure of the dispersion of returns for a given security or market index. High volatility indicates a greater potential for significant price swings, both upward and downward, while low volatility suggests a more stable and predictable price movement. Volatility plays a central role in options pricing, risk management, and overall market analysis, providing insights into the level of uncertainty and market sentiment at a given time.
Similar to the risks associated with stocks, the volatility of an individual stock can also be broken down into two components: idiosyncratic and systematic volatilities. In a model proposed by Reference [1], the price and volatility dynamics of a stock are described using separate stochastic differential equations: one for the stock price, one for the idiosyncratic volatility, and one for the systematic volatility.
The authors studied volatility estimation in different timeframes. They pointed out,
Our research provides insights into the complexity of volatility estimation within financial markets, elucidating the dynamics of retail traders’ impact and the evolving landscape shaped by the fintech revolution. The characterization of retail traders, as underscored by our study, is multifaceted; they amplify idiosyncratic risk while simultaneously mitigating broader market risk.
We demonstrate that the granularity of time intervals is pivotal in capturing risks, ranging from idiosyncratic nuances to systematic trends. Moreover, our findings challenge prevailing narratives, revealing that retail traders, often underestimated, exhibit strategic responses to market shifts, thus playing a role traditionally attributed to more seasoned market actors.
In short, the research results show that the selection of time intervals in volatility calculations distinctly captures various aspects of risks, depending on the specific mean-reversion process of the stock. Volatilities calculated over short intervals predominantly capture idiosyncratic risk, whereas longer intervals are more effective in capturing systematic risk.
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References
[1] Sanford, Anthony and Ma, Yue, A Tale of Two Risks: The Role of Time in the Decomposition of Total Risk into Systematic and Idiosyncratic risks (2023). https://ssrn.com/abstract=4600474
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