Analysis of stock price fluctuation models.
It has been several years since the establishment of the base volume rule in the capital market. A rule that inevitably performs pricing instead of valuation and determines the real price in the market. Determining the base volume and price threshold to prevent price fluctuations is known as one of the most important control policies of the supervisory body in the Iranian Stock Exchange, which has been established for several years. This policy has not provided a clear scientific basis, but it has been widely accepted in order to prevent stock price fluctuations on each business day in the market. Of course, it has been criticized sporadically. For some stocks, when long sales queues are formed, the stock price decreases due to systematic reasons and excitement, and this decrease sometimes reaches 10 business days or more without many people in the queue succeeding in selling their shares. The mismatch of the equal fluctuation range for the daily transient price of securities transactions has created problems for reducing the value of companies' shares in the capital market.
Research shows that studies have often been conducted on the nature of the range of fluctuations and the volume of the basis in various stock markets since the 19th century. Among them, Fama studies used descriptive and inferential statistics and Wilcoxon models. Studies have been conducted to investigate the existence of a correlation between independent variables in research in this area. The common point of these studies to provide new methods and models has not been the range of fluctuations. Rather, they have examined the effective factors and intervening variables in the models provided by the stock exchanges. Some have considered these studies for short-term time periods and some for long-term time periods.
Price volatility refers to the degree of change in asset prices over time. It is a key measure of market risk because it indicates the extent to which prices deviate from their average values. Volatility can be caused by various factors, including changes in economic fundamentals, investor sentiment, external shocks, and market liquidity. Theoretical models of volatility aim to explain and quantify these price fluctuations. One of the most common models for measuring price volatility is the GARCH (Generalized Autoregressive Conditional Volatility) model, developed by Tim Bollersloe in 1986. The GARCH model captures changes in the variance of asset returns over time and assumes that volatility is not usually constant but varies in response to market conditions. The GARCH model has been widely used to forecast volatility in financial markets and assess risk. Different variants of the GARCH model, such as EGARCH (exponential GARCH) and TGARCH (threshold GARCH), have been introduced to account for asymmetries in volatility, as market reactions to positive and negative news may differ. Another important theory is the phenomenon of volatility clustering, which suggests that large changes in asset prices are likely to be followed by other large changes, while small changes tend to be accompanied by other small changes. This theory is based on the idea that financial markets exhibit a degree of momentum; Where periods of high volatility are usually followed by more volatility and vice versa. Stochastic volatility (SV) and implied volatility (IV) models are also commonly used to estimate the volatility of an asset based on market data such as option prices. The Black-Scholes model for option pricing considers the concept of volatility as a critical input that reflects the uncertainty in the future price of the underlying asset. Similarly, the VIX (volatility index), often referred to as the “fear index,” measures the market’s expectations of future volatility based on option prices; particularly options on the S&P 500 index. Despite advanced models, volatility forecasting remains a challenging task because financial markets are affected by a wide range of unpredictable factors, including geopolitical events, natural disasters, and investor behavior. Therefore, volatility forecasting models are often subject to significant errors; Especially in times of crisis or high uncertainty.