Abstract

In this work, the financial data of 377 stocks of Standard & Poor’s 500 Index (S&P 500) from the years 1998–2012 with a 250-day time window were investigated by measuring realized stock returns and realized volatility. We examined the normal distribution and frequency distribution for both daily stock returns and volatility. We also determined the beta-coefficient and correlation among the stocks for 15 years and found that, during the crisis period, the beta-coefficient between the market index and stock’s prices and correlation among stock’s prices increased remarkably and decreased during the non-crisis period. We compared the stock volatility and stock returns for specific time periods i.e., non-crisis, before crisis and during crisis year in detail and found that the distribution behaviors of stock return prices has a better long-term effect that allows predictions of near-future market behavior than realized volatility of stock returns. Our detailed statistical analysis provides a valuable guideline for both researchers and market participants because it provides a significantly clearer comparison of the strengths and weaknesses of the two methods.

Highlights

  • At present, stock market investment is one of the most significant parts of the nations’ economy

  • In the stock market the most crucial part is predicting the stock price of listed companies and their estimated real value, since prices are a crucial point to guide the effective allocation of capital and liquidity that might be considered as a powerful tool in the efficient allocation of resources (Azizian 2006)

  • The average range for each day was used to measure the volatility of Standard & Poor’s 500 Index (S&P 500 index)

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Summary

Introduction

Stock market investment is one of the most significant parts of the nations’ economy. Crestmont Research showed the historical relationship between stock market performance and market volatility (Ang and Liu 2007). For this investigation, the average range for each day was used to measure the volatility of Standard & Poor’s 500 Index (S&P 500 index). Glosten et al (1993) investigated how volatility affects the risk premium of stocks and specified an adjusted GARCH-M model (Ang and Liu 2007). We tried to investigate the distribution analysis with the real returns of the stock prices and to find which distribution is better for forecasting an upcoming crisis period

Materials and Methods
Logarithmic Return
Realized Volatility
Beta Coefficient
Raw Stock Correlations
Descriptive Statistics
Conclusions
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