RATIONAL BUBBLES IN THE STOCK MARKET: ACCOUNTING FOR THE U.S. STOCK-PRICE VOLATILITY

1997 ◽  
Vol 35 (2) ◽  
pp. 309-319 ◽  
Author(s):  
WU YANGRU
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Price Volatility ◽  
Stock Price Volatility ◽  
Rational Bubbles ◽  
The U.S

scholarly journals The Task Force Report: The Reasoning Behind the Recommendations

1988 ◽  
Vol 2 (3) ◽  
pp. 3-23 ◽  
Author(s):  
Bruce Greenwald ◽  
Jeremy Stein
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Task Force ◽  
Price Volatility ◽  
Market Mechanisms ◽  
Task Force Report ◽  
Stock Market Crash ◽  
Stock Price Volatility ◽  
Princeton University

This paper was prepared for the Symposium on the [October 1987] Stock Market Crash, held February 8, 1988, at Princeton University. The article provides a framework for thinking about the recommendations made by the Presidential Task Force on Market Mechanisms. Three conclusions can be drawn from the Task Force's findings: First, the proper focus of analysis of the events of the October crash should be on “market mechanisms” rather than on fundamental imbalances in the economy as a whole. Second, the instability evident in the events of October 1987 was not the inexorable limit of a steadily increasing level of day-to-day stock price volatility. Third, under the sorts of conditions that prevailed on late Monday and Tuesday, an orderly halt to trading (and subsequent orderly reopening) would have been preferable to what actually took place. We describe how the data collected by the Task Force leads us to these three broad conclusions.

scholarly journals Stock market volatility and it's determinants in Pakistan stock market.

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Sumaira Tufail ◽  
Keyword(s):  
Foreign Direct Investment ◽  
Stock Market ◽  
Foreign Exchange ◽  
Stock Price ◽  
Direct Investment ◽  
Dividend Policy ◽  
Price Volatility ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Stock Price Volatility

Stock price volatility is considered as one of the most important areas of concern for the capital markets regulators, investors and academicians in recent years. Corporate dividend policy as a determinant of stock market volatility is a significant area of concern for the investors as well as the managers of the company due to political instability and the current economic crisis in Pakistan. This study aims at determining the effect of significant factors such as dividend yield, dividend payout ratio, foreign exchange rate and foreign direct investment on stock price fluctuation in Pakistan, which contributes to overall variation in stock price volatility. The study used a sample of 200 Pakistani listed companies by employing the regression analysis. The endogeneity issues were addressed through the generalized method of moments (GMM) estimation. The study concludes that stock price volatility has a negative association with dividend policy. The study also suggests that foreign direct investment and foreign exchange rates both negatively influence the stock price fluctuations in emerging markets. The findings of this study provide practical implications for the investors, policymakers and firm managers.

scholarly journals Price clusters and stock price stability

2020 ◽  
Vol 24 ◽  
pp. 33
Author(s):  
Hamid Reza Kordlouie ◽  
Mehrnoush Ebrahimi ◽  
Narges Mohseni Dehkolani ◽  
Azam Zare Jafar Kolaei
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Traditional Approach ◽  
Price Volatility ◽  
Price Stability ◽  
Stock Price Volatility ◽  
Factors Affecting ◽  
Price Increases ◽  
Research Findings ◽  
Clustering Test

Understanding the factors affecting stock return volatility, for analysts, investors and company executives, it is critical. In this study, using a traditional approach, we identify the factors influencing volatility and how price friction is formed on stock price stability, and in particular, examining the clustering test for price increases. This study was carried out by examining the price clusters and stock price stability in the stock market and the OTC market between 2009 -2010. Econometric software was used to investigate the research variables. In this study, we tried to study stock price volatility in proportion to stock price clusters. Research findings showed; there is no significant relationship between stock price volatility and price clusters in the OTC market and the stock market.

scholarly journals LITHUANIAN STOCK MARKET ANALYSIS USING A SET OF GARCH MODELS

2009 ◽  
Vol 10 (4) ◽  
pp. 349-360 ◽  
Author(s):  
Deimantė Teresienė
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Main Idea ◽  
Price Volatility ◽  
Market Analysis ◽  
Garch Models ◽  
Stock Price Volatility ◽  
Stage System ◽  
Main Factor

This article analyses the main factors that influence stock price volatility. The author offers a three‐stage system for explaning a set of stock price volatility factors. The main point is to pay attention to investor's psychology as the main factor of price volatility. For practical analysis the returns of the OMXV index and stock prices of the Lithuanian stock market are taken and applied to a set of GARCH models. The main idea is to choose the best of the general autoregressive conditional heteroskedasticity models (GARCH) for OMXV index and all sectors. All models are ranged according to their ability to model stock price return. The main tendencies of the Lithuanian stock market are also analysed in this article by highlighting the leverage effect.

scholarly journals Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1595
Author(s):  
Nagaraj Naik ◽  
Biju R. Mohan
Keyword(s):  
Stock Market ◽  
Regime Switching ◽  
Stock Price ◽  
Price Volatility ◽  
Information Criteria ◽  
Percentage Error ◽  
Stock Price Volatility ◽  
Volatility Estimation ◽  
Stock Traders ◽  
Indian Stock Market

Volatility is the degree of variation in the stock price over time. The stock price is volatile due to many factors, such as demand, supply, economic policy, and company earnings. Investing in a volatile market is riskier for stock traders. Most of the existing work considered Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) models to capture volatility, but this model fails to capture when the volatility is very high. This paper aims to estimate the stock price volatility using the Markov regime-switching GARCH (MSGARCH) and SETAR model. The model selection was carried out using the Akaike-Informations-Criteria (AIC) and Bayesian-Information Criteria (BIC) metric. The performance of the model is evaluated using the Root mean square error (RMSE) and mean absolute percentage error (MAPE) metric. We have found that volatility estimation using the MSGARCH model performed better than the SETAR model. The experiments considered the Indian stock market data.

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