scholarly journals A Study Of U.S. Stock Market Volatility, Fall 2008

2011 ◽  
Vol 7 (11) ◽  
Author(s):  
Ron Christner
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Life Span ◽  
Relative Magnitude ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Font Size ◽  
Bear Market ◽  
Stock Indexes ◽  
The U.S

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; color: black;"><span style="font-size: x-small;">This is a market volatility study utilizing three measures of assessing volatility in the U.S stock markets prior to and after the month of September 2008 using three proxies. The first is the VIX index, the CBOE options volatility measure. The next two are bearish, or short position strategy, ETF&rsquo;s based on stock indexes but designed to reflect and benefit from stock market movements in the downward direction. They are the Power Shares index, symbol SDS, and the Rydex Index, symbol RMS. This research evaluates and analyzes weekly movements in the three volatility variables mentioned above for a period of the last eight months of 2008. This includes the four months prior to and the four months after the beginning of September 2008. Specifically, the relative magnitude, volatility and degree of correlation between the three variables will be examined and compared to the movements in NYSE, NASDAQ and S &amp; P stock indexes. The life span and volume of trading, one measure of liquidity, in each of the three variables will also be evaluated. Part of the analysis, and conclusions, will involve analyzing how similar or dissimilar the three behave and whether one may be a better indicator of current<span style="mso-spacerun: yes;">&nbsp; </span>or future volatility in the stock market, or financial markets in general and how effective the bear market ETF&rsquo;s might be as hedging vehicles in a down market.</span></span></p>

scholarly journals Financial contagion: The impact of the volatility of global stock exchanges on the Lima-Peru Stock Exchange

2020 ◽  
Vol 1 (1) ◽  
pp. 13-27
Author(s):  
Pedro Pablo Chambi Condori
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Stock Exchange ◽  
Market Volatility ◽  
Financial Contagion ◽  
Stock Market Volatility ◽  
Important Indicator ◽  
Dynamic Correlation ◽  
High Volatility ◽  
The Impact

What happens in the international financial markets in terms of volatility, have an impact on the results of the local stock market financial markets, as a result of the spread and transmission of larger stock market volatility to smaller markets such as the Peruvian, assertion that goes in accordance with the results obtained in the study in reference. The statistical evaluation of econometric models, suggest that the model obtained can be used for forecasting volatility expected in the very short term, very important estimates for agents involved, because these models can contribute to properly align the attitude to be adopted in certain circumstances of high volatility, for example in the input, output, refuge or permanence in the markets and also in the selection of best steps and in the structuring of the portfolio of investment with equity and additionally you can view through the correlation on which markets is can or not act and consequently the best results of profitability in the equity markets. This work comprises four well-defined sections; a brief history of the financial volatility of the last 15 years, a tight summary of the background and a dense summary of the methodology used in the process of the study, exposure of the results obtained and the declaration of the main conclusions which led us mention research, which allows writing, evidence of transmission and spread of the larger stock markets toward the Peruvian stock market volatility, as in the case of the American market to the market Peruvian stock market with the coefficient of dynamic correlation of 0.32, followed by the Spanish market and the market of China. Additionally, the coefficient of interrelation found by means of the model dcc mgarch is a very important indicator in the structure of portfolios of investment with instruments that they quote on the financial global markets.

Risk and Ambiguity in Turbulent Times

2021 ◽  
Author(s):  
Menachem Brenner ◽  
Yehuda Izhakian
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Long Run ◽  
Turbulent Times ◽  
Volatility Derivatives ◽  
History Of ◽  
Tail Events ◽  
Derivatives Markets

This paper focuses on the 2008–2020 period during which two major crises, affecting the economy and the financial markets, occurred. Between 2008 and 2020, there were less extreme tail events, including the lingering Eurozone and Greece crises. In particular, after extremely high stock market volatility and volatility of volatility (VoV) during 2008, the long-run average volatility declined to about 20% and the VoV to around 100%. This paper analyzes this period through the lens of risk and ambiguity (uncertainty). It aims to address the question: what are the financial markets that trade risk — the volatility derivatives markets — telling us? To this end, this paper uses several measures of uncertainty. It reviews the history of volatility and uncertainty measures and discusses their informativeness. It then discusses the information derived from volatility derivatives.

scholarly journals The U.S. Presidential Election 2012/2016 and Investors’ Sentiment: The Case of CBOE Market Volatility Index

SAGE Open ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 215824401986417
Author(s):  
Imlak Shaikh
Keyword(s):  
Stock Market ◽  
Presidential Election ◽  
Presidential Elections ◽  
Implied Volatility ◽  
Substantial Effect ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Volatility Index ◽  
Stock Market Performance ◽  
The U.S

Given that political events have substantial effect on new economic policies and economic performance of the country, this article aims to examine the behavior of the investors’ sentiment in terms of implied volatility index trailed by the U.S. presidential elections. The study empirically tests whether the presidential elections in 2012/2016 do contain the important market inclusive information to explain the expected stock market volatility. The findings indicate that investors’ concern was distracted around the presidential elections window, albeit the market performed identically in both the presidential election years. The significant fall in the implied volatility level (post-election period) is the calm before the storm, just wait and watch. The positive estimate uncovers the fact that investor worries were higher before the election day. In particular, the significant estimate of the presidential election debate shows that investors do regard the minutes of the presidential election debates in their portfolio selection. At the two elections era, on the candidacy of both the parties, the empirical result speaks marginally contrasting outcomes and falsifies the presidential election cycle hypothesis of past 29 U.S. election years. Empirical estimates conclude that the presidential elections in 2012/2016 have a strong, significant relationship with investor’s sentiment and stock market performance.

scholarly journals Stock Market Volatility under Sanctions

2014 ◽  
Vol 3 (4) ◽  
pp. 234-249
Author(s):  
Hojatallah Goudarzi
Keyword(s):  
Stock Market ◽  
Nuclear Energy ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Fat Tails ◽  
Arch Models ◽  
Volatility Clustering ◽  
Market Growth ◽  
Congressional Research ◽  
The U.S

Since 1979, Iran has faced with unilateral and multilateral harsh sanctions due to its nuclear energy program. These sanctions have resulted in significant problem to both sanctioned and sanctioning parties. Given the fact that sanctions have had significant impacts on Iran’s economy and since Iran stock market is the barometer of its economy, it is assumed that sanctions affect the Iranian stock market as well. To test this hypothesis, this study studied the Iranian stock market volatility during harsh sanctions using ARCH models. The study found that, despite all sanctions, not only Iran’s stock market shows major stylized facts of any stock market’s volatility i.e. volatility clustering, fat tails and mean reversion but also it shows no irregularity which could be attributed to effect of sanctions. This finding was consistent with Iranian stock market regulators claiming Iranian stock market growth and the U.S. Congressional Research Service report 2013. Therefore, based on findings, this study concluded that Iranian stock market has not affected by sanctions.

Estimating Emerging Stock Market Volatility Using Garch Family Models

2011 ◽  
Vol 3 (9) ◽  
pp. 331-333 ◽  
Author(s):  
Ramona Birău ◽  
◽  
Jatin Trivedi
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Family Models
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