The Information Content of Option-Based Forecasts of Volatility: Evidence from the Italian Stock Market

2013 ◽  
Vol 03 (01) ◽  
pp. 1350005 ◽  
Author(s):  
Silvia Muzzioli
Keyword(s):  
Stock Market ◽  
Value At Risk ◽  
Implied Volatility ◽  
Practical Importance ◽  
Trading Strategies ◽  
Market Volatility ◽  
Future Market ◽  
Market Returns ◽  
Policy Makers ◽  
Volatility Index

The aim of this paper is to comprehensively compare option-based measures of volatility, with the ultimate plan of devising a new volatility index for the Italian stock market. The performance of the different implied volatility measures in forecasting future volatility is evaluated both in a statistical and in an economic setting. The properties of the implied volatility measures are also explored, by looking at both the contemporaneous relationship between implied volatility changes and market returns and the usefulness of the proposed index in forecasting future market returns. The results of the paper are of practical importance for both policy-makers and investors. The volatility index, based on corridor measures, could be used to forecast market volatility, for value at risk purposes, in order to determine trading strategies on the underlying index and as an early warning for future market conditions.

scholarly journals Opportunity or Crisis? Volatility and Leverage Effect in the World’s Largest Economies

2019 ◽  
Vol 9 (4) ◽  
pp. 1
Author(s):  
Amalendu Bhunia ◽  
Devrim Yaman
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Market Volatility ◽  
Future Market ◽  
Market Returns ◽  
Stock Market Returns ◽  
The Us ◽  
The World ◽  
The Impact ◽  
The Relationship

This paper examines the relationship between asset volatility and leverage for the three largest economies (based on purchasing power parity) in the world; US, China, and India. Collectively, these economies represent Int$56,269 billion of economic power, making it important to understand the relationship among these economies that provide valuable investment opportunities for investors. We focus on a volatile period in economic history starting in 1997 when the Asian financial crisis began. Using autoregressive models, we find that Chinese stock markets have the highest volatility among the three stock markets while the US stock market has the highest average returns. The Chinese market is less efficient than the US and Indian stock markets since the impact of new information takes longer to be reflected in stock prices. Our results show that the unconditional correlation among these stock markets is significant and positive although the correlation values are low in magnitude. We also find that past market volatility is a good indicator of future market volatility in our sample. The results show that positive stock market returns result in lower volatility compared to negative stock market returns. These results demonstrate that the largest economies of the world are highly integrated and investors should consider volatility and leverage besides returns when investing in these countries.

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 The Forecasting Power of the Volatility Index: Evidence from the Indian Stock Market

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Surya Bahadur G. C. ◽  
Ranjana Kothari
Keyword(s):  
Stock Market ◽  
Implied Volatility ◽  
Market Volatility ◽  
Series Data ◽  
Future Price ◽  
Stock Market Volatility ◽  
Short Term ◽  
Volatility Index ◽  
India Vix ◽  
Indian Stock Market

<div><p><em>Stock market volatility is a measure of risk in investment and it plays a key role in securities pricing and risk management. </em><em>The paper empirically analyzes the relationship between India VIX and volatility in Indian stock market. </em><em>India VIX is a measure of implied volatility which reflects markets’ expectation of future short-term stock market volatility.</em><em> It is a volatility index based on the index option prices of Nifty. </em><em>The study is based on time series data comprising of daily closing values of CNX Nifty 50 index comprising of 1656 observations from March 2009 to December 2015. </em><em>The results of the study </em><em>reveal that India VIX has predictive power for future short-term stock market volatility. It has higher forecasting ability for upward stock market movements as compared to downward movements. Therefore, it is more a bullish indicator. Moreover, the accuracy of forecasts provided by India VIX is higher for low magnitude future price changes relative to higher stock price movements. The current value of India VIX is found to be affected by past period volatility up to one month and it has forecasting ability for next one-month’s volatility which means the volatility in the Indian stock markets can be forecasted for up to 60 days period. </em></p></div>

scholarly journals The Impact of COVID-19 Pandemic on the Relationship between India’s Volatility Index and Nifty 50 Returns

2020 ◽  
Vol 4 (2) ◽  
pp. 58-63
Author(s):  
Jyothi Chittineni
Keyword(s):  
Implied Volatility ◽  
Emerging Market ◽  
Market Price ◽  
Market Volatility ◽  
Future Market ◽  
Trading Behavior ◽  
Volatility Index ◽  
Study Results ◽  
The Impact ◽  
The Relationship

The paper intends to re-examine the relationship between India’s Implied Volatility Index (IVIX) and Nifty 50 Returns during this COVID-19 pandemic. The study results are important for two reasons, one is to understand whether Indian VIX is fulfilling the purpose of measuring the near future volatility of Nifty 50 during this pandemic, and secondly, it reports the impact of COVID-19 on the investors’ perceptions about the returns and its volatility. The study results documented that the Nifty return and IVIX are moving independently during the COVID-19 pandemic and there is no association between market size and the market move. The one period lagged Nifty returns have a significant influence on the future market volatility. The combined impact of negative and positive Nifty returns on IVIX is not significant during the COVID-19 period. This implies that the Indian investors are not much worried about the fluctuation in the market price or size of the market during the COVID-19 pandemic period. The investors might be taking the market decline as an opportunity to invest and market rise as an opportunity to sell the stocks. Indian investors are much focused on the fundamentals than the market movements during this pandemic. The study results are important for the fund managers, policymakers, and analysts to understand the dynamics of emerging market volatility and the trading behavior of Indian investors.

Relationship between Foreign Portfolio Investments (FPI), Domestic Institutional Investors, and Stock Market Returns in India

GIS Business ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. 1-9
Author(s):  
Dhananjaya Kadanda ◽  
Krishna Raj
Keyword(s):  
Stock Market ◽  
Institutional Investors ◽  
Negative Relationship ◽  
Trading Strategies ◽  
Market Returns ◽  
Stock Market Returns ◽  
Foreign Portfolio Investment ◽  
Stock Market Return ◽  
Indian Stock Market ◽  
Feedback Trading

The present article attempts to understand the relationship between foreign portfolio investment (FPI), domestic institutional investors (DIIs), and stock market returns in India using high frequency data. The study analyses the trading strategies of FPIs, DIIs and its impact on the stock market return. We found that the trading strategies of FIIs and DIIs differ in Indian stock market. While FIIs follow positive feedback trading strategy, DIIs pursue the strategy of negative feedback trading which was more pronounced during the crisis. Further, there is negative relationship between FPI flows and DII flows. The results indicate the importance of developing strong domestic institutional investors to counteract the destabilising nature FIIs, particularly during turbulent times.

Effects of Daylight-Saving Time Changes on Stock Market Returns and Stock Market Volatility: Rebuttal

2013 ◽  
Vol 112 (1) ◽  
pp. 89-99 ◽  
Author(s):  
Mark J. Kamstra ◽  
Lisa A. Kramer ◽  
Maurice D. Levi
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
Daylight Saving Time ◽  
Daylight Saving ◽  
Time Changes ◽  
Saving Effect

In a 2011 reply to our 2010 comment in this journal, Berument and Dogen maintained their challenge to the existence of the negative daylight-saving effect in stock returns reported by Kamstra, Kramer, and Levi in 2000. Unfortunately, in their reply, Berument and Dogen ignored all of the points raised in the comment, failing even to cite the Kamstra, et al. comment. Berument and Dogen continued to use inappropriate estimation techniques, over-parameterized models, and low-power tests and perhaps most surprisingly even failed to replicate results they themselves reported in their previous paper, written by Berument, Dogen, and Onar in 2010. The findings reported by Berument and Dogen, as well as by Berument, Dogen, and Onar, are neither well-supported nor well-reasoned. We maintain our original objections to their analysis, highlight new serious empirical and theoretical problems, and emphasize that there remains statistically significant evidence of an economically large negative daylight-saving effect in U.S. stock returns. The issues raised in this rebuttal extend beyond the daylight-saving effect itself, touching on methodological points that arise more generally when deciding how to model financial returns data.

scholarly journals Efficiency of Indian Option Market: Estimation of Future Market Volatility Using Implied Volatility

2019 ◽  
Vol 10 (1) ◽  
pp. 11-20 ◽  
Author(s):  
T. Viswanathan ◽  
R. Sriram ◽  
Prarthana Mukherjee ◽  
◽  
◽  
...  
Keyword(s):  
Implied Volatility ◽  
Market Volatility ◽  
Future Market ◽  
Option Market
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