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

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 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.

scholarly journals Agricultural Commodity Trading: Is it Destabilizing Spot Markets?

2018 ◽  
Vol 43 (1) ◽  
pp. 47-57 ◽  
Author(s):  
C. P. Gupta ◽  
Sanjay Sehgal ◽  
Sahaj Wadhwa
Keyword(s):  
Causal Relationship ◽  
Trading Volume ◽  
Spot Market ◽  
Market Volatility ◽  
Future Market ◽  
Soy Oil ◽  
Open Interest ◽  
Future Trading ◽  
The Future ◽  
Spot Volatility

Executive Summary The future trading has been held responsible by certain political and interest groups of enhancing speculative trading activities and causing volatility in the spot market, thereby further spiralling up inflation. This study examines the effect of future of trading activity on spot market volatility. The study first determined the Granger causal relationship between unexpected future trading volume and spot market volatility. It then examined the Granger causal relationship between unexpected open interest and spot market volatility. The spot volatility and liquidity was modelled using EGARCH and unexpected trading volume. The expected trading volume and open interest was calculated by using the 21-day moving average, and the difference between actual and expected component was treated as the unexpected trading volume and unexpected open interest. Empirical results confirm that for chickpeas ( channa), cluster bean ( guar seed), pepper, refined soy oil, and wheat, the future (unexpected) liquidity leads spot market volatility. The causal relationship implies that trading volume, which is a proxy for speculators and day traders, is dominant in the future market and leads volatility in the spot market. The results are in conformity with earlier empirical findings — Yang, Balyeat and Leathan (2005) and Nath and Lingareddy (2008) —that future trading destabilizes the spot market for agricultural commodities. Results show that there is no causal relationship between future open interest and spot volatility for all commodities except refined soy oil and wheat. The findings imply that open interest, which is a proxy of hedging activity, is leading to volatility in spot market for refined soy oil and wheat. The results are in conformity to earlier empirical studies that there is a weak causal feedback between future unexpected open interest and volatility in spot market ( Yang et al., 2005 ). For chickpeas (channa), the increase in volatility in the spot market increases trading activity in the future market. The findings are contrary to earlier empirical evidence ( Chatrath, Ramchander, & Song, 1996 ; Yang et al., 2005 ) that increase in spot volatility reduces future trading activity. However, they are in conformity to Chen, Cuny and Haugen (1995) that increase in spot volatility increases future open interest. The results reveal that the future market has been unable to engage sufficient hedging activity. Thereby, a causal relationship exists only for future trading volume and spot volatility, and not for future open interest and spot volatility. The results have major implications for policymakers, investment managers, and for researchers as well. The study contributes to literature on price discovery, spillovers, and price destabilization for Indian commodity markets.

Implied volatility smirk and future stock returns: evidence from the German market

2015 ◽  
Vol 41 (12) ◽  
pp. 1357-1379
Author(s):  
Di Mo ◽  
Neda Todorova ◽  
Rakesh Gupta
Keyword(s):  
High Frequency ◽  
Implied Volatility ◽  
Stock Index ◽  
High Frequency Data ◽  
Frequency Data ◽  
European Options ◽  
German Market ◽  
Content Type ◽  
The Relationship ◽  
Option Market

Purpose – The purpose of this paper is to investigate the relationship between option’s implied volatility smirk (IVS) and excess returns in the Germany’s leading stock index Deutscher-Aktien Index (DAX) 30. Design/methodology/approach – The study defines the IVS as the difference in implied volatility derived from out-of-the-money put options and at-the-money call options. This study employs the ordinary least square regression with Newey-West correction to analyse the relationship between IVS and excess DAX 30 index returns in Germany. Findings – The authors find that the German market adjusts information in an efficient way. Consequently, there is no information linkage between option volatility smirk and market index returns over the nine years sample period after considering the control variables, global financial crisis dummies, and the subsample test. Research limitations/implications – This study finds that the option market and the DAX 30 index are informationally efficient. Implications of the findings are that the investors cannot profit from the information contained in the IVS since the information is simultaneously incorporated into option prices and the stock index prices. The findings of this study are applicable to other markets with European options and for market participants who seek to exploit short-term market divergence from efficiency. Originality/value – The relationship between IVS and stock price changes has not been investigated sufficiently in academic literature. This study looks at this relationship in the context of European options using high-frequency transactions data. Prior studies look at this relationship for only American options using daily data. Pricing efficiency of the European option market using high-frequency data have not been studied in the prior literature. The authors find different results for the German market based on this high-frequency data set.

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.

Implied Volatility of the KOSPI 200 Index Option Market

2015 ◽  
Vol 23 (4) ◽  
pp. 517-541
Author(s):  
Dam Cho
Keyword(s):  
Financial Crisis ◽  
Trading Volume ◽  
Implied Volatility ◽  
Spot Price ◽  
Strike Price ◽  
Market Pricing ◽  
Put Options ◽  
The Difference ◽  
Index Option ◽  
Option Market

This paper analyzes implied volatilities (IVs), which are computed from trading records of the KOSPI 200 index option market from January 2005 to December 2014, to examine major characteristics of the market pricing behavior. The data includes only daily closing prices of option transactions for which the daily trading volume is larger than 300 contracts. The IV is computed using the Black-Scholes option pricing model. The empirical findings are as follows; Firstly, daily averages of IVs have shown very similar behavior to historical volatilities computed from 60-day returns of the KOSPI 200 index. The correlation coefficient of IV of the ATM call options to historical volatility is 0.8679 and that of the ATM put options is 0.8479. Secondly, when moneyness, which is measured by the ratio of the strike price to the spot price, is very large or very small, IVs of call and put options decrease days to maturity gets longer. This is partial evidence of the jump risk inherent in the stochastic process of the spot price. Thirdly, the moneyness pattern showed heavily skewed shapes of volatility smiles, which was more apparent during the global financial crises period from 2007 to 2009. Behavioral reasons can explain the volatility smiles. When the moneyness is very small, the deep OTM puts are priced relatively higher due to investors’ crash phobia and the deep ITM calls are valued higher due to investors’ overconfidence and confirmation biases. When the moneyness is very large, the deep OTM calls are priced higher due to investors’ hike expectation and the deep ITM puts are valued higher due to overconfidence and confirmation biases. Fourthly, for almost all moneyness classes and for all sub-periods, the IVs of puts are larger than the IVs of calls. Also, the differences of IVs of deep OTM put ranges minus IVs of deep OTM calls, which is known to be a measure of crash phobia or hike expectation, shows consistent positive values for all sub-periods. The difference in the financial crisis period is much bigger than in other periods. This suggests that option traders had a stronger crash phobia in the financial crisis.

Actuaries and Derivatives

1997 ◽  
Vol 3 (1) ◽  
pp. 51-180 ◽  
Author(s):  
M.H.D. Kemp
Keyword(s):  
Transaction Costs ◽  
Market Volatility ◽  
Future Market ◽  
Equity Derivatives

ABSTRACTThis paper draws analogies between techniques used to reserve for, control and manage derivatives and techniques used by actuaries in other fields. It concentrates on equity derivatives. It also includes a review of the factors which significantly influence the appropriate size of reserves to hold for a derivatives portfolio. These include the likelihood of market jumps, uncertainty in future market volatility and the size of transaction costs, as well as on more obvious factors like position risk.

scholarly journals Option pricing with random risk aversion

2022 ◽  
Author(s):  
Luiz Vitiello ◽  
Ser-Huang Poon
Keyword(s):  
Risk Aversion ◽  
Implied Volatility ◽  
Market Volatility ◽  
European Options ◽  
Parameter Change ◽  
Pricing Kernel ◽  
Underlying Asset ◽  
State Dependent ◽  
Low Levels ◽  
Aggregate Wealth

AbstractBased on a standard general equilibrium economy, we develop a framework for pricing European options where the risk aversion parameter is state dependent, and aggregate wealth and the underlying asset have a bivariate transformed-normal distribution. Our results show that the volatility and the skewness of the risk aversion parameter change the slope of the pricing kernel, and that, as the volatility of the risk aversion parameter increases, the (Black and Scholes) implied volatility shifts upwards but its shape remains the same, which implies that the volatility of the risk aversion parameter does not change the shape of the risk neutral distribution. Also, we demonstrate that the pricing kernel may become non-monotonic for high levels of volatility and low levels of skewness of the risk aversion parameter. An empirical example shows that the estimated volatility of the risk aversion parameter tends to be low in periods of high market volatility and vice-versa.

scholarly journals Uncertainty Shocks as Second-Moment News Shocks

2019 ◽  
Vol 87 (1) ◽  
pp. 40-76 ◽  
Author(s):  
David Berger ◽  
Ian Dew-Becker ◽  
Stefano Giglio
Keyword(s):  
Stock Market ◽  
Implied Volatility ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
News Shocks ◽  
Second Moment ◽  
Uncertainty Shocks ◽  
Aggregate Uncertainty ◽  
The Future ◽  
The Relationship

AbstractWe provide evidence on the relationship between aggregate uncertainty and the macroeconomy. Identifying uncertainty shocks using methods from the news shocks literature, the analysis finds that innovations in realized stock market volatility are robustly followed by contractions, while shocks to forward-looking uncertainty have no significant effect on the economy. Moreover, investors have historically paid large premia to hedge shocks to realized but not implied volatility. A model in which fundamental shocks are skewed left can match those facts. Aggregate volatility matters, but it is the realization of volatility, rather than uncertainty about the future, that has been associated with declines.

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