Relevant bibliographies by topics / The CBOE Volatility Index

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'The CBOE Volatility Index'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "The CBOE Volatility Index"

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Mariničevaitė, Tamara, and Jovita Ražauskaitė. "The Relevance of Cboe Volatility Index to Stock Markets in Emerging Economies." Organizations and Markets in Emerging Economies 6, no. 1 (May 29, 2015): 93–106. http://dx.doi.org/10.15388/omee.2015.6.1.14229.

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We examine the capability of CBOE S&P500 Volatility index (VIX) to determine returns of emerging stock market indices as compared to local stock markets volatility indicators. Our study considers CBOE S&P500 VIX, local BRIC stock market volatility indices and BRIC stock market MSCI indices daily returns in the period from January 1, 2009 to September 30, 2014. Research is conducted in two steps. First, we perform Spearman correlation analysis between daily changes in CBOE S&P500 VIX, local BRIC stock market VIX and MSCI BRIC stock market indices returns. Second, we perform multiple regression analysis with ARCH effects to estimate the relevance of CBOE S&P500 VIX and local VIX in determining BRIC stock market returns. Research reports weak correlation between CBOE S&P500 VIX and local VIX (except for Brazil). Furthermore, results challenge the assumption of CBOE S&P500 VIX being an indicator of global risk aversion. We conclude that commonly documented trends of rising globalization and stock markets co-integration are not yet present in emerging economies, therefore the usage of CBOE S&P500 VIX alone in determining BRIC stock market returns should be considered cautiously, and local volatility indices should be accounted for in analysis. Furthermore, the data confirms the presence of safe haven properties in Chinese stock market index.
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Fernandes, Marcelo, Marcelo C. Medeiros, and Marcel Scharth. "Modeling and predicting the CBOE market volatility index." Journal of Banking & Finance 40 (March 2014): 1–10. http://dx.doi.org/10.1016/j.jbankfin.2013.11.004.

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Chen, Hongtao, Li Liu, and Xiaolei Li. "The predictive content of CBOE crude oil volatility index." Physica A: Statistical Mechanics and its Applications 492 (February 2018): 837–50. http://dx.doi.org/10.1016/j.physa.2017.11.014.

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Hu, Wenbin. "Volatility Forecasting of China Silver Futures: the Contributions of Chinese Investor Sentiment and CBOE Gold and Silver ETF Volatility Indices." E3S Web of Conferences 253 (2021): 02023. http://dx.doi.org/10.1051/e3sconf/202125302023.

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This paper is to detect the role of CBOE gold ETF volatility index (GVZ), CBOE silver ETF volatility index (VXSLV), and constructed Chinese investor sentiment (CnSENT) on the volatility forecasting of China silver futures over daily, weekly and monthly horizons. Different types of HAR models and ridge regression models are utilized to do the analysis, and the out-of-sample R-square statistics and different rolling window sizes are used to ensure the robustness of the conclusion. The empirical results suggest that GVZ and VXSLV have the explanatory power on the China silver futures. Particularly, VXSLV has a better performance than GVZ. However, the predictive power of CnSENT is doubtful as some results indicate that it cannot improve the prediction accuracy. Additionally, the ridge regression method does not achieve a better result than all types of HAR models.
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FUKASAWA, M., I. ISHIDA, N. MAGHREBI, K. OYA, M. UBUKATA, and K. YAMAZAKI. "MODEL-FREE IMPLIED VOLATILITY: FROM SURFACE TO INDEX." International Journal of Theoretical and Applied Finance 14, no. 04 (June 2011): 433–63. http://dx.doi.org/10.1142/s0219024911006681.

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We propose a new method for approximating the expected quadratic variation of an asset based on its option prices. The quadratic variation of an asset price is often regarded as a measure of its volatility, and its expected value under pricing measure can be understood as the market's expectation of future volatility. We utilize the relation between the asset variance and the Black-Scholes implied volatility surface, and discuss the merits of this new model-free approach compared to the CBOE procedure underlying the VIX index. The interpolation scheme for the volatility surface we introduce is designed to be consistent with arbitrage bounds. We show numerically under the Heston stochastic volatility model that this approach significantly reduces the approximation errors, and we further provide empirical evidence from the Nikkei 225 options that the new implied volatility index is more accurate in predicting future volatility.
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Cary, Dayne, Gary van Vuuren, and David McMillan. "Replicating the CBOE VIX using a synthetic volatility index trading algorithm." Cogent Economics & Finance 7, no. 1 (January 1, 2019): 1641063. http://dx.doi.org/10.1080/23322039.2019.1641063.

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OROSI, GREG. "A NOVEL METHOD FOR ARBITRAGE-FREE OPTION SURFACE CONSTRUCTION." Annals of Financial Economics 14, no. 04 (December 2019): 1950021. http://dx.doi.org/10.1142/s2010495219500210.

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In this paper, we provide an alternative framework for constructing an arbitrage-free European-style option surface. The main motivation for our work is that such a construction has rarely been achieved in the literature so far. The novelty of our approach is that we perform the calibration and interpolation in the put option space. To demonstrate the applicability of our technique, we extract the model-free implied volatility from S&P 500 index options. Subsequently, we compare its information content to that of the CBOE VIX index. Our empirical tests indicate that information content of the option-implied volatility values based on our method are superior to the VIX index.
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Min-Yuh Day, Min-Yuh Day, Paoyu Huang Min-Yuh Day, and Yensen Ni Paoyu Huang. "Does CBOE Volatility Index Jumped or Located at a Higher Level Matter for Evaluating DJ 30, NASDAQ, and S＆P500 Index Subsequent Performance." 電腦學刊 32, no. 4 (August 2021): 057–66. http://dx.doi.org/10.53106/199115992021083204005.

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Tsuji, Chikashi. "Does the CBOE Volatility Index Predict Downside Risk at the Tokyo Stock Exchange?" International Business Research 10, no. 3 (January 10, 2016): 1. http://dx.doi.org/10.5539/ibr.v10n3p1.

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This study investigates the predictability of the preceding day’s US volatility index (VIX) from the Chicago Board Options Exchange (CBOE) for sharp price drops of the Tokyo Stock Price Index (TOPIX) by employing several versions of probit models. All our results indicate that the preceding day’s US S&P 500 VIX movement has predictive power for sharp price declines of the TOPIX in Japan. As we repeatedly examined several left tail risks in TOPIX price changes and we also tested by applying some different versions of probit models, our evidence of the forecast power of the S&P 500 VIX for downside risk of the TOPIX shall be very robust.
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Dr. Avijit Sikdar. "Study of Association between Volatility Index and Nifty using VECM." International Journal of Engineering and Management Research 11, no. 1 (February 27, 2021): 200–204. http://dx.doi.org/10.31033/ijemr.11.1.27.

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Volatility in capital markets is the measure degree of variability of stock return from their expected return. The volatility in the capital market is the basis for price discovery in the financial asset. The volatility index (VIX) is the measurement index of the volatility of the capital market. It is the fear index of the capital market. The concept is first coined in 1993 in Chicago Board Options Exchange (CBOE). In India, such an index was introduced in 2008 by NSE. India VIX calculates the expected market volatility over the coming thirty days on Nifty Options. It Market index is the performance metric of the Indian capital market. This index is designed to reflect the overall market sentiments. An index is an important parameter to measure the performance of the economy as a whole. While the market index measures the direction of the market and is calculated by the price movements of the underlying stocks, the Volatility Index measures the volatility of the market and is calculated using the order book of the underlying index's options. In this study, we examine the association between India VIX and Nifty Index returns by using Johanson's co-integration, Vector Error Correction Model (VECM), and Granger causality Tools. The data for this study covers closing data of VIX value and Nifty closing value from January 2014 to December 2019 and has a total of 1474 daily observations. The result confirms that there are co-integrating relationships (long-run association) between VIX and Nifty. The Granger causality indicates Nifty does Granger Cause VIX but VIX does not granger Cause Nifty.
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Dissertations / Theses on the topic "The CBOE Volatility Index"

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Kozyreva, Maria. "How reliable is implied volatility A comparison between implied and actual volatility on an index at the Nordic Market." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1635.

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Volatility forecast plays a central role in the financial decision making process. An intrinsic purpose of any investor is profit earning. For that purpose investors need to estimate the risk. One of the most efficient

methods to this end is the volatility estimation. In this theses I compare the CBOE Volatility Index, (VIX) with the actual volatility on an index at the Nordic Market. The actual volatility is defined as the one-day-ahead prediction as calculated by using the GARCH(1,1) model. By using the VIX model I performed consecutive predictions 30 days ahead between February the 2nd, 2007 to March

the 6th, 2007. These predictions were compared with the GARCH(1,1) one-day-ahead predictions for the same period. To my knowledge, such comparisons have not been performed earlier on the Nordic Market. The conclusion of the study was that the VIX predictions tends to higher values then the GARCH(1,1) predictions except for large prices upward jumps, which indicates that the VIX is not able to predict future shocks.

Except from these jumps, the VIX more often shows larger value than the GARCH(1,1). This is interpreted as an uncertainly of the prediction. However, the VIX predictions follows the actual volatility reasonable

well. I conclude that the VIX estimation can be used as a reliable estimator of market volatility.

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Xin, Mao. "The VIX Volatility Index." Thesis, Uppsala universitet, Analys och tillämpad matematik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-153705.

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Olofsson, Isak. "@TheRealDonaldTrump’s tweets correlation with stock market volatility." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275683.

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The purpose of this study is to analyze if there is any tweet specific data posted by Donald Trump that has a correlation with the volatility of the stock market. If any details about the president Trump's tweets show correlation with the volatility, the goal is to find a subset of regressors with as high as possible predictability. The content of tweets is used as the base for regressors. The method which has been used is a multiple linear regression with tweet and volatility data ranging from 2010 until 2020. As a measure of volatility, the Cboe VIX has been used, and the regressors in the model have focused on the content of tweets posted by Trump using TF-IDF to evaluate the content of tweets. The results from the study imply that the chosen regressors display a small significant correlation of with an adjusted R2 = 0.4501 between Trump´s tweets and the market volatility. The findings Include 78 words with correlation to stock market volatility when part of President Trump's tweets. The stock market is a large and complex system of many unknowns, which aggravate the process of simplifying and quantifying data of only one source into a regression model with high predictability.
Syftet med denna studie är att analysera om det finns några specifika egenskaper i de tweets publicerade av Donald Trump som har en korrelation med volatiliteten på aktiemarknaden. Om egenskaper kring president Trumps tweets visar ett samband med volatiliteten är målet att hitta en delmängd av regressorer med för att beskriva sambandet med så hög signifikans som möjligt. Innehållet i tweets har varit i fokus använts som regressorer. Metoden som har använts är en multipel linjär regression med tweet och volatilitetsdata som sträcker sig från 2010 till 2020. Som ett mått på volatilitet har Cboe VIX använts, och regressorerna i modellen har fokuserat på innehållet i tweets där TF-IDF har använts för att transformera ord till numeriska värden. Resultaten från studien visar att de valda regressorerna uppvisar en liten men signifikant korrelation med en justerad R2 = 0,4501 mellan Trumps tweets och marknadens volatilitet. Resultaten inkluderar 78 ord som de när en är en del av president Trumps tweets visar en signifikant korrelation till volatiliteten på börsen. Börsen är ett stort och komplext system av många okända, som försvårar processen att förenkla och kvantifiera data från endast en källa till en regressionsmodell med hög förutsägbarhet.
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Vikberg, Sara, and Julia Björkman. "How Well Does Implied Volatility Predict Future Stock Index Returns and Volatility? : A Study of Option-Implied Volatility Derived from OMXS30 Index Options." Thesis, Stockholms universitet, Företagsekonomiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-187552.

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The purpose of this thesis is to study if and how well implied volatility can predict realised volatility and returns on the OMXS30 index one month in the future. The findings are put in relation to how historical volatility can predict realised volatility and how changes in implied volatility can predict returns. The study covers the time period from 10th of May 2012 to 9th of February 2020 and the implied volatility used in the study is derived from an unweighted average of OMXS30 call and put option implied volatility. Six different OLS-regressions are performed to study the prediction capability of implied volatility. This study finds support of implied volatility to be a statistically significant estimate for future realised returns in a univariate regression. However, our results show that historical volatility performs slightly better predictions of realised volatility than implied volatility. These are contradictory results to the majority of the papers studied in this thesis. These papers share the common notion that implied volatility is superior to historical volatility in predicting realised volatility. Further our results show that implied volatility nor change in implied volatility are significant estimates to future realised returns and perform poorly as predictors. This result is supported by the larger part of previous research, which found implied volatility to be a weak predictor of returns.
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Lu, Yu Hang. "Hedging and volatility of Hang Seng Index." Thesis, University of Macau, 2006. http://umaclib3.umac.mo/record=b1676381.

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Reuterhäll, Fredrik. "Forecast quality of the Swedish Volatility Index." Thesis, Stockholm University, School of Business, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-6007.

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In this paper, I investigate the forecasting power of implied volatility via a new volatility index for the Swedish stock market (SVIX). By implementing the same methodology as the new VIX index originated from CBOE, I examine the information content of implied volatility and appraise the forecast quality of SVIX using two methods. Firstly, I use option valuation to evaluate the information content of implied volatility. I use four different volatilities and the evidence is clear. Using historical volatility or lagged one day at-the-money implied volatility generates poor results. Evaluating the quality of the Swedish volatility index SVIX and the average between the implied volatility lagged one day of one at-the-money call and one at-the-money put option (AIV), the results are diverted and there is no clear evidence whether to use AIV or the SVIX. Secondly, I evaluate the forecasting performance of the GARCH (1,1) model, SVIX and the AIV. Evidence point in the directions that SVIX and AIV forecasts is of higher quality than the GARCH (1,1) model, which uses historical information to produce volatility forecasts.

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Blair, Bevan John. "Modelling Standard and Poors 100 index volatility." Thesis, Lancaster University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340564.

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Nilsson, Oscar, and Okumu Emmanuel Latim. "Does Implied Volatility Predict Realized Volatility? : An Examination of Market Expectations." Thesis, Uppsala universitet, Nationalekonomiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-218792.

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The informational content of implied volatility and its prediction power is evaluated for time horizons of one month. The study covers the period of November 2007 to November 2013 for the two indices S&P500 and OMXS30. The findings are put in relation to the corresponding results for past realized volatility. We find results supporting that implied volatility is an efficient, although biased estimator of realized volatility. Our results support the common notion that implied volatility predicts realized volatility better than past realized volatility, and that it also subsumes most of the informational content of past realized volatility.
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Pachentseva, Marina, and Anna Bronskaya. "On Stock Index Volatility With Respect to Capitalization." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1189.

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Condfidence in the future is a signicant factor for business development. However frequently, accurate and specific purposes are spread over the market environment influence.Thus,it is necessary to make an appropriate consideration of instability, which is peculiar to the dynamic development. Volatility, variance and standard deviation are used to

characterize the deviation of the investigated quantity from mean value.

Volatility is one of the main instruments to measure the risk of the asset.

The increasing availability of financial market data has enlarged volatility research potential but has also encouraged research into longer horizon volatility forecasts.

In this paper we investigate stock index volatility with respect to capitalization with help of GARCH-modelling.

There are chosen three indexes of OMX Nordic Exchange for our research. The Nordic list segment indexes comprising Nordic Large Cap,

Mid Cap and Small Cap are based on the three market capitalization groups.

We implement GARCH-modeling for considering indexes and compare our results in order to conclude which ones of the indexes is more volatile.

The OMX Nordic list indexis quiet new(2002)and reorganized as late as October 2006. The current value is now about 300 and no options do exist. In current work we are also interested in estimation of the Heston

model(SVmodel), which is popular in financial world and can be used in option pricing in the future.

The results of our investigations show that Large Cap Index is more volatile then Middle and Small Cap Indexes.

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Süss, Stephan. "Volatility indices and their derivatives /." [S.l.] : [s.n.], 2009. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=018685872&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Books on the topic "The CBOE Volatility Index"

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Hodgson, Allan. Volatility and index futures options. [s.l.]: [s.n.], 1988.

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Cassese, Gianluca. Modelling the MIB30 implied volatility surface: Does efficiency matter? [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2005.

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Gonçalves, Silva. Predictable dynamics in the S&P 500 index options implied volatility surface. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2005.

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Engle, R. F. Index-option pricing with stochastic volatility and the value of accurate variance forecasts. Cambridge, MA: National Bureau of Economic Research, 1993.

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Schwert, G. William. Stock volatility in the new millennium: How wacky is Nasdaq? Cambridge, MA: National Bureau of Economic Research, 2001.

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MacDonald, Ronald. Stock prices, dividends, efficiency and excessive volatility: Some evidence for the FT ordinary share index. Aberdeen: University of Aberdeen. Department of Economics, 1987.

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Thosar, Satish. Increased stock volatility and excess returns in the index futures trading era: New evidence from additions to the S&P500 index. Boston, MA: Boston University, School of Management, 1992.

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Augen, Jeffrey. The volatility edge in options trading: New technical strategies for trading equity and index options in unstable markets. Upper Saddle River, N.J: FT Press, 2008.

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Giblin, Paul R. The impact of volatility on the levels of basis, open interest and volume in the FTSE 100 index futures market. Dublin: Universitry College Dublin, 1995.

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United States. Congress. House. Committee on Agriculture. Subcommittee on Conservation, Credit, and Rural Development. Review of recent volatility in the stock market and the stock index futures markets: Hearing before the Subcommittee on Conservation, Credit, and Rural Development of the Committee on Agriculture, House of Representatives, One Hundredth Congress, first session, November 4, 1987. Washington: U.S. G.P.O., 1988.

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Book chapters on the topic "The CBOE Volatility Index"

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Dierckx, Thomas, Jesse Davis, and Wim Schoutens. "Quantifying News Narratives to Predict Movements in Market Risk." In Data Science for Economics and Finance, 265–85. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_12.

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AbstractThe theory of Narrative Economics suggests that narratives present in media influence market participants and drive economic events. In this chapter, we investigate how financial news narratives relate to movements in the CBOE Volatility Index. To this end, we first introduce an uncharted dataset where news articles are described by a set of financial keywords. We then perform topic modeling to extract news themes, comparing the canonical latent Dirichlet analysis to a technique combining doc2vec and Gaussian mixture models. Finally, using the state-of-the-art XGBoost (Extreme Gradient Boosted Trees) machine learning algorithm, we show that the obtained news features outperform a simple baseline when predicting CBOE Volatility Index movements on different time horizons.
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Auinger, Florian. "Financial Market Volatility." In The Causal Relationship between the S&P 500 and the VIX Index, 19–32. Wiesbaden: Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-08969-6_4.

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Roh, Tae Hyup. "Forecasting the Volatility of Stock Price Index." In Advanced Data Mining and Applications, 424–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_47.

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Hol, Eugenie M. J. H. "Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility." In Dynamic Modeling and Econometrics in Economics and Finance, 71–97. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-5129-1_6.

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Hol, Eugenie M. J. H. "Stock Index Volatility Forecasting with High Frequency Data." In Dynamic Modeling and Econometrics in Economics and Finance, 99–127. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-5129-1_7.

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Thomakos, Dimitrios D., and Tao Wang. "Volatility Timing and Portfolio Construction Using Realized Volatility for the S&P500 Futures Index." In Handbook of Portfolio Construction, 711–32. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-77439-8_28.

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Yue, Zhenzhen. "Correlation Analysis for CSI300 Index Return and Realized Volatility." In Advances in Intelligent Systems and Computing, 131–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9330-3_12.

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Yu, ShuiLing, and Zhe Li. "Forecasting Stock Price Index Volatility with LSTM Deep Neural Network." In Recent Developments in Data Science and Business Analytics, 265–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72745-5_29.

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Maréchal, Frédéric, Daniel Stamate, Rapheal Olaniyan, and Jiri Marek. "On XLE Index Constituents’ Social Media Based Sentiment Informing the Index Trend and Volatility Prediction." In Computational Collective Intelligence, 366–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98446-9_34.

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Floros, Christas, and Dimitrios V. Vougas. "Index Futures Trading, Information and Stock Market Volatility: The Case of Greece." In Derivatives and Hedge Funds, 118–39. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/9781137554178_6.

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Conference papers on the topic "The CBOE Volatility Index"

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Kyung, Richard, and Minjun Kye. "Study on the CBOE Volatility Data Forecast Using Statistical and Computational Simulations." In 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). IEEE, 2020. http://dx.doi.org/10.1109/iemtronics51293.2020.9216432.

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"Sudden Changes in Global Volatility Index." In International Conference on Trends in Economics, Humanities and Management. International Centre of Economics, Humanities and Management, 2014. http://dx.doi.org/10.15242/icehm.ed0814046.

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Zhu, Huiming, and Keming Yu. "Bayesian Analysis of Stock Index Return Volatility." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.2302.

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Pranesh, K. Kiran, P. Balasubramanian, and Deepti Mohan. "The determinants of India's implied volatility index." In 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI). IEEE, 2017. http://dx.doi.org/10.1109/icdmai.2017.8073532.

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Guo, Xicai. "Index Futures and Spot Index Volatility: Evidence from China Stock Market." In 2011 Fourth International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2011. http://dx.doi.org/10.1109/bife.2011.69.

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Hanyu Zhang, Fang Wei, and Zhonghan Zhang. "Modeling volatility of Baltic dry bulk freight index." In 2008 IEEE International Conference on Automation and Logistics (ICAL). IEEE, 2008. http://dx.doi.org/10.1109/ical.2008.4636313.

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Jie Wei and Liyan Han. "Volatility transmission between Hangseng index futures and option markets." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5691598.

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Muzzioli, Silvia, Luca Gambarelli, and Bernard De Baets. "Towards a fuzzy volatility index for the Italian market." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015446.

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He, Yichen, Yijun Mao, Xianfen Xie, Wanrong Gu, and Ziye Zhang. "Multi-factor Data Mining Analysis of Stock Index Volatility." In ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3469213.3470231.

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Xu, Xu, Zuoliang Xu, and Jun Suo. "Volatility Inversion and Empirical Analysis on Hang Seng Index Option." In 2019 6th International Conference on Systems and Informatics (ICSAI). IEEE, 2019. http://dx.doi.org/10.1109/icsai48974.2019.9010444.

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Reports on the topic "The CBOE Volatility Index"

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Goncalves, Silvia, and Massimo Guidolin. Predictable Dynamics in the S&P 500 Index Options Implied Volatility Surface. Federal Reserve Bank of St. Louis, 2005. http://dx.doi.org/10.20955/wp.2005.010.

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Engle, Robert, Alex Kane, and Jaesun Noh. Index-Option Pricing with Stochastic Volatility and the Value of Accurate Variance Forecasts. Cambridge, MA: National Bureau of Economic Research, November 1993. http://dx.doi.org/10.3386/w4519.

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Research Institute (IFPRI), International Food Policy. 2011 Global Hunger Index The Challenge of Hunger: Taming price spikes and excessive food price volatility. Washington, DC: International Food Policy Research Institute, 2011. http://dx.doi.org/10.2499/9780896299344enghi2011.

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