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Journal articles on the topic 'The CBOE Volatility Index'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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3

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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4

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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6

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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7

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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8

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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9

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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10

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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11

Shaikh, Imlak. "The U.S. Presidential Election 2012/2016 and Investors’ Sentiment: The Case of CBOE Market Volatility Index." SAGE Open 9, no. 3 (July 2019): 215824401986417. http://dx.doi.org/10.1177/2158244019864175.

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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.

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12

de Boyrie, Maria E., and Ivelina Pavlova. "Equities and Commodities Comovements: Evidence from Emerging Markets." Global Economy Journal 18, no. 3 (April 26, 2018): 20170075. http://dx.doi.org/10.1515/gej-2017-0075.

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The financialization of commodities and their inclusion in financial portfolios as part of an investment strategy may result in higher correlations and volatility spillovers between commodity and equity markets. In this paper, we estimate the correlation between equity markets and commodities using the dynamic conditional correlation (DCC) model, while emphasizing the differences between emerging and developed markets co-movements with commodities. The results reveal that certain emerging markets, especially those in Asia, show a much lower level of co-movement with commodities than developed markets do, while Latin American equities exhibit a higher level of integration with commodities. Furthermore, it is found that both agricultural and precious metals commodities offer better diversification possibilities in the less developed markets. We also find that increases in the CBOE Volatility Index (VIX) are related to higher agriculture commodities-equities correlations, while commodity net index investment has limited explanatory power in our study.

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13

Guo, Zi-Yi. "A Model of Plausible, Severe and Useful Stress Scenarios for VIX Shocks." Applied Economics and Finance 4, no. 3 (April 18, 2017): 155. http://dx.doi.org/10.11114/aef.v4i3.2309.

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The implied volatility is a key component in determining option prices, and consequently a model of VIX shocks in stress testing plays a crucial role in quantifying market risk of derivative portfolios. Based on hypothetical moves of SPX spot price, we first apply the “sticky strike” rule to the existing SPX volatility surface and shock the implied volatility level by an additional relative amount, which would be determined by the analysis of historical VIX fluctuations. Then, we calculate the after-shock VIX index level according to the CBOE VIX White paper, and finally determine the daily VIX shocks. Our backtesting results show that the model could generate realistic VIX shocks in mimicking historical financial crises. A simple application of our model generates stress testing scenarios of VIX shocks comparable with the scenarios from a leading financial institution in the United States. Our model has practical implications for the Basel stress testing.

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14

Bekiros, Stelios D., and Dimitris A. Georgoutsos. "Non-linear dynamics in financial asset returns: the predictive power of the CBOE volatility index." European Journal of Finance 14, no. 5 (July 2008): 397–408. http://dx.doi.org/10.1080/13518470802042203.

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15

Özdurak, Caner. "Nexus between crude oil prices, clean energy investments, technology companies and energy democracy." Green Finance 3, no. 3 (2021): 337–50. http://dx.doi.org/10.3934/gf.2021017.

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<abstract> <p>In this study, we examine the nexus between crude oil prices, clean energy investments, technology companies, and energy democracy. Our dataset incorporates four variables which are S & P Global Clean Energy Index (SPClean), Brent crude oil futures (Brent), CBOE Volatility Index (VIX), and NASDAQ 100 Technology Sector (DXNT) daily prices between 2009 and 2021. The novelty of our study is that we included technology development and market fear as important factors and assess their impact on clean energy investments. DCC-GARCH models are utilized to analyze the spillover impact of market fear, oil prices, and technology company stock returns to clean energy investments. According to our findings when oil prices decrease, the volatility index usually responds by increasing which means that the market is afraid of oil price surges. Renewable investments also tend to decrease in that period following the oil price trend. Moreover, a positive relationship between technology stocks and renewable energy stock returns also exists.</p> </abstract>

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16

Kwon, Soon Shin, Byung Jin Kang, and Jay M. Chung. "Performance of Option Based Strategy Benchmark Index." Journal of Derivatives and Quantitative Studies 26, no. 2 (May 31, 2018): 183–216. http://dx.doi.org/10.1108/jdqs-02-2018-b0002.

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This paper develops “Strategy Benchmark Index (SBI)” using KOSPI200 options data from January 2004 to March 2017, and then investigates their performances. The SBIs were constructed in the same way as those published daily by CBOE. To effectively analyze the performance of these SBIs, we classified them into four types : (1) Return enhancement SBIs (six indices), (2) Volatility trading SBIs (two indices), (3) Directional trading SBIs (two indices) and (4) Other SBIs (two indices). The return enchancement SBIs include bechmark indices tracking the performance of various covered call strategies and put writing strategies, which are generally used to increase investment returns. The volatility trading SBIs include benchmark indices tracking the performance of well-known volatility trading strategies such as butterfly spread and condor. Benchmark indices tracking the performance of various types of zero-cost collar strategies are classified into the directional trading SBIs. Our empirical results are as follows. First, the risk-adjusted performances of nine SBIs of the total twelve SBIs constructed from KOSPI200 index options has been shown to be great. Second, from a portfolio perspective, some SBIs can be helpful to improve the portfolio performance of CRRA (Constant Relative Risk Aversion) investors. These results imply that passive investment strategies with KOSPI200 index options can provide additional benefits that both equities and bonds do not provide. Third, even when we use the traditional mean-variance framework other than expected utility theory to verify the economic benefit of the SBIs, our empirical results are found to be still valid. In conclusion, our results suggest that some passive investment strategies using KOSPI200 index options would be beneficial to long term investors.

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17

Zhang, Wenjun, and Jin E. Zhang. "GARCH Option Pricing Models and the Variance Risk Premium." Journal of Risk and Financial Management 13, no. 3 (March 9, 2020): 51. http://dx.doi.org/10.3390/jrfm13030051.

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In this paper, we modify Duan’s (1995) local risk-neutral valuation relationship (mLRNVR) for the GARCH option-pricing models. In our mLRNVR, the conditional variances under two measures are designed to be different and the variance process is more persistent in the risk-neutral measure than in the physical one, so that one is able to capture the variance risk premium. Empirical estimation exercises show that the GARCH option-pricing models under our mLRNVR are able to price the SPX one-month variance swap rate, i.e., the CBOE Volatility Index (VIX) accurately. Our research suggests that one should use our mLRNVR when pricing options with GARCH models.

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18

Będowska-Sójka, Barbara, and Krzysztof Echaust. "Commonality in Liquidity Indices: The Emerging European Stock Markets." Systems 7, no. 2 (April 28, 2019): 24. http://dx.doi.org/10.3390/systems7020024.

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The aim of the paper is to examine commonality in liquidity indices across emerging European stock markets. Five markets are included in the study: Hungarian, Czech, Polish, Russian and Turkish, in the period from 2008 to 2017. We propose liquidity indices that are based on low-frequency liquidity proxies and capture both the dynamics coming from volume and price changes. We find strong commonality of the liquidity indices across all examined markets which is robust to the choice of liquidity proxy. The dependence between indices enhances in times of crisis and large market declines, and weakens when markets become stable. We also examine the interdependency between liquidity and volatility estimates and find that liquidity on the European emerging markets is related to CBOE Volatility Index (VIX). Liquidity in the whole region decreases when VIX increases, and vice versa. The liquidity indices based on the extreme market movements show that there are no differences in commonality in time of extreme negative and positive returns.

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19

Kokholm, Thomas, and Martin Stisen. "Joint pricing of VIX and SPX options with stochastic volatility and jump models." Journal of Risk Finance 16, no. 1 (January 19, 2015): 27–48. http://dx.doi.org/10.1108/jrf-06-2014-0090.

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Purpose – This paper studies the performance of commonly employed stochastic volatility and jump models in the consistent pricing of The CBOE Volatility Index (VIX) and The S&P 500 Index (SPX) options. With the existence of active markets for volatility derivatives and options on the underlying instrument, the need for models that are able to price these markets consistently has increased. Although pricing formulas for VIX and vanilla options are now available for commonly used models exhibiting stochastic volatility and/or jumps, it remains to be shown whether these are able to price both markets consistently. This paper fills this vacuum. Design/methodology/approach – In particular, the Heston model, the Heston model with jumps in returns and the Heston model with simultaneous jumps in returns and variance (SVJJ) are jointly calibrated to market quotes on SPX and VIX options together with VIX futures. Findings – The full flexibility of having jumps in both returns and volatility added to a stochastic volatility model is essential. Moreover, we find that the SVJJ model with the Feller condition imposed and calibrated jointly to SPX and VIX options fits both markets poorly. Relaxing the Feller condition in the calibration improves the performance considerably. Still, the fit is not satisfactory, and we conclude that one needs more flexibility in the model to jointly fit both option markets. Originality/value – Compared to existing literature, we derive numerically simpler VIX option and futures pricing formulas in the case of the SVJ model. Moreover, the paper is the first to study the pricing performance of three widely used models to SPX options and VIX derivatives.

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Naifar, Nader. "What Explains the Sovereign Credit Default Swap Spreads Changes in the GCC Region?" Journal of Risk and Financial Management 13, no. 10 (October 16, 2020): 245. http://dx.doi.org/10.3390/jrfm13100245.

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This paper aimed to investigate the drivers of sovereign credit risk spreads changes in the case of four Gulf Cooperation Council (GCC) countries, namely Kingdom of Saudi Arabia (KSA), the United Arab Emirates (UAE), Qatar, and Bahrain. Specifically, we explained the changes in sovereign credit default swap (hereafter SCDS) spreads at different locations of the spread distributions by three categories of explanatory variables: global uncertainty factors, local financial variables, and global financial market variables. Using weekly data from 5 April 2013, to 17 January 2020, and the quantile regression model, empirical results indicate that the global factors outperform the local factors. The most significant variables for all SCDS spreads are the global financial uncertainty embedded in the Chicago Board Options Exchange (CBOE) volatility index (VIX) and the global conventional bond market uncertainty embedded in the Merrill Lynch Option Volatility Estimate (MOVE) index. Moreover, the MOVE index affects the various SCDS spreads only when the CDS markets are bullish. Interestingly, the SCDS spreads are not affected by the global economic policy and the gold market uncertainties. Additionally, a weak dependence is observed between oil prices and SCDS spreads. For the country-specific factors, stock market returns are the most significant variable and impact the SCDS spreads at different market circumstances.

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Ishfaq, Muhammad, Zhang Bi Qiong, and Ghulam Abbas. "Global Volatility Spillover, Transaction Cost and CNY Exchange Rate Parities." Mediterranean Journal of Social Sciences 9, no. 2 (March 1, 2018): 161–71. http://dx.doi.org/10.2478/mjss-2018-0036.

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AbstractThe present study examines the intertemporal association between CBOE market volatility indices (VIX), foreign exchange rates and respective bid-ask spread for four CNY exchange rate parities. For this purpose, the study utilizes the stylized EGARCH (1, 1) model for the period of 2011 to 2016. Results report that negative slopes of EUVIX, BPVIX, and JYVIX imply a higher level of volatility, hence improves the underlying exchange rate through appreciation, while positive slopes of VXFXI deteriorates exchange rates during the sample period. Similarly, high volatility widens bid-ask spread which, in turn, deteriorates respective exchange rate and vice versa. The market-oriented policies of China increased the forecasting capability of options volatility indexes to anticipate exchange rate dynamics from 2% to 5%. This indicates that flexible exchange rate regimes lead to increase the predicting power of micro structural components. Assessments of Post-reforms in CNY exchange rate evidence the rise in volatility in financial markets of China, which may discourage investor confidence and seeks for ‘flight to safety’ effect. While, low volatility reduces bid-ask spread which improves underlying exchange rate. The level and variance estimates of exchange rates and spreads reveal that there exists a significant relationship with VIX indices which implies that GARCH forecasts outperform in anticipating future volatility. The volatility estimates of variances show the persistence of volatility and absence of leverage effect. Overall, this article suggests that VIX index can act as ‘fear gauge’ indicator and its potential direction may guide investors in anticipating the movements of CNY exchange rate parities. Moreover, outcomes provide imperative implications to monetary and financial institutions for policy framing.

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Senarathne, Chamil W. "Gambling Behaviour in the Cryptocurrency Market." International Journal of Applied Behavioral Economics 8, no. 4 (October 2019): 1–16. http://dx.doi.org/10.4018/ijabe.2019100101.

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This article examines whether the investment strategies of cryptocurrency market involve high-risk gambling. Results show that the cryptocurrency risk premiums co-move closely with the return on CBOE Volatility Index (VIX). As such, the strategies of cryptocurrency trading closely resemble that of high-risk gambling. In other words, traders' expectations co-move closely (significantly) with the expected future payoffs from gambling. The co-movement is more pronounced when the gambling offers gains rather than losses and the payoffs are above average. VIX index returns significantly Granger-cause CSAD of returns (with and without Bitcoin) indicates that the cryptocurrency trading constitutes a form of gambling where the motivation for gambling comes from the amount of variation (i.e. riskiness) in the gambling payoffs. These findings warrant policymakers of countries to revisit the existing regulatory framework governing the conduct of electronic finance in the financial services industry.

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Grima, Simon, Letife Özdemir, Ercan Özen, and Inna Romānova. "The Interactions between COVID-19 Cases in the USA, the VIX Index and Major Stock Markets." International Journal of Financial Studies 9, no. 2 (May 20, 2021): 26. http://dx.doi.org/10.3390/ijfs9020026.

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With this study, we aimed to determine (1) the effect of the daily new cases and deaths due to the COVID-19 pandemic in the United States on the CBOE volatility index (VIX index) and (2) the effect of the VIX index on the major stock markets during the early stage of the pandemic period. To do this, we collected and analysed the daily new cases and death numbers during the COVID-19 pandemic period in the United States and the country indexes of the USA (DJI), Germany (DAX), France (CAC40), England (FTSE100), Italy (MIB), China (SSEC) and Japan (Nikkei225) to determine the impact of the VIX index on the major stock markets. We then subjected this data to the Johansen co-integration test and the fully modified least-squares (FMOLS) method. The results indicated that there was co-integration between the VIX and the COVID-19 pandemic and that there was co-integration between the VIX index and major indexes, except for the CAC 40 and MIB. Moreover, the results showed that the new COVID-19 cases in the USA had a higher impact on the VIX than cases of deaths during the same period.

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Kjærland, Frode, Aras Khazal, Erlend Krogstad, Frans Nordstrøm, and Are Oust. "An Analysis of Bitcoin’s Price Dynamics." Journal of Risk and Financial Management 11, no. 4 (October 15, 2018): 63. http://dx.doi.org/10.3390/jrfm11040063.

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This paper aims to enhance the understanding of which factors affect the price development of Bitcoin in order for investors to make sound investment decisions. Previous literature has covered only a small extent of the highly volatile period during the last months of 2017 and the beginning of 2018. To examine the potential price drivers, we use the Autoregressive Distributed Lag and Generalized Autoregressive Conditional Heteroscedasticity approach. Our study identifies the technological factor Hashrate as irrelevant for modeling Bitcoin price dynamics. This irrelevance is due to the underlying code that makes the supply of Bitcoins deterministic, and it stands in contrast to previous literature that has included Hashrate as a crucial independent variable. Moreover, the empirical findings indicate that the price of Bitcoin is affected by returns on the S&P 500 and Google searches, showing consistency with results from previous literature. In contrast to previous literature, we find the CBOE volatility index (VIX), oil, gold, and Bitcoin transaction volume to be insignificant.

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Zhang, Wenting, and Shigeyuki Hamori. "Do Machine Learning Techniques and Dynamic Methods Help Forecast US Natural Gas Crises?" Energies 13, no. 9 (May 9, 2020): 2371. http://dx.doi.org/10.3390/en13092371.

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Our study combines machine learning techniques and dynamic moving window and expanding window methods to predict crises in the US natural gas market. Specifically, as machine learning models, we employ extreme gradient boosting (XGboost), support vector machines (SVMs), a logistic regression (LogR), random forests (RFs), and neural networks (NNs). The data set used to develop the model covers the period 1994 to 2019 and contains 121 explanatory variables, including those related to crude oil, stock markets, US bond and gold futures, the CBOE Volatility Index (VIX) index, and agriculture futures. To the best of our knowledge, this study is the first to combine machine learning techniques with dynamic approaches to predict US natural gas crises. To improve the model’s prediction accuracy, we applied a suite of parameter-tuning methods (e.g., grid-search) to select the best-performing hyperparameters for each model. Our empirical results demonstrated very good prediction accuracy for US natural gas crises when combining the XGboost model with the dynamic moving window method. We believe our findings will be useful to investors wanting to diversify their portfolios, as well as to policymakers wanting to take preemptive action to reduce losses.

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Andreou, Elena, Patrick Gagliardini, Eric Ghysels, and Mirco Rubin. "Mixed-Frequency Macro–Finance Factor Models: Theory and Applications*." Journal of Financial Econometrics 18, no. 3 (2020): 585–628. http://dx.doi.org/10.1093/jjfinec/nbaa015.

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Abstract This article presents tests for the existence of common factors spanning two large panels/groups of macroeconomic and financial variables, and the estimation of common and group-specific factors. New analytical results are derived regarding (i) the difference in the asymptotic distribution of the test statistics when aggregating the data first and then extracting the principal components (PCs), or vice versa, as well as (ii) the estimation of the common factor and its asymptotic distribution, extending the work of Andreou et al. (2019). We find that although there is no empirical evidence for one common factor, with constant loadings, in the United States during the period 1963–2017, there is evidence of one common macro–finance factor during the pre- and post-Great Moderation regimes. The aforementioned approaches of estimating PCs yield almost identical common and group-specific (financial and macro) factors which turn out to be significant in predicting key economic indicators, such as real Gross Domestic Product (GDP) growth and the CBOE Volatility Index, among others.

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Silva Junior, Julio Cesar Araujo da. "An S-Shaped Crude Oil Price Return-Implied Volatility Relation: Parametric and Nonparametric Estimations." International Journal of Economics and Finance 9, no. 12 (November 13, 2017): 54. http://dx.doi.org/10.5539/ijef.v9n12p54.

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Oil market movements have important implications for portfolio management and hedge strategies for investors who negotiate this commodity. Studies involving the relation of the CBO Crude Oil ETF Volatility Index (OVX) and the United States Oil Fund (USO) return are small in number and do not explore some aspects related to the asymmetry and nonlinearity of this relation. Therefore, this article proposes an analysis about the relation between return and volatility, using parametric and nonparametric methods. To do so, a daily data series from 2007 to 2016, ordinary least squares, quantile regressions and the nonparametric B-splines methods were used. The results indicated a negative, asymmetric and nonlinear contemporary relation between the variables. The effects of negative returns were more pronounced than the positive ones in volatility. In addition, it was found that the relation is not the same for different quantiles. Nonparametric estimates suggested that the positive returns have a convex profile and the negative returns have a concave profile. It indicated the downward-sloping reclined S-curve for the 0.05, 0.90 and 0.95 quantiles of volatility.

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Corrado, Charles J., and Thomas W. Miller, Jr. "The forecast quality of CBOE implied volatility indexes." Journal of Futures Markets 25, no. 4 (2005): 339–73. http://dx.doi.org/10.1002/fut.20148.

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29

Chance, Don M., and Stephen P. Ferris. "The CBOE call option index." Journal of Portfolio Management 12, no. 1 (October 31, 1985): 75–83. http://dx.doi.org/10.3905/jpm.1985.409035.

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30

Jung, Young Cheol. "Forecasting Power of Five CBOE Volatility Indexes for the Price Interval." International Review of Business Research Papers 14, no. 1 (March 31, 2018): 205–25. http://dx.doi.org/10.21102/irbrp.2018.03.141.12.

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31

Markellos, Raphael N., and Dimitris Psychoyios. "Interest rate volatility and risk management: Evidence from CBOE Treasury options." Quarterly Review of Economics and Finance 68 (May 2018): 190–202. http://dx.doi.org/10.1016/j.qref.2017.08.005.

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32

Whaley, Robert E. "Return and Risk of CBOE Buy Write Monthly Index." Journal of Derivatives 10, no. 2 (November 30, 2002): 35–42. http://dx.doi.org/10.3905/jod.2002.319194.

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33

Lin, Yueh-Neng. "VIX option pricing and CBOE VIX Term Structure: A new methodology for volatility derivatives valuation." Journal of Banking & Finance 37, no. 11 (November 2013): 4432–46. http://dx.doi.org/10.1016/j.jbankfin.2013.03.006.

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34

Wei, Yu, Chao Liang, Yan Li, Xunhui Zhang, and Guiwu Wei. "Can CBOE gold and silver implied volatility help to forecast gold futures volatility in China? Evidence based on HAR and Ridge regression models." Finance Research Letters 35 (July 2020): 101287. http://dx.doi.org/10.1016/j.frl.2019.09.002.

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35

Lu, Xinjie, Feng Ma, Jiqian Wang, and Jianqiong Wang. "Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models." Energy 212 (December 2020): 118743. http://dx.doi.org/10.1016/j.energy.2020.118743.

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36

Hansoo Yoo. "Korean Stock Price Index Volatility and Japanese Stock Price Index Volatility." Journal of Eurasian Studies 7, no. 1 (March 2010): 47–59. http://dx.doi.org/10.31203/aepa.2010.7.1.003.

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37

Huang, Darien, Christian Schlag, Ivan Shaliastovich, and Julian Thimme. "Volatility-of-Volatility Risk." Journal of Financial and Quantitative Analysis 54, no. 6 (November 5, 2018): 2423–52. http://dx.doi.org/10.1017/s0022109018001436.

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Abstract:

We show that market volatility of volatility is a significant risk factor that affects index and volatility index option returns, beyond volatility itself. The volatility and volatility of volatility indices, identified model-free as the VIX and VVIX, respectively, are only weakly related to each other. Delta-hedged index and VIX option returns are negative on average and are more negative for strategies that are more exposed to volatility and volatility-of-volatility risks. Further, volatility and volatility of volatility significantly negatively predict future delta-hedged option payoffs. The evidence suggests that volatility and volatility-of-volatility risks are jointly priced and have negative market prices of risk.

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Hansoo Yoo. "Exchange Rate Volatility and Stock Price Index Volatility." Global Business Administration Review 5, no. 1 (March 2008): 125–48. http://dx.doi.org/10.17092/jibr.2008.5.1.125.

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39

Emory, Claire. "Index Volatility in Perspective." CFA Digest 40, no. 4 (November 2010): 65–66. http://dx.doi.org/10.2469/dig.v40.n4.5.

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40

Hill, Joanne M. "Index Volatility in Perspective." Journal of Index Investing 1, no. 1 (May 31, 2010): 12–23. http://dx.doi.org/10.3905/jii.2010.1.1.012.

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41

Bramante, Riccardo, and Santamaria Luigi. "Forecasting stock index volatility." Applied Stochastic Models in Business and Industry 17, no. 1 (January 2001): 19–26. http://dx.doi.org/10.1002/asmb.423.

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42

Choi, Ji-Eun, and Dong Wan Shin. "Bootstrapping volatility spillover index." Communications in Statistics - Simulation and Computation 49, no. 1 (November 4, 2018): 66–78. http://dx.doi.org/10.1080/03610918.2018.1476696.

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43

Lee, Jae Ha, and Je Ryun Chung. "Lead-Lag Relationship between Volatility Index and Stock Market Index." Journal of Derivatives and Quantitative Studies 13, no. 2 (November 30, 2005): 87–105. http://dx.doi.org/10.1108/jdqs-02-2005-b0004.

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This study examines the lead-lag relationship between KOSPI200 and the volatility index based on the implied volatility from the KOSPI200 options. The sample period covers from January 2, 2003 to June 30, 2004. Both daily and minute-by-minute data were used for the lead-lag analysis. The study also determines whether the response of volatil ity index to KOSPI200 is symmetric or not. The most important findings may be summarized as follows. First, there is no lead-lag relationship between the change in volatility index and the KOSPI200 returns on a daily basis. However, on a minute-by-minute basis, volatility index leads KOSPI200 for the group of largest increases in volatility index, and the opposite is true for the group of largest decreases and least changes in volatility index. The option market appears to react more quickly to volatility increases, while the stock market seems more sensitive to volatility decreases. Second, the volatility increase in response to the stock market decline is more severe than the volatility decrease in response to the stock market rise for daily data. This evidence of asymmetry suggests that volatility index plays a role of investors’fear gauge. Our results show no asymmetric response of volatility index to stock market movements for weekly data.

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44

Mazouz, Khelifa. "The effect of CBOE option listing on the volatility of NYSE traded stocks: a time-varying variance approach." Journal of Empirical Finance 11, no. 5 (December 2004): 695–708. http://dx.doi.org/10.1016/j.jempfin.2003.09.003.

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45

Tanjung, Hendri. "Volatility of Jakarta Islamic Index." Al-Iqtishad: Jurnal Ilmu Ekonomi Syariah 6, no. 2 (July 29, 2014): 207–22. http://dx.doi.org/10.15408/aiq.v6i2.1231.

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Volatility of Jakarta Islamic Index. This study investigates the volatility of Jakarta Islamic Index (JII) in Jakarta Stock Exchange. The method that used in this research is used a simple statistical analysis. The normality of JII return is analyzed to answer whether the return of JII follows normal distribution. By using data of Jakarta Islamic Index from 2nd March 2009 to 30th October 2013 (1122 daily data), it is found that the distribution of return of JII is not normal, even the 5 sigma occurred. This means the return of Jakarta Islamic Index is much volatile than the theory predicted. This will make too much gain or loss in one day in the economy DOI:10.15408/aiq.v6i2.1231

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46

Figlewski, Stephen, and Anja Frommherz. "Volatility Leadership Among Index Options." Journal of Derivatives 25, no. 2 (November 27, 2017): 43–60. http://dx.doi.org/10.3905/jod.2017.25.2.043.

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47

Whipple, Fred L. "A volatility index for comets." Icarus 98, no. 1 (July 1992): 108–14. http://dx.doi.org/10.1016/0019-1035(92)90211-o.

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48

Kim, Jungmu, Yuen Jung Park, and Doojin Ryu. "Testing CEV stochastic volatility models using implied volatility index data." Physica A: Statistical Mechanics and its Applications 499 (June 2018): 224–32. http://dx.doi.org/10.1016/j.physa.2018.02.001.

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49

Doifode, Adesh, Mrityunjay Tiwary, and Vaibhav Aggarwal. "Volatility Spillover from Institutional Equity Investments to Indian Volatility Index." International Journal of Management Concepts and Philosophy 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijmcp.2020.10031501.

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Aggarwal, Vaibhav, Adesh Doifode, and Mrityunjay Kumar Tiwary. "Volatility spillover from institutional equity investments to Indian volatility index." International Journal of Management Concepts and Philosophy 13, no. 3 (2020): 173. http://dx.doi.org/10.1504/ijmcp.2020.111020.

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