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Journal articles on the topic 'Index VIX'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Shaikh, Imlak, and Puja Padhi. "On the relationship between implied volatility index and equity index returns." Journal of Economic Studies 43, no. 1 (2016): 27–47. http://dx.doi.org/10.1108/jes-12-2013-0198.

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Purpose – The purpose of this paper is to analyze the asymmetric contemporaneous relationship between implied volatility index (India VIX) and Equity Index (S & P CNX Nifty Index). In addition, the study also analyzes the seasonality of implied volatility index in the form of day-of-the-week effects and option expiration cycle. Design/methodology/approach – This study employs simple OLS estimation to analyze the contemporaneous relationship among the volatility index and stock index. In order to obtain robust results, the analysis has been presented for the calendar years and sub-periods. Moreover, the international evidenced presented for other Asian markets (Japan and China). Findings – The empirical evidences reveal a strong persistence of asymmetry among the India VIX and Nifty stock index, at the same time the magnitude of asymmetry is not identical. The results show that the changes in India VIX occur bigger for the negative return shocks than the positive returns shocks. The similar kinds of results are recorded for the Japan and China volatility index. Particularly, the analysis also supports that India VIX holds seasonality, on the market opening VIX observed to be at its high level, and on the subsequent days it remains low. The results on the options expiration unfold the facts that India VIX remains more normal on the day of expiration. Practical implications – The asymmetric relation and seasonal patterns are quite useful to the volatility traders to price the financial assets when market trades in the high- and low-volatility periods. Originality/value – There is a lack of studies of this kind in the context of emerging markets like India; hence, this is an attempt in this direction. The study provides an insight to the NSE to launch some derivative products (i.e. F & Os) on India VIX that can generate more liquidity in the market for the volatility traders.

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2

Adrangi, Bahram, Arjun Chatrath, Madhuparna Kolay, and Kambiz Raffiee. "Dynamic Responses of Standard and Poor’s Regional Bank Index to the U.S. Fear Index, VIX." Journal of Risk and Financial Management 14, no. 3 (2021): 114. http://dx.doi.org/10.3390/jrfm14030114.

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This study examines the reaction of the Standard and Poor’s Regional Bank Index (SPRB) to the U.S. equity market fear index (i.e., the Chicago Board of Trade Volatility Index [VIX]). The VIX is designed to perform as a leading indicator of the volatility in equity markets. However, practitioners observe many periods of divergence between the VIX and S&P 500. Our paper examines the daily data for the period of 2009 through 2019. We show that once the effects of consumer confidence and capacity utilization are accounted for, there is a negative association between the VIX and regional bank performance.

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3

G. Russon, Manuel, and Ahmad F. Vakil. "On the non-linear relationship between VIX and realized SP500 volatility." Investment Management and Financial Innovations 14, no. 2 (2017): 200–206. http://dx.doi.org/10.21511/imfi.14(2-1).2017.05.

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VIX, a ticker symbol for Volatility Index, measures the implied annual volatility of at-the-money SP500 Index Options. Conventional wisdom presumes VIX to measure the magnitude (positive or negative) of possible movements in future equity prices, with movements being a positive function of VIX. This research investigates the nature of the relationship between VIX and SP500 volatility, and answers the question as to whether that relationship is linear or nonlinear. Based on this research paper, the authors conclude that the realized SP500 volatility is nonlinear, and grows with the level of VIX at an increasing rate. The nonlinearity relationship between VIX and SP500 has enormous implications for investment management and hedging in the financial markets.

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4

Ratner, Mitchell, and Chih-Chieh (Jason) Chiu. "Portfolio Effects of VIX Futures Index." Quantitative Finance and Economics 1, no. 3 (2017): 288–99. http://dx.doi.org/10.3934/qfe.2017.3.288.

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5

Saha, Atanu, Burton G. Malkiel, and Alex Rinaudo. "Has the VIX index been manipulated?" Journal of Asset Management 20, no. 1 (2018): 1–14. http://dx.doi.org/10.1057/s41260-018-00102-4.

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6

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 (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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7

Dr. Avijit Sikdar. "Study of Association between Volatility Index and Nifty using VECM." International Journal of Engineering and Management Research 11, no. 1 (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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8

Hancock, G. D. "Behind the Volatility Index Levels: The Paradox of 2016." International Research in Economics and Finance 1, no. 1 (2017): 44. http://dx.doi.org/10.20849/iref.v1i1.270.

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The low 2016 volatility index levels present a paradox in light of previous research suggesting periods of uncertainty and negative news events should reflect higher VIX levels. This study uses daily data for the VIX, VIX futures and the VVIX, to examine the information content of variations in the natural logarithmic changes in the index levels relative to 12 other parallel time periods encompassing 2004-2016. Straight-forward variation and predictive tests are constructed to determine signs of unusual market volatility behavior. The results reveal strong evidence of unusual volatility behavior during the 2016 election period, pocked by frequent periods of abnormal returns. The 2016 VIX levels alone are shown to be insufficient to draw conclusions regarding investor sentiment.

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9

Magafas, L., M. Hanias, A. Tavlatou, and P. Kostantaki. "Non-Linear Properties of the VIX Index." International Journal of Productivity Management and Assessment Technologies 5, no. 2 (2017): 16–24. http://dx.doi.org/10.4018/ijpmat.2017070102.

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This paper applies non-linear methods to analyze and predict the daily VIX index which is one of the most important stock indexes in the world. The aim of the analysis is to quantitatively show if the corresponding time series is a deterministic chaotic one and if one or more days ahead prediction can be achieved. The research employs Grassberger and Procaccia's methodology in the time series analysis in order to estimate the correlation and minimum embedding dimensions of the corresponding strange attractor. To achieve from the sample a multistep ahead prediction, the article gives the average for overall neighbours' projections of k-steps into the future. These results make the present work a valuable tool for traders, investors, and funds.

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10

GRASSELLI, MARTINO, and LAKSHITHE WAGALATH. "VIX VERSUS VXX: A JOINT ANALYTICAL FRAMEWORK." International Journal of Theoretical and Applied Finance 23, no. 05 (2020): 2050033. http://dx.doi.org/10.1142/s0219024920500338.

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We propose a framework for modeling in a consistent manner the VIX index and the VXX, an exchange-traded note written on the VIX. Our study enables to link the properties of VXX to those of the VIX in a tractable way. In particular, we quantify the systematic loss observed empirically for VXX when the VIX futures term-structure is in contango and we derive option prices, implied volatilities and skews of VXX from those of VIX in infinitesimal developments. We also perform a calibration on real data which highlights the flexibility of our model in fitting the futures and the vanilla options market of VIX and VXX. Our framework can be used to model other exchange-traded notes on the VIX as well as any market where exchange-traded notes have been introduced on a reference index, hence providing tools to better anticipate and quantify systematic behavior of an exchange-traded note with respect to the underlying index.

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11

Wang, Jying-Nan, Hung-Chun Liu, and Lu-Jui Chen. "On Forecasting Taiwanese Stock Index Option Prices: The Role of Implied Volatility Index." International Journal of Economics and Finance 9, no. 9 (2017): 133. http://dx.doi.org/10.5539/ijef.v9n9p133.

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This paper aims to propose four volatility measures: The first is the GARCH model advocated by Bollerslev (1986); the second is the GARCHVIX model which extends the GARCH model by including the volatility index (VIX) as explanatory variable for volatility; the last two are HS20D and HS252D, which represent the historical volatilities generated by traditional rolling window technique with 20- and 252-day historical index returns data, respectively. We examine the price information on VIX to improve the predictive performance of GARCH model for valuing TAIEX stock index call options (TXO) over the period from January 2014 to May 2015. Empirical results firstly indicate that both the GARCH and GARCHVIX models consistently perform better than the historical volatility models for forecasting call value of TXO under different moneynesses. Secondly, the GARCHVIX model significantly outperforms the GARCH model for most cases, indicating that the GARCH-based option price forecasts can be effectively improved with the additional information contained in VIX. Finally, the use of GARCHVIX model can greatly reduce model mispricing especially for out-the-money TXO option case. Thus, volatility index is crucial for option traders to efficiently predict TXO option value with GARCH model.

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12

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 (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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13

Adrangi, Bahram, Arjun Chatrath, Joseph Macri, and Kambiz Raffiee. "Dynamic Responses of Major Equity Markets to the US Fear Index." Journal of Risk and Financial Management 12, no. 4 (2019): 156. http://dx.doi.org/10.3390/jrfm12040156.

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This study examines the reaction of four major equity markets of the world to the US equity market fear index, i.e., the Chicago Board of Trade Volatility Index (VIX). The VIX is designed to perform as a leading indicator of the volatility in equity markets. Our paper examines the daily data for the period of 2013 through 2018. We find that during this period there were three significant breaks in the data. Impulse responses from the structural vector autoregressive model estimation show that, in the first and second subperiods that cover from 6/2013 through 5/2016, equity market volatility in the US, UK, France, and Germany responded to structural shocks to the VIX. Nonlinear Granger causality tests confirm these findings. However, in the post Brexit-vote era, equity indices neither react to VIX structural shocks nor are caused by these shocks.

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14

KULA, Veysel, and Ender BAYKUT. "BORSA İSTANBUL KURUMSAL YÖNETİM ENDEKSİ (XKURY) İLE KORKU ENDEKSİ (CHICAGO BOARD OPTIONS EXCHANGE VOLATILITY INDEX-VIX) ARASINDAKİ İLİŞKİNİN ANALİZİ." İktisadi ve İdari Bilimler Fakültesi Dergisi 19, no. 2 (2017): 27–37. http://dx.doi.org/10.5578/jeas.63964.

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15

Markowski, Łukasz, and Jakub Keller. "Fear Anatomy – an Attempt to Assess the Impact of Selected Macroeconomic Variables on the Variability of the VIX S&P 500 Index." Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia 54, no. 2 (2020): 41. http://dx.doi.org/10.17951/h.2020.54.2.41-51.

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<p>This article deals with the subject of volatility of financial markets in relation to the US stock market and its volatility index, i.e. the VIX index. The authors analyzed previous studies on the VIX index and based on them, defined a research gap that relates to the problem of market response to emerging macroeconomic information about the US economy. The vast majority of research on the VIX index relates to its forecasting based on mathematical models not taking into account current market data. The authors attempted to assess the impact of emerging macro data on the variability of the VIX index, thus illustrating the magnitude of the impact of individual variables on the so-called US Stock Exchange fear index. The study analysed 80 macroeconomic variables in the period from January 2009 to June 2019 in order to check which of them cause the greatest market volatility. The study was based on correlation study and econometric modeling. The obtained results allowed to formulate conclusions indicating the most important macroeconomic parameters that affect the perception of the market by investors through the pricing of options valuation on the S&amp;P 500 index. The authors managed to filter the most important variables for predicting the change of VIX level. In the eyes of the authors, the added value of the article is to indicate the relationship between macro variables and market volatility illustrated by the VIX index, which has not been explored in previous studies. The analyzes carried out are part of the research trend on market information efficiency and broaden knowledge in the area of capital investments.</p>

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16

Slivka, Ronald T., Shuang Gao, and Jie Ren. "An Empirical Model of India's Nifty VIX Index." Indian Journal of Finance 9, no. 8 (2015): 7. http://dx.doi.org/10.17010//2015/v9i8/74559.

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17

Slivka, Ronald T., Shuang Gao, and Jie Ren. "An Empirical Model of India's Nifty VIX Index." Indian Journal of Finance 9, no. 8 (2015): 7. http://dx.doi.org/10.17010/ijf/2015/v9i8/74559.

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18

Ishfaq, Muhammad, Zhang Bi Qiong, and Awais ur Rehman. "Global Volatility Spillover in Asian Financial Markets." Mediterranean Journal of Social Sciences 9, no. 2 (2018): 109–16. http://dx.doi.org/10.2478/mjss-2018-0031.

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AbstractThe present paper accommodates the spillover impact of market volatility index of S & P 500 (VIX) and China exchange-traded fund’s volatility (VXFXI) on the emerging equity (KSE-100 index) and foreign exchange markets of Pakistan. In this context, we use a vector autoregressive (VAR) model and impulse response functions (IRF) to explore link among VIX indices and financial markets of Pakistan for the differential time periods. The study concludes that a rise in both VIX and VXFXI results in price falls of KSE-100 index and deteriorates exchange rate market. This implies that VIX act as ‘fear gauge’ on both stock and exchange rate markets in Pakistan. These outcomes provide an imperative implication on the pattern of currency and stock sensitivities against global volatility. This reveals that adverse movements in global volatility in the USA and Chinese financial market have a significant impact and a rise in VIX causes an outflow of investment from financial markets of Pakistan. Moreover, our results may guide local and global investors to anticipate the potential direction of stock and exchange rate markets based on market volatility index.

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19

Lin, Jeng-Bau, Chin-Chia Liang, and Wei Tsai. "Nonlinear Relationships between Oil Prices and Implied Volatilities: Providing More Valuable Information." Sustainability 11, no. 14 (2019): 3906. http://dx.doi.org/10.3390/su11143906.

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This paper investigates the linear/nonlinear long-run and short-run dynamic relationships between oil prices and two implied volatilities, oil price volatility index (OVX) and stock index options volatility index (VIX), representing panic gauges. The results show that there is a long-run equilibrium relationship between oil prices and OVX (VIX) using the linear autoregressive distributed lag (ARDL)-bounds test. Likewise, while using the nonlinear autoregressive distributed lag (NARDL)-bounds test, not only does a long-run equilibrium relationship exist, but also the rising OVX (VIX) has a greater negative influence on oil prices than the declining OVX (VIX), thus indicating that a long-run, asymmetric cointegration exists between the variables. Furthermore, OVX (VIX) oil prices have a linear Granger causality, while for the nonlinear Granger causality test, oil prices have a bidirectional relation with OVX (VIX). In addition, we find that once major international political and economic events occur, structural changes in oil prices change the behavior of oil prices, and thus panic indices, thereby switching from a linear relationship to a nonlinear one. The empirical results of this study provide market participants with more valuable information.

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20

Osterrieder, Joerg, Daniel Kucharczyk, Silas Rudolf, and Daniel Wittwer. "Neural networks and arbitrage in the VIX." Digital Finance 2, no. 1-2 (2020): 97–115. http://dx.doi.org/10.1007/s42521-020-00026-y.

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Abstract The Chicago Board Options Exchange Volatility Index (VIX) is considered by many market participants as a common measure of market risk and investors’ sentiment, representing the market’s expectation of the 30-day-ahead looking implied volatility obtained from real-time prices of options on the S&P 500 index. While smaller deviations between implied and realized volatility are a well-known stylized fact of financial markets, large, time-varying differences are also frequently observed throughout the day. Furthermore, substantial deviations between the VIX and its futures might lead to arbitrage opportunities on the VIX market. Arbitrage is hard to exploit as the potential strategy to exploit it requires buying several hundred, mostly illiquid, out-of-the-money (put and call) options on the S&P 500 index. This paper discusses a novel approach to predicting the VIX on an intraday scale by using just a subset of the most liquid options. To the best of the authors’ knowledge, this the first paper, that describes a new methodology on how to predict the VIX (to potentially exploit arbitrage opportunities using VIX futures) using most recently developed machine learning models to intraday data of S&P 500 options and the VIX. The presented results are supposed to shed more light on the underlying dynamics in the options markets, help other investors to better understand the market and support regulators to investigate market inefficiencies.

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21

MADAN, DILIP B. "MULTIVARIATE DISTRIBUTIONS FOR FINANCIAL RETURNS." International Journal of Theoretical and Applied Finance 23, no. 06 (2020): 2050041. http://dx.doi.org/10.1142/s0219024920500417.

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Multivariate return distributions consistent with bilateral gamma marginals are formulated and termed multivariate bilateral gamma (MBG). Tail probability distances and Wasserstein distances between return data, model simulations and their squares evaluate the model performance. A full Gaussian copula [Formula: see text] is taken as an alternate test model and the MBG delivers a comparatively better performance for equity pairs. The MBG is however inadequate for the S&P 500 index return when paired with the VIX returns. Applying MBG to the S&P 500 the index and regression residuals of VIX on the S&P 500 index return is successful. This model is termed MBGR. The residual taken as an independent bilateral gamma, delivers the model MBGIR. Characteristic function estimations are employed to develop asset-specific VIX levels and their joint returns with the asset return are studied. The CBOE SKEW index is generalized to be asset-specific and triples of returns for the asset, its VIX and its SKEW are studied using all four models and performance statistics. The model MBGR continues to deliver a good performance.

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22

Tsuji, Chikashi. "Does the CBOE Volatility Index Predict Downside Risk at the Tokyo Stock Exchange?" International Business Research 10, no. 3 (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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23

Jung, Young Cheol. "A portfolio insurance strategy for volatility index (VIX) futures." Quarterly Review of Economics and Finance 60 (May 2016): 189–200. http://dx.doi.org/10.1016/j.qref.2015.09.001.

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Shaikh, Imlak, and Puja Padhi. "The information content of implied volatility index (India VIX)." Global Business Perspectives 1, no. 4 (2013): 359–78. http://dx.doi.org/10.1007/s40196-013-0025-4.

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25

Silva, Fábio, and Fernando Morais. "Groundwater vulnerability analysis to contamination at the Urubu river watershed, TO, Brazil." Terr Plural 15 (2021): 1–16. http://dx.doi.org/10.5212/terraplural.v.15.2114786.015.

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This study aims to evaluate the vulnerability of groundwater to contamination in the Urubu river watershed, Tocantins. The analysis was based on the VIX vulnerability index and considered the areas with the highest water accumulation, as indicated by the NDWI index values extracted from the study area. It was observed that the highest values of the VIX index were found in areas in which the groundwater levels were deeper. The western region of the basin presented areas more vulnerable to contamination due to the shallow depth of the water table. The use of the VIX and NDWI indices was effective in determining areas that are more susceptible to contamination within the Urubu river basin, thus, it can be considered an efficient tool for local natural resource management.

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Ma, Changfu, Wei Xu, and Yue Kuen Kwok. "Willow tree algorithms for pricing VIX derivatives under stochastic volatility models." International Journal of Financial Engineering 07, no. 01 (2020): 2050003. http://dx.doi.org/10.1142/s2424786320500036.

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VIX futures and options are the most popular contracts traded in the Chicago Board Options Exchange. The bid-ask spreads of traded VIX derivatives remain to be wide, possibly due to the lack of reliable pricing models. In this paper, we consider pricing VIX derivatives under the consistent model approach, which considers joint modeling of the dynamics of the S&P index and its instantaneous variance. Under the affine jump-diffusion formulation with stochastic volatility, analytic integral formulas can be derived to price VIX futures and options. However, these integral formulas invariably involve Fourier inversion integrals with cumbersome hyper-geometric functions, thus posing various challenges in numerical evaluation. We propose a unified numerical approach based on the willow tree algorithms to price VIX derivatives under various common types of joint process of the S&P index and its instantaneous variance. Given the analytic form of the characteristic function of the instantaneous variance of the S&P index process in the Fourier domain, we apply the fast Fourier transform algorithm to obtain the transition density function numerically in the real domain. We then construct the willow tree that approximates the dynamics of the instantaneous variance process up to the fourth order moment. Our comprehensive numerical tests performed on the willow tree algorithms demonstrate high level of numerical accuracy, runtime efficiency and reliability for pricing VIX futures and both European and American options under the affine model and 3/2-model. We also examine the implied volatility smirks and the term structures of the implied skewness of VIX options.

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Cloutier, Richard, Arsen Djatej, and Dean Kiefer. "A tactical asset allocation strategy that exploits variations in VIX." Investment Management and Financial Innovations 14, no. 1 (2017): 27–34. http://dx.doi.org/10.21511/imfi.14(1).2017.03.

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Buy and hold strategies make staying disciplined difficult for investors, especially given the variability of returns for different asset classes/strategies during divergent market conditions. Market timing strategies, on the other hand, present significant theoretical benefits, but in reality these benefits are difficult to obtain. Tactical asset allocation, where limited deviations from the strategic allocation are allowed permits the portfolio manager to take advantage of market conditions fits between these two extremes. The authors correlate daily returns for each of eighteen separate asset classes typically used in diversified institutional portfolios and daily closing values of the VIX (the ticker symbol for the Chicago Board Options Exchange Volatility Index). This information is used to select those classes whose returns are most responsive to the level of the VIX. Portfolio allocations for eight selected asset classes are revised depending on the level of the VIX at the daily close of the market. The portfolio is rebalanced on the business day following the day the VIX hits the trigger value. The VIX tactical allocation overlay yields an increase in return over the buy and hold portfolio of approximately 38 basis points. The authors conclude that the tactical asset allocation strategy based on the level of VIX provides a higher return than the neutral buy and hold allocation with a higher Sharpe ratio and lower volatility.

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Chang, Chia-Lin, Tai-Lin Hsieh, and Michael McAleer. "Connecting VIX and Stock Index ETF with VAR and Diagonal BEKK." Journal of Risk and Financial Management 11, no. 4 (2018): 58. http://dx.doi.org/10.3390/jrfm11040058.

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As stock market indexes are not tradeable, the importance and trading volume of Exchange-Traded Funds (ETFs) cannot be understated. ETFs track and attempt to replicate the performance of a specific index. Numerous studies have demonstrated a strong relationship between the S&P500 Composite Index and the Volatility Index (VIX), but few empirical studies have focused on the relationship between VIX and ETF returns. The purpose of the paper is to investigate whether VIX returns affect ETF returns by using vector autoregressive (VAR) models to determine whether daily VIX returns with different moving average processes affect ETF returns. The ARCH-LM test shows conditional heteroskedasticity in the estimation of ETF returns, so that the Diagonal BEKK (named after Baba, Engle, Kraft and Kroner) model is used to accommodate multivariate conditional heteroskedasticity in the VAR estimates of ETF returns. Daily data on ETF returns that follow different stock indexes in the USA and Europe are used in the empirical analysis, which is presented for the full data set, as well as for the three sub-periods Before, During, and After the Global Financial Crisis. The estimates show that daily VIX returns have: (1) significant negative effects on European ETF returns in the short run; (2) stronger significant effects on single-market ETF returns than on European ETF returns; and (3) lower impacts on the European ETF returns than on S&P500 returns. For the European markets, the estimates of the mean equations tend to differ between the whole sample period and the sub-periods, but the estimates of the matrices A and B in the Diagonal BEKK model are quite similar for the whole sample period and at least two of the three sub-periods. For the US Markets, the estimates of the mean equations also tend to differ between the whole sample period and the sub-periods, but the estimates of the matrices A and B in the Diagonal BEKK model are very similar for the whole sample period and the three sub-periods.

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Allen, David, and Vince Hooper. "Generalized Correlation Measures of Causality and Forecasts of the VIX Using Non-Linear Models." Sustainability 10, no. 8 (2018): 2695. http://dx.doi.org/10.3390/su10082695.

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This paper features an analysis of causal relations between the daily VIX, S&P500 and the daily realised volatility (RV) of the S&P500 sampled at 5 min intervals, plus the application of an Artificial Neural Network (ANN) model to forecast the future daily value of the VIX. Causal relations are analysed using the recently developed concept of general correlation Zheng et al. and Vinod. The neural network analysis is performed using the Group Method of Data Handling (GMDH) approach. The results suggest that causality runs from lagged daily RV and lagged continuously compounded daily return on the S&P500 index to the VIX. Sample tests suggest that an ANN model can successfully predict the daily VIX using lagged daily RV and lagged daily S&P500 Index continuously compounded returns as inputs.

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Chittineni, Jyothi. "Indian Implied Volatility Index: A Macroeconomic Study." Applied Economics and Finance 5, no. 5 (2018): 75. http://dx.doi.org/10.11114/aef.v5i5.3585.

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The study investigates the dynamic behavior of Indian implied volatility index and its time dependent conditional correlations with selected macroeconomic variables. The volatility of macroeconomic variables is likely to put burden on inflation and also influence the economic decisions as investment vehicles. Thus, the volatility of these variables has become central issue for fund managers and investors. The study uses three macroeconomic variables, oil price, gold price and federal fund rate over the period 2nd March 2009 to 30th June 2018. The Dynamic Regime-Switching model reveals that the Indian Implied volatility index exhibits two regimes high volatility and low volatility states. There exists a high degree of synchronicity between Indian VIX and oil price movement. Oil price has significant impact on India VIX during high volatile state. The result alarms the attention of monetary policy makers. The policy of oil price deregulation has to be carefully monitored.

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İskenderoglu, Ömer, and Saffet Akdag. "Comparison of the Effect of Vix Fear Index on Stock Exchange Indices of Developed and Developing Countries: the G20 Case." South East European Journal of Economics and Business 15, no. 1 (2020): 105–21. http://dx.doi.org/10.2478/jeb-2020-0009.

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AbstractThis study aims to examine the potential causal relationship between the VIX and the indicator stock exchange index returns of G20 (9 developed and 10 developing) countries. Nineteen countries of the sample are G20 countries with available data. In this respect, the frequency domain Granger causality test of Breitung and Candelon (2006) is employed for the daily data between March 2011 and December 2017. The results obtained from the study indicate that there is no causal relationship between the VIX and the returns of the NASDAQ 100 index in developed countries. Similarly, no causal relationship is detected which runs from the VIX to the BIST100, BOVESPA, MERVAL, S&P/BMV IPC and TADAWUL stock index returns in developing countries. As a result, the causal relationship is more tend to be found in developed countries in comparison to developing countries.

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32

Black, Keith H. "Is the VIX Futures Market Able to Predict the VIX Index? A Test of the Expectation Hypothesis." CFA Digest 40, no. 1 (2010): 36–38. http://dx.doi.org/10.2469/dig.v40.n1.75.

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Nossman, Marcus, and Anders Wilhelmsson. "Is the VIX Futures Market Able to Predict the VIX Index?A Test of the Expectation Hypothesis." Journal of Alternative Investments 12, no. 2 (2009): 54–67. http://dx.doi.org/10.3905/jai.2009.12.2.054.

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Guo, Zi-Yi. "A Model of Plausible, Severe and Useful Stress Scenarios for VIX Shocks." Applied Economics and Finance 4, no. 3 (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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35

Mollick, Andre. "VIX and the variance of Dow Jones industrial average stocks." Managerial Finance 41, no. 3 (2015): 226–43. http://dx.doi.org/10.1108/mf-07-2013-0197.

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Purpose – The purpose of this paper is to examine what happens to the variance of individual stocks forming the Dow Jones Industrial Average (DJIA) allowing for aggregate uncertainty measured by VIX, the “fear gauge index” of US options contracts. In examining each individual stock belonging to DJIA in 2011, the authors reconsider aggregate market uncertainty (VIX) as the mixing variable. In contrast to studies on the effects of VIX on the aggregate equity market, the data set used in this paper allow a further look at the proposition that market aggregate uncertainty should have varying impact on individual stock variance. Design/methodology/approach – GARCH-M models estimate individual stock returns belonging to the DJIA in 2011 on its lags and on the ARCH-M term in the mean equation linking stock returns to the variance equation. The longest time span has 5,738 observations for most stocks under daily frequency from January 3, 1990 to December 30, 2011. The authors use one lag for the VIX2 term to address simultaneity problems in the variance equation. In order to allow for interactions between volatility and business cycles, the authors include a dummy variable for the three recessions identified by the NBER over the period. Findings – Adding the “fear gauge” VIX index and a dummy variable for recessions to the variance equation in GARCH-M models, the VIX coefficient always increases variance and the recession dummy has mixed effects. Overall, VIX acts as expected as mixing variable. Supporting the mixture of distribution hypothesis, the impact of VIX is always positive (1.039 on market variance) and GARCH effects vanish completely for the index and almost as much for 24 stocks. Research limitations/implications – In theory, the effects of VIX on stock variance should be positive and statistically significant, together with reductions of GARCH persistence. The authors find this to be the case for the aggregate stock market and for 24 out of its 29 DJIA stocks. The authors leave for further work extensions to estimating the variance equation for companies very exposed to idiosyncratic changes, such as oil price fluctuations or stock buybacks. The implication of this research for the academic or financial community relies on the estimation of VIX effects on individual stock variance, controlling for business cycles. Originality/value – Due to its benchmark in equities, stocks in the Dow Jones Industrials make it a very interesting case study. This paper reconsiders the aggregate uncertainty hypothesis for two main reasons. First, the financial press and traders keep a very close track on the daily evolution of VIX. Second, recent research emphasizes the formal predictive power of VIX in US stock markets. For the variance equation, existing works report positive values for the VIX-coefficient on the S&P 500 index but they have not examined individual stocks as the authors do in this paper.

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Vuong, Ngoc Bao, and Yoshihisa Suzuki. "Does Fear has Stronger Impact than Confidence on Stock Returns? The Case of Asia-Pacific Developed Markets." Scientific Annals of Economics and Business 67, no. 2 (2020): 157–75. http://dx.doi.org/10.47743/saeb-2020-0009.

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Employing data from Australia, Hong Kong, and Japan over the period between January 2004 to December 2017, this study investigates the relationship between investor sentiment and stock returns. We analyze two reversed sentiment indicators, namely Consumer Confidence Index (CCI) and Volatility Index (VIX), in two conversing situations: low and high sentiment. The empirical evidence suggests that sentiment has a significant link with concurrent returns, but its influence seems to wipe out quickly as the little to no return predictability is detected. More importantly, we find that “investor fear gauge” (VIX) generates a more significant contemporaneous effect on market returns than investor confidence. The impact on future returns, on the contrary, is inconclusive since low CCI and VIX dominate the opposite ones most of the time.

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Cheuathonghua, Massaporn, Chaiyuth Padungsaksawasdi, Pattana Boonchoo, and Jittima Tongurai. "Extreme spillovers of VIX fear index to international equity markets." Financial Markets and Portfolio Management 33, no. 1 (2019): 1–38. http://dx.doi.org/10.1007/s11408-018-0323-6.

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Huang, Hung-Hsi, Shin-Hung Lin, and Chiu-Ping Wang. "Reasonable evaluation of VIX options for the Taiwan stock index." North American Journal of Economics and Finance 48 (April 2019): 111–30. http://dx.doi.org/10.1016/j.najef.2019.01.016.

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Baba, N., and Y. Sakurai. "Predicting regime switches in the VIX index with macroeconomic variables." Applied Economics Letters 18, no. 15 (2011): 1415–19. http://dx.doi.org/10.1080/13504851.2010.539532.

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Shaikh, Imlak, and Puja Padhi. "Inter-temporal relationship between India VIX and Nifty equity index." DECISION 41, no. 4 (2014): 439–48. http://dx.doi.org/10.1007/s40622-014-0046-0.

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41

Johnson, Travis L. "Risk Premia and the VIX Term Structure." Journal of Financial and Quantitative Analysis 52, no. 6 (2017): 2461–90. http://dx.doi.org/10.1017/s0022109017000825.

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The shape of the Chicago Board Options Exchange Volatility Index (VIX) term structure conveys information about the price of variance risk rather than expected changes in the VIX, a rejection of the expectations hypothesis. The second principal component, SLOPE, summarizes nearly all this information, predicting the excess returns of synthetic Standard & Poor’s (S&P) 500 variance swaps, VIX futures, and S&P 500 straddles for all maturities and to the exclusion of the rest of the term structure. SLOPE’s predictability is incremental to other proxies for the conditional variance risk premia, economically significant, and inconsistent with standard asset pricing models.

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

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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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Janardan, Shriya. "Evidence of Fear in Fixed Income and Bourses: A Study on Certain G-7 Economies." Ushus - Journal of Business Management 18, no. 3 (2019): 1–12. http://dx.doi.org/10.12725/ujbm.48.1.

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The paper aimed to predict the Fear index for certain G7 countries (Canada, France, Germany and Japan) considering the two variables Stock Price (Close) and Bond Yield(LBY). Daily data were analyzed for the period from April 2013 to June 2017. The main purpose was to identify the degree in which fear affecting the stock market percolates to Fixed Income Instruments. Using Panel Data Regression (Fixed Effect Model) the two variables were able to predict the VIX index and the model was found to be robust in nature. The major finding is that Fixed Income and stocks share a negative relationship with VIX (Fear Index).

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Tsuji, Chikashi. "An Investigation of the Predictive Speed of the UK VIX for the Downside Risk in European Equity Markets." International Business Research 11, no. 12 (2018): 18. http://dx.doi.org/10.5539/ibr.v11n12p18.

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Using the time-series data of UK volatility index (VIX) and other four European equity indices of France, Italy, Spain, and Portugal, and applying quantile regressions, this study investigates the predictive power and predictive speed of the UK VIX for the future sharp price drops in other four European equity markets. As a result, our empirical examinations derive the following findings. (1) First, we clarify that the increases of the UK VIX have statistically significant predictive power for the downside risk in other four European equity markets. (2) Second, our empirical results reveal that the two to four days before, the changes in the UK VIX can forecast the downside risk in other four European equity markets.

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45

Ruan, Lei. "Research on Sustainable Development of the Stock Market Based on VIX Index." Sustainability 10, no. 11 (2018): 4113. http://dx.doi.org/10.3390/su10114113.

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The frequent occurrence of financial crises has made the dynamic linkage between international financial markets an important research topic. In the past, scholars mostly studied the correlation between financial markets directly, however ignored the impact of exogenous financial variables on financial markets. The stock market is an important part of the financial market and plays an important role in the overall economy. Information asymmetry is common and has a certain degree of impact on investors’ returns. However, many scholars believe that the problem of information asymmetry in China has seriously negatively impacted investors, forming an unsustainable state. At present, there are still many problems in the Chinese stock market, especially the stock market fraud, which brings great challenges to the sustainable development of the stock market. Based on the idea of the STCC model, it is assumed that the Copula parameter is affected by the exogenous variables and the time-varying dynamic Copula model-ST-VCopula model is established. Based on the model, the influence of market volatility (VIX index) on the stock market is explored and then the stock index data of several countries are empirically analyzed. The empirical results show that the VIX index has a significant impact on the linkage between stock markets. The VIX index is easy and more intuitive to obtain, providing another way for the dynamic linkage research between the market, which can provide investors with some guidance and advice when conducting financial activities such as diversification.

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46

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 (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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OROSI, GREG. "A NOVEL METHOD FOR ARBITRAGE-FREE OPTION SURFACE CONSTRUCTION." Annals of Financial Economics 14, no. 04 (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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Singh, Amanjot. "On the Linkages between India VIX and US Financial Stress Index." Theoretical Economics Letters 06, no. 01 (2016): 68–74. http://dx.doi.org/10.4236/tel.2016.61009.

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Shaikh, Imlak, and Puja Padhi. "Macroeconomic Announcements and the Implied Volatility Index: Evidence from India VIX." Margin: The Journal of Applied Economic Research 7, no. 4 (2013): 417–42. http://dx.doi.org/10.1177/0973801013500168.

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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 (2019): 1641063. http://dx.doi.org/10.1080/23322039.2019.1641063.

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