Expected Market Volatility Research Articles

Study of Association between Volatility Index and Nifty using VECM

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.

Study of Association between Volatility Index and Nifty using VECM

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.

Dynamic Liquidity Preferences of Mutual Funds

This paper examines the relation between expected market volatility and open-end mutual funds’ liquidity preferences. Using a large panel of actively managed U.S. equity mutual funds, I show that mutual fund managers hold more cash and tilt their holdings more heavily towards liquid stocks during periods when expected market volatility is high. Cross-sectional tests suggest that the dynamic preferences for liquidity are driven by concerns over investor withdrawals during volatile times. Furthermore, I find evidence that this type of dynamic behavior leads to higher fund returns.

Investor Behavior under Changing MarketVolatility

This article analyzes the reaction of S&amp;P 500 Index returns to changes in implied volatility given by the CBOE Volatility Index (VIX), using daily data samples from 1990 to 2012. The authors found that in normal regimes increases (declines) in the expected market volatility result in lower (higher) subsequent stock market returns. Thus, investors enter into selling positions upon a perception of increased risk for their equity investments, while they enter into long positions when they perceive an improved environment for those investments. For extreme regimes, however, investor reaction to increasing risk is ambiguous. The authors find that VIX variation significantly influences investment strategies for holding periods up to one month. Additionally, they propose an investment rule for short-term-oriented investors. <b>TOPICS:</b>Mutual funds/passive investing/indexing, analysis of individual factors/risk premia

How Long is Too Long? Volatility-Based Holding Strategies for Leveraged Bull and Bear ETFs

Since their favorable introduction in the U.S. in 2006, leveraged bull and bear exchangetraded funds (ETFs) have provided short-term investors with the opportunity to express their directional views regarding a wide variety of indexes. However, unlike traditional unleveraged ETFs, leveraged ETFs are not intended to be used as long-term trading instruments. Instead, leveraged ETFs are designed to return a multiple of their benchmark index on a daily basis. Leveraged ETFs are structured only for short-term investors because these funds must be rebalanced each day to prevent leverage from becoming too excessive. This daily rebalancing complicates predicting long-term returns for leveraged ETFs due to both compounding and volatility. Using Morningstar return data and Chicago Board Options Exchange volatility index data, we investigate the effects of compounding and expected market volatility on specific longterm holding strategies for leveraged bull and bear ETF returns. We show that compounded leveraged returns over these holding periods are comparable to compounding the respective multiple of their underlying benchmark return with tracking error increasing over time and with leverage. Our results also show that expected market volatility has a significant effect on tracking error, after adjusting for expenses, and that this effect increases over time and with leverage. These results suggest that volatility indexes may be used by sophisticated investors to devise trading rules for long-term holding strategies for leveraged bull and bear ETFs.

Bad news bears

Purpose – This study aims to look into how volatility significantly impacts the tracking error for daily-rebalanced leveraged bull and bear ETFs. Design/methodology/approach – Using Morningstar return data and Chicago Board Options Exchange (CBOE) volatility index (VIX) data, the paper examines the daily tracking error for leveraged bull and bear ETFs. Tracking error is defined as the difference between the daily returns for a leveraged bull or bear ETF and the multiple of the daily return for that ETF's respective underlying benchmark index. Findings – Changes in the market VIX of the CBOE have a significant and opposite effect on the daily returns for both leveraged bull and bear ETFs. Furthermore, these effects are more pronounced for bear ETFs than similarly leveraged bull ETFs. Research limitations/implications – The sample period (June 19, 2006 to September 22, 2009) contains periods of extraordinarily high volatility. Considering that the VIX reached an all-time high during this period, the results may be time-period specific and may not translate to other time periods. Practical implications – The implication is that market timing may be feasible for enhancing daily returns for both leveraged bull and bear ETFs. However, any specific timing strategies go beyond the scope of this paper. Originality/value – In this study, the paper examined the effects of expected market volatility on the daily tracking error of leveraged bull and bear ETFs. Specifically, the paper performed multiple linear regression analysis using Morningstar return data for the ETFs and their underlying benchmark and CBOE VIX data. The findings suggest that market timing could be beneficial for increasing daily yields for leveraged and inverse ETFs.

Implied Systematic Moments and the Cross-Section of Stock Returns

Using the risk-neutral volatility and skewness computed from options on the S&P500, we show there is an asymmetric contemporaneous relation between stock returns and changes in implied market volatility and skewness. Changes in expected market volatility and skewness are cross-sectionally priced only when implied market volatility or skewness increases. All stocks have similar returns when implied systematic volatility and/or skewness are decreasing. The economic impact of stocks' sensitivities to changes in expected market skewness is almost twice as much as sensitivities to changes in expected market volatility. These findings highlight the importance of not only implied systematic volatility to investors, but also the sensitivity to changes in implied systematic skewness.

Evidence on Investor Behavior from Aggregate Stock Mutual Fund Flows

Utilizing monthly aggregate flow data for U.S. equity mutual funds over 1986-2008, we document several new findings on investor behavior. First, we find a strong negative relationship between changes in expected market volatility as measured by the VIX index and net equity fund flows. Second, we document several asymmetries in mutual fund investors’ buy-sell decisions. We find that the negative volatility - net flow relationship is entirely due to the effect of volatility on outflows. When volatility increases, equity fund inflows actually increase - just not as much as outflows. In contrast, returns on equity funds only impact inflows, i.e., when returns increase aggregate inflows rise but outflows do not slow. These findings imply that mutual fund investor purchase decisions are primarily driven by returns while redemption decisions are primarily driven by risk perceptions. Suggesting further compartmentalization in investor decision making, we also find an asymmetry in the investor reaction to risk in that both inflows and outflows increase significantly (with a much stronger effect on outflows) as the VIX increases, but there is little effect on either outflows or inflows when the VIX declines. Lastly, we find evidence of month-of-year flow patterns consistent with: 1) tax minimization, and 2) a tendency to reevaluate and rebalance portfolios at the turn-of-the-year.

Dynamic Liquidity Preferences of Mutual Funds

This paper examines the relation between expected market volatility and open-end mutual funds' liquidity preferences. Using a large panel of actively managed U.S. equity mutual funds, I show that mutual fund managers hold more cash and tilt their holdings more heavily towards liquid stocks during periods when expected market volatility is high. Cross-sectional tests suggest that the dynamic preferences for liquidity are driven by concerns over investor withdrawals during volatile times. Furthermore, I find evidence that this type of dynamic behavior leads to higher fund returns.

Liquidity Beyond the Best Quote: A Study of the NYSE Limit Order Book

We conduct a comprehensive analysis of the New York Stock Exchange (NYSE) limit order book at both the aggregate market level and the individual stock level based on a sample covering all the NYSE ordinary stocks. By providing detailed description of the limit order book, we show that it supplies considerable amount of liquidity beyond the best quote posted on the market. At the aggregate market level, we find volatility is a key factor that determines the liquidity provided by limit order book. When expected market volatility increases, the book becomes more dispersed and its liquidity provision decreases; this in turn leads to high realized volatility in the future. Past market movements, especially market declines, exert significant impact on the book as well. The stock-level analysis suggests that small stock's limit order book is more sensitive to volatility and market declines than that of large stocks, and volatility and returns mainly affect the systematic component of the liquidity provided by individual stock's limit order book.

Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility

This article introduces four models of conditional heteroscedasticity that contain Markov-switching parameters to examine their multiperiod stock-market volatility forecasts as predictions of options-implied volatilities. The volatility model that best predicts the behavior of the options-implied volatilities allows the Student-t degrees-of-freedom parameter to switch such that the conditional variance and kurtosis are subject to discrete shifts. The half-life of the most leptokurtic state is estimated to be a week, so expected market volatility reverts to near-normal levels fairly quickly following a spike.