Future Stock Market Volatility Research Articles

A Machine Learning-Based Early Warning System for the Housing and Stock Markets

This study analyzes the relationship between the housing and stock markets, focusing on housing market bubbles. Stock market dynamics generally have a more significant impact on housing price movements than housing market dynamics have on stock dynamics. However, if housing market information is provided as a signal, housing price movements can predict stock market volatility. Accordingly, we build a machine learning-based early warning system (EWS) for the housing market using a long short-term memory (LSTM) neural network. Applying the generalized supremum augmented Dickey-Fuller test to extract the bubble signal in the housing market, we find that the signal simultaneously detects future changes in the housing market prices and future stock market volatility, and our EWS effectively detects the bubble signal. We confirm that the LSTM approach performs better than other benchmark models, the random forest and support vector machine models.

DOES POLICY UNCERTAINTY AFFECT EQUITY, COMMODITY, INTEREST RATES, AND CURRENCY MARKETS? EVIDENCE FROM CBOE’S VOLATILITY INDEX

Economic policy drives investment, production, employment, and other macroeconomic indicators of the economy. The study examines the equity, commodity, interest rates, and currency markets, taking into consideration the US economic policy uncertainty (EPU) index. The present work determines the association among policy uncertainty and volatility index, expressed in terms of generalized autoregressive conditional heteroscedasticity and period of empirical work spanning from 2000 to 2018. The results suggest that equity markets’ volatility tends to be very high based on a high degree of policy uncertainty. The findings on the commodity market indicate that crude oil and gold prices remain more volatile during the presidential election and financial crisis. One of the essential results shows that the 2000s boom, early credit crunch, Lehman’s collapse and recession, and fiscal policy battles have significantly affected the equity, currency, and commodity markets. The interest rates and currency markets have responded considerably to Feds’ and EPU index. The empirical outcome provides evidence that implied volatility index is a forward looking expectation of future stock market volatility, and it uncovers that policy uncertainty affects investor sentiment. The present work holds some practical implications for the government to formulate policies to regulate the US market.

Can happiness predict future volatility in stock markets?

In this paper, we use the Twitter based happiness index as a proxy for investor sentiment in order to examine whether happiness influences future market volatility of country VIX indexes. Our sample includes the major stock markets of the USA, Canada, UK, Germany, France, Netherlands, Switzerland, Japan, China, Hong Kong, India, Brazil, South Korea, and South Africa. Using linear and nonlinear causality tests, we find that Twitter happiness significantly causes the future volatility of the sample countries. The robustness checks show no divergence from our primary findings and provide strong evidence of a nonlinear relationship between investor sentiment and future stock market volatility.

Information Contents of Order Flow Toxicity in the Options Market : The Case of KOSPI200 Index Options

This study estimates the VPIN (volume-synchronized probability of informed trading) of the KOSPI200 index options, the measure of order flow toxicity suggested by Easley et al. (2012), for the first time. To apply the VPIN approach, options are categorized by their real-time moneyness. I examine the predictive power of VPIN for the future stock market volatility using time-series regression analysis. The empirical result shows that the toxic order flow measure estimated by price changes has more information than that estimated by the actual order imbalance. In general, put options contain more information than call options, and the toxic order flow measure of OTM (out-of-the-money) put options contains the most significant information about the future stock market volatility. In addition, the predictive power of toxic order flow measure is much significant in the highly volatile than in the stable market. The volatility predictability of toxic order flow measure declined significantly after the option multiplier increase, whereas it has gradually recovered over time.

Volatility spillovers between crude oil and Chinese sectoral equity markets: Evidence from a frequency dynamics perspective

We examine the frequency dynamics of volatility spillovers between crude oil and China's stock markets in a spectral representation framework of generalized forecast error variance decomposition using sectoral stock indices data. We find evidence of total volatility spillover driven mainly by short-term spillovers. The net spillovers of the oil market are almost all positive and dominated by short-ter.m components, although the spillover during China's 2015 financial crisis is negative and attributable to long-term components. In addition, there exists heterogeneity in net pairwise (frequency) spillovers between the oil and sectoral stock markets. Moreover, structural breaks in volatilities appear to be a significant feature of volatility spillovers. Finally, frequency spillovers in our system can predict future stock market volatility. These results have economic implications for investors and policymakers.

Efficiency of Indian Option Market: Estimation of Future Market Volatility Using Implied Volatility

Forecasting volatility is a key process in pricing stock and index options. Accurate forecasting of future volatility would facilitate the traders and investors to make an informed decision. The study examines the market efficiency of exchange traded index options in India. We investigate the predictive power of implied volatility of Nifty index options in forecasting the future stock market volatility. The efficient market hypothesis believes that the implied contains all past information, thereby making it a superior volatility forecast for the underlying asset. Our study is based on the implied volatility of Nifty index options for the years between 2010 and 2018. In this paper, we compare the accuracy of expected future volatility using implied volatility concerning historical volatility. We study the implied volatility for Nifty 50 index option over the last seven years and compare the results against the ARMA model and historical forecasts to re-establish the superiority of implied volatility and efficiency of the Indian option market.

Shiller's CAPE: Market Efficiency and Risk

AbstractRobert Shiller shows that Cyclically Adjusted Price to Earnings Ratio (CAPE) is strongly associated with future long‐term stock returns. This is often interpreted as evidence of market inefficiency. We present two findings contrary to such an interpretation. First, if markets are efficient, stock returns should be higher than the risk‐free rate. We find that even when CAPE is in its ninth decile, future 10‐year stock returns, on average, are higher than future returns on 10‐year U.S. Treasurys. Thus, the results are largely consistent with market efficiency. Second, consistent with a risk–return tradeoff, we find that CAPE is negatively associated with future stock market volatility.

A Study on the Relationship between the Volatility Index and the Equity Index with reference to S&P 500 and Nifty

The study examines the asymmetric and inter temporal relationship between India Volatility Index and select equity index returns. The empirical evidences in the study reveals that there prevails an asymmetric relationship among India VIX and select equity index and at the same time the magnitude of asymmetry is not identical. The study also reveals that the current values of change in volatility and stock returns are negatively correlated, and past and current stock returns are positively associated with the future stock market volatility.

Stock price synchronicity and its effect on stock market volatility: evidence from the MENA region

This study investigates whether stock price synchronicity contains information regarding future stock market volatility. More specifically, this paper answers three important questions: 1) Does historic stock price synchronicity affect stock market volatility?; 2) If it does, how much of the volatility is explained by synchronicity?; 3) Does the impact of unexpected shocks on stock market volatility depend on historic synchronicity? Using the data from MENA region (Morocco, Tunisia, Egypt, United Arab Emirates, Jordan, Oman, and Bahrain), we document significantly positive relationship between stock price synchronicity and stock market volatility during the period between 2005 and 2010. We show that, whether stocks co-move downward or co-move upward, it causes stock market volatility to go up significantly. Our results are significant across all markets. We also show that synchronous component of volatility can, at times, completely explain stock market volatility. Furthermore, we also show that the impact of unexpected shocks on stock market volatility is an increasing function of stock price synchronicity.

Stock price synchronicity and its effect on stock market volatility: evidence from the MENA region

This study investigates whether stock price synchronicity contains information regarding future stock market volatility. More specifically, this paper answers three important questions: 1) Does historic stock price synchronicity affect stock market volatility?; 2) If it does, how much of the volatility is explained by synchronicity?; 3) Does the impact of unexpected shocks on stock market volatility depend on historic synchronicity? Using the data from MENA region (Morocco, Tunisia, Egypt, United Arab Emirates, Jordan, Oman, and Bahrain), we document significantly positive relationship between stock price synchronicity and stock market volatility during the period between 2005 and 2010. We show that, whether stocks co-move downward or co-move upward, it causes stock market volatility to go up significantly. Our results are significant across all markets. We also show that synchronous component of volatility can, at times, completely explain stock market volatility. Furthermore, we also show that the impact of unexpected shocks on stock market volatility is an increasing function of stock price synchronicity.

Dynamics of the Relationship between Implied Volatility Indices and Stock Prices Indices: The Case of European Stock Markets

This paper examines the relationship between implied volatility indices and stock price indices in the case of five European market : Euro zone, France, Germany, Switzerland and the United Kingdom for the period from January 2010 to March 2015. To achieve that, two empirical models were estimated. Using GARCH modelling, our results show clearly that implied volatility indices contain relevant information concerning future stock market volatility, while this information is still insufficient in predicting the latter.? A multiple linear regression procedure confirmed the existence of a strong negative and asymmetrical relationship between the implied volatility indices and stock market returns for three studied markets.

Shiller's CAPE: Market Efficiency and Risk

Robert Shiller shows that Cyclically Adjusted Price to Earnings Ratio (CAPE) is strongly associated with future long-term stock returns. This result has often been interpreted as evidence of market inefficiency. We present two findings that are contrary to such an interpretation. First, if markets are efficient, returns on average, even when conditional on CAPE, should be higher than the risk-free rate. We find that even when CAPE is in its ninth decile, future 10-year stock returns, on average, are higher than future returns on 10-year Treasurys. Thus, the results are largely consistent with market efficiency. Only when CAPE is very high, say, CAPE is in the upper half of the tenth decile (CAPE higher than 27.6), future 10-year stock returns, on average, are lower than those on 10-year U.S. Treasurys. Second, we provide a risk-based explanation for the association between CAPE and future stock returns. We find that CAPE and future stock returns are positively associated with future stock market volatility. Overall, CAPE levels do not seem to reflect market inefficiency and do reflect risk (volatility).

A Plain English Measure of Financial Reporting Readability

Over the past decade, a burgeoning literature has emerged using textual analysis to examine the impact of financial reporting readability on capital markets. Despite the rapid growth of this literature, clear guidance on measuring readability in financial texts is rather limited. In this study, we introduce a new measure of readability, the Bog Index, which is designed to capture the plain English attributes of disclosure (e.g., active voice, fewer hidden verbs). We then validate this new measure based on a combination of regulatory guidance, experiments, and archival tests using a regulatory intervention related to the readability of prospectus filings. After validating this measure, we next examine the extent to which different readability measures are associated with future stock market volatility and equity analysts’ earnings forecast properties. We find that the Bog Index has nearly a twenty-five percent greater association with future stock return volatility than any other measure of readability. In contrast, only quantity-based measures of disclosure (e.g., total words, file size) are associated with analyst forecast accuracy. Combined, this evidence suggests that financial statement users may be affected by different attributes of readability. Finally, we use both archival and experimental evidence to demonstrate the importance of understanding the underlying drivers of quantity-based measures of readability. In particular, we caution researchers that a vast amount of the variation in the file size measure over time is driven by the inclusion of content unrelated to the underlying text in the 10-K (e.g., HTML, XML, PDFs).

The implied volatility index: Is ‘investor fear gauge’ or ‘forward-looking’?

The paper aims to examine implied volatility as the investor fear gauge or/and forward-looking expectation of future stock market volatility within emerging markets setting-India VIX. The earliest results evidenced that VIX is the gauge of investor fear, where in the expected stock market volatility rises when the given market is declined. It is also proven that expected volatility is being unbiased estimate of the actual return volatility (30-calendar days); hence, during the market turmoil VIX likely to be biased. Lastly, it is suggested that the nervousness of investor yields potential profit to the options seller (market crises). Thus, our research has practical implications for various reasons, such as, portfolio risk management, stock market volatility forecast, and options pricing.

Inter-temporal relationship between India VIX and Nifty equity index

This study investigates the contemporaneous inter-temporal relationship between implied volatility index and stock returns. The empirical evidence reveals that an asymmetry prevails among India VIX and the Nifty index; at the same time, the magnitude of asymmetry is not identical. The results show that the change in India VIX occurs bigger for the negative return shocks than that of positive return shocks. The empirical model described that long-run inter-temporal contemporaneous relation persists between the implied volatility and stock market returns. Moreover, the cross correlation has supported the past literature that current values of change in volatility and stock returns are negatively correlated, and past and current stock returns are positively associated with the future stock market volatility. The magnitude of the change of volatility in response to the return variation, one can use that level of changes as one of the inputs for the pricing of future options.

The Impact of Stock Market Volatility Expectations on Investor Behavior: Evidence From Aggregate Mutual Fund Flows

We find that aggregate net equity fund flows are strongly negatively correlated with changes in expected future stock market volatility as measured by the VIX. Implying that investor purchase decisions are primarily driven by returns and sale decisions by risk perceptions, we further find that changes in the VIX primarily impact aggregate equity mutual fund redemptions with little impact on inflows while stock market returns impact inflows but not outflows. We also find that changes in the VIX are positively correlated with the degree of portfolio rebalancing. While the impact on outflows is much larger, both fund inflows and outflows tend to increase (decrease) when the VIX increases (decreases). Aside from possibly the change in civilian employment, we find no evidence that macroeconomic variables and consumer sentiment have an independent impact on mutual fund flows after controlling for the VIX and stock market returns.

Monetary Policy And Stock Returns: The Case Of Turkey

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-size: 10pt; mso-bidi-font-size: 11.0pt; mso-ansi-language: EN-US;"><span style="font-family: Times New Roman;">This paper <span style="color: black;">investigates how much of the variance in stock returns can be explained by monetary policy for the case of Turkey. </span>We extend the work of Ewing (2001a) for the case of Turkey by using the newly developed generalized forecast error variance decomposition technique [Koop et al. (1996), Pesaran and Shin (1998)]. Results suggest that the growth rate of money supply contain significant information <span style="color: black;">for predicting</span> variance of future forecast errors of stock returns. The results provide information which is important for building accurate asset pricing models, forecasting future stock market volatility and furthers our understanding of stock market behavior in Turkey.<span style="color: black;"></span></span></span></p>

The information content of implied volatility: Evidence from Australia

AbstractThis study develops an implied volatility index for the Australian stock market, termed as the AVX, and assesses its information content. The AVX is constructed using S&P/ASX 200 index options with a constant time‐to‐maturity of three months. It is observed that the AVX has a significant negative and asymmetric relationship with S&P/ASX 200 returns. When evaluating the forecasting power of the AVX for future stock market volatility, it is found that the AVX contains important information both in‐sample and out‐of‐sample. In‐sample, the AVX significantly improves the fit of a GJR‐GARCH(1, 1) model. Out‐of‐sample, the AVX significantly outperforms the RiskMetrics approach and the GJR‐GARCH(1, 1) model, with its highest forecasting power at the one‐month forecasting horizon. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:134–155, 2010

VOLATILITY FORECASTS, TRADING VOLUME, AND THE ARCH VERSUS OPTION‐IMPLIED VOLATILITY TRADE‐OFF

AbstractWe investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t−1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t−1 relative to the recent past, option‐implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option‐implied forward‐looking estimate.

Is implied volatility an informationally efficient and effective predictor of future volatility?

We examine how well implied volatility forecasts future stock market volatility. If markets are efficient and the option pricing model is correct, the implied volatility calculated from option prices should be an unbiased and informationally efficient predictor of future volatility; that is, it should correctly impound all available information, including the asset’s price history. However, numerous studies have found that the implied volatility forecast is biased and/or is not informationally efficient. We re-examine this issue using S&P500 futures options data, a more active market which is less susceptible to measurement error than the OEX options market considered in most previous studies. Our findings are that, first, implied volatility has strong predictive power and generally subsumes the information in historical volatility; second, prediction and efficiency results are quite sensitive to the forecasting horizon and whether the data period covers the October 1987 stock market crash; and third, at least in the S&P500 options market, measurement error due to such factors as bid–ask spreads, non-synchronous price observations and discrete prices does not significantly affect the results.