Forecasting stock market volatility and information content of implied volatility index

This study investigates the incremental information content of implied volatility index relative to the GARCH family models in forecasting volatility of the three Asia-Pacific stock markets, namely India, Australia and Hong Kong. To examine the in-sample information content, the conditional variance equations of GARCH family models are augmented by incorporating implied volatility index as an explanatory variable. The return-based realized variance and the range-based realized variance constructed from 5-min data are used as proxy for latent volatility. To assess the out-of-sample forecast performance, we generate one-day-ahead rolling forecasts and employ the Mincer–Zarnowitz regression and encompassing regression. We find that the inclusion of implied volatility index in the conditional variance equation of GARCH family model reduces volatility persistence and improves model fitness. The significant and positive coefficient of implied volatility index in the augmented GARCH family models suggests that it contains relevant information in describing the volatility process. The study finds that volatility index is a biased forecast but possesses relevant information in explaining future realized volatility. The results of encompassing regression suggest that implied volatility index contains additional information relevant for forecasting stock market volatility beyond the information contained in the GARCH family model forecasts.     

Interest-rate volatility and volatility spillovers in emerging Europe

While many transition economies – particularly those that hope to join the Euro – have seen their economies converge to Europe’s, this process is by no means complete. Considerable macroeconomic volatility persists. This study examines the variability of the short-term nominal interest rates of ten transition economies, finding that eight of them exhibit time-varying volatility that can be modeled as a GARCH or Exponential GARCH process. Incorporating various measures of external volatility into the models, we find that those economies with fixed or managed exchange rates tend to experience more volatility spillovers, particularly from the Eurozone, regardless of the degree of transition. Only Estonia has a fixed exchange rate and remains free of international contagion.     

An empirical analysis of the US stock market and output growth volatility spillover effects on three Anglo-Saxon countries

This paper examines the dynamic and switching effects of volatility spillovers arising from US stock market returns and GDP growth on those of Australia, Canada and the UK. For this purpose, we use quarterly data (1961q1–2013q1) and a constant probability Markov regime switching model. We found that the US stock market volatility significantly affects the stock market volatility of all three countries at least in one of the two specified regimes over time. However, the stock market volatilities in none of the three countries are contemporaneously influenced by the US output volatility even after allowing for two distinct regimes. On the other hand, the US stock market volatility exerts significant influences on the output volatilities of both Australia and the UK. Compared with Australia and the UK, Canada and the US show substantial output volatility co-movements, thereby confirming the close association between the two neighbouring economies through the NAFTA (North American Free Trade Agreement). We conclude that shocks emanating from the US stock market have unequivocal flow-on effects on the output and return volatilities of the other economies.     

Investigating Intraday Interdependence Between Gold,Silver and Three Major Currencies: the Euro,British Pound and Japanese Yen

In this study, the interrelationship between major exchange rate returns (namely EUR/USD, GBP/USD, JPY/USD) and precious metal returns (gold and silver) is examined using a vector autoregressive model in a multivariate asymmetric GARCH framework on the intraday frequency. Our findings indicate a unidirectional volatility transmission from the majority of our currencies (EUR/USD, GBP/USD) to precious metals. The sluggish response of silver volatility to currency volatility shocks permits implementation of intraday profitable strategies, providing implications against market efficiency when analyzing intraday data. In the case of the British pound and Japanese yen, a volatility shock affects silver volatility more than gold volatility. Crisis events such as the Greek default and US credit rating downgrade reduce significantly the correlation of EUR/USD and gold/silver. The covariance between EUR/USD and silver increases after a volatility shock in EUR/USD. The same happens with JPY/USD and silver. These findings are important for portfolio managers and monetary authorities.     

Modelling the impact of central bank intervention on exchange rate volatility under inflation targeting

This article empirically investigates the effect of central bank’s foreign exchange interventions on the level and volatility of the Uganda shilling/US dollar exchange rate (UGX/USD) under an inflation-targeting regime. Utilizing daily data spanning the period 1 September 2005, to 31 December 2015, we estimate a foreign exchange intervention model within a GARCH theoretic framework. Empirical results indicate that foreign exchange interventions have had mixed impact on the volatility of the exchange rate. We find that inflation targeting is capable of curbing temporary exchange rate shocks. Empirical results indicate that while order flow is capable of reducing exchange rate volatility, an increase in the operating target rate, the 7-day interbank rate tends to exacerbate exchange rate volatility. Our empirical results are robust to alternative model specifications. We argue that inflation targeting is an effective monetary policy tool for curbing exchange rate volatility.     

On the asymmetric impact of macro–variables on volatility

We extend the GARCH–MIDAS model to take into account possible different impacts from positive and negative macroeconomic variations on financial market volatility: a Monte Carlo simulation which shows good properties of the estimator with realistic sample sizes. The empirical application is performed on the daily S&P500 volatility dynamics with the U.S. monthly industrial production and national activity index as additional (signed) determinants. We estimate the Relative Marginal Effect of macro variable movements on volatility at different lags. In the out-of-sample analysis, our proposed GARCH–MIDAS model not only statistically outperforms the competing specifications (GARCH, GJR-GARCH and GARCH–MIDAS models), but shows significant utility gains for a mean-variance investor under different risk aversion parameters. Attention to robustness is given by choosing different samples and estimating the model in an international context (six different stock markets).     

S&P 500 volatility,volatility regimes,and economic uncertainty

We assess the relationship between regime-dependent volatility in S&P 500, economic policy uncertainty, the S&P 500 bull and bear sentiment spread (bb_sp), as well as the Chicago Board Options Exchange's VIX over the period 2000–2018. Our findings from two-covariate GARCH–MIDAS (GM) methodology, regime switching Markov Chain, and quantile regressions suggest that the association of realized volatility and sentiment varies across high- and low-volatility regimes and depends on investors’ sensitivity toward incidents of market uncertainties under these regimes. The findings suggest that these indicators may not be useful in volatility forecasting, especially under high-volatility regimes.     

On the predictive power of ARJI volatility forecasts for Bitcoin

Motivated by the recent literature on cryptocurrency volatility dynamics, this paper adopts the ARJI, GARCH, EGARCH, and CGARCH models to explore their capabilities to make out-of-sample volatility forecasts for Bitcoin returns over a daily horizon from 2013 to 2018. The empirical results indicate that the ARJI jump model can cope with the extreme price movements of Bitcoin, showing comparatively superior in-sample goodness-of-fit, as well as out-of-sample predictive performance. However, due to the excessive volatility swings on the cryptocurrency market, the realized volatility of Bitcoin prices is only marginally explained by the GARCH genre of employed models.     

FINANCIAL CRISES AND INTERNATIONAL STOCK MARKET VOLATILITY TRANSMISSION

This paper examines the interplay between stock market returns and their volatility, focusing on the Asian and global financial crises of 1997–98 and 2008–09 for Australia, Singapore, the UK, and the US. We use a multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model and weekly data (January 1992–June 2009). Based on the results obtained from the mean return equations, we could not find any significant impact on returns arising from the Asian crisis and more recent global financial crises across these four markets. However, both crises significantly increased the stock return volatilities across all of the four markets. Not surprisingly, it is also found that the US stock market is the most crucial market impacting on the volatilities of smaller economies such as Australia. Our results provide evidence of own and cross ARCH and GARCH effects among all four markets, suggesting the existence of significant volatility and cross volatility spillovers across all four markets. A high degree of time‐varying co‐volatility among these markets indicates that investors will be highly unlikely to benefit from diversifying their financial portfolio by acquiring stocks within these four countries only.     

Does VIX or volume improve GARCH volatility forecasts?

This article considers whether the inclusion of two additional variables can improve volatility forecasts over a standard GARCH-based model. We consider three alternative ways of incorporating the volatility index (VIX) and trading volume as exogenous variables within a selection of GARCH models. We are particularly interested in whether these variables have additional incremental forecast power over and above the baseline GARCH specification. Our results suggest that both the VIX and volume do provide some additional forecast power, and this is generally improved when considering both of these series jointly in the model. However, while the results may be statistically significant the gain is marginal and the coefficient values small. Moreover, in a horse race exercise VIX does not outperform the GARCH approach. In answering the question of whether VIX produces better forecasts than the GARCH model, then the answer is no, but the informational content of VIX cannot be ignored and should be incorporated into forecast regressions.     

Forecasting Irregularly Spaced UHF Financial Data: Realized Volatility vs UHF-GARCH Models

A new literature has been recently devoted to the modeling of ultra-high-frequency (UHF) data. Our first aim is to develop an empirical application of UHF-GARCH models to forecast future volatilities on irregularly spaced data. We also compare the out-sample performance of these generalized autoregressive conditional heteroskedastic (GARCH) models with the realized volatility method. We propose a procedure to account for the time deformation problem and show how to use these models for computing daily Value at Risk (VaR).     

Modelling conditional moments and correlation with the continuous hidden-threshold-skew-normal distribution

A key issue in modelling conditional densities of returns of financial assets is the time-variation of conditional volatility. The classic econometric approach models volatility of returns with the generalized autoregressive conditional heteroscedasticity (GARCH) models where the conditional mean and the conditional volatility depend only on historical prices. We propose a new family of distributions in which the conditional distribution depends on a latent continuous factor with a continuum of states. The distribution has an interpretation in terms of a mixture distribution with time-varying mixing probabilities. The distribution parameters have economic interpretations in terms of conditional volatilities and correlations of the returns with the hidden continuous state. We show empirically that this distribution outperforms its main competitor, the mixed normal conditional distribution, in terms of capturing the stylized facts known for stock returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence.     

Volatility and Spillover Effects of Yen Interventions

We consider the effects of interventions by the Bank of Japan's (BoJ) on the intraday volatility of the US dollar/Japanese yen (USD/JPY) exchange rates and their spillovers to volatility of the euro/JPY exchange rates. We use 15‐minute data during the period 2000–2004 and employ multivariate generalized autoregressive conditional heteroskedasticity (GARCH) modeling and quartile plots of intraday volatility to analyze the intraday effects of the BoJ interventions on exchange rate volatility. The results indicate that the BoJ interventions decrease daily volatility of the USD/JPY exchange rate but increase the volatility of the euro/JPY series. On intervention days, the intraday volatility has different patterns to those on non‐intervention days.     

A common factor of stochastic volatilities between oil and commodity prices

This article analyses the multivariate stochastic volatilities (SVs) with a common factor influencing volatilities in the prices of crude oil and agricultural commodities, used for both biofuel and nonbiofuel purposes. Modelling the volatility is crucial because the volatility is an important variable for asset allocation, risk management and derivative pricing. We develop a SV model comprising a latent common volatility factor with two asymptotic regimes with a smooth transition between them. In contrast to conventional volatility models, SVs are generated by the logistic transformation of latent factors, which comprise two components: the common volatility factor and an idiosyncratic component. We present a SV model with a common factor for oil, corn and wheat from 8 August 2005 to 10 October 2014, using a Markov chain Monte Carlo method to estimate the SVs and extract the common volatility factor. We find that the volatilities of oil and grain markets are persistent. According to the estimated common volatility factor, high volatility periods match the 2007–2009 recession and the 2007–2008 financial crisis quite well. Finally, the extracted common volatility factor exhibits a distinct pattern.     

Volatility forecasting using high frequency data: Evidence from stock markets

The paper aims to suggest the best volatility forecasting model for stock markets in Turkey. The findings of this paper support the superiority of high frequency based volatility forecasting models over traditional GARCH models. MIDAS and HAR-RV-CJ models are found to be the best among high frequency based volatility forecasting models. Moreover, MIDAS model performs better in crisis period. The findings of paper are important for financial institutions, investors and policy makers.     

Maturity and volatility effects on UK smiles

The smile effect is a result of an empirical observation of the options implied volatility with the same expiration date, across different exercise prices. However, its shape has been under discussion seeming to be dependent on the option underlying security. In this paper, and filling up a scarce empirical research on the topic, we used liquid equity options on 9 stocks traded on the London International Financial Futures and Options Exchange (LIFFE) between August 1990 and December 1991. We tested two different hypothesis for testing two different phenomena: (1) the increase of the smile as maturity approaches; (2) and the association between the smile and the volatility of the underlying stock. In order to estimate implied volatilities for unavailable exercise prices, we modelled the smile using cubic B-spline curves. We found empirical support for the smile intensification (the U-shape is more pronounced) as maturity approaches as well as when volatility rises. However, we found two major sources of disagreement with the literature on stochastic volatility models. First, as maturity approaches, out-of-the-money options implied volatility tends to be higher than the implied volatility of in-the-money options. Second, as the volatility of the underlying asset increases, the implied volatility of in-the-money options tends to be higher than implied volatility of out-of-the-money options.Received: September 2001, Accepted: September 2003, JEL Classification: G13Correspondence to: João L. C. DuqueWe thank Professor Dean A. Paxson (University of Manchester), António Sousa Câmara (University of Strathclyde), Ser-Huang Poon (University of Lancaster) and the attendees of the 26th EFA Annual Conference for helpful comments on previous versions of this paper. We also want to thank to two anonymous referees for their relevant comments and suggestions. Financial support granted by the Fundação para a Ciência e a Tecnologia (FCT) and the Programa Praxis XXI is gratefully acknowledged.     

The risk-return relation and VIX: evidence from the S&P 500

A significantly positive risk-return relation for the S&P 500 market index is detected if the squared implied volatility index (VIX) is allowed for as an exogenous variable in the conditional variance equation of the parsimonious GARCH(1,1) model. This result holds for both daily and weekly observations, for extended conditional mean and variance specifications, and is robust to sub-samples. We show that the conditional variance obtained from the GARCH model with VIX has better predictive ability for realized volatility than the conditional variance from GARCH without VIX and VIX itself, thereby documenting an important information content of VIX for conditional variance. The results are interpreted as evidence that adding VIX squared in the conditional variance equation yields a better measure of conditional variance which, subsequently, uncovers a strong risk-return relation.     

Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S.

We propose three Realized-GARCH-Kernel-type models which do not make the distribution assumptions on the return disturbance terms. We use this type of model to predict the return volatilities of the 50ETF in China and the S&P500 index in the U.S. The semiparametric kernel density estimator of our models, which captures the skewness, asymmetry and fat-tail of financial assets, performs well both statistically and economically. Our models have more predictive power than other eight comparable volatility models that need to pre-specify the distribution of the disturbance terms. Our results are robust to eight measures of realized volatility. Using option straddle strategies, we show that our models generate larger trading profits and greater Sharpe ratios than the other competing models.     

Does industrial concentration impact on the relationship between policies and volatility?

It is usually recommended that countries diversify their economies to guard against any negative shocks that might impact on one industry. However, previous research has not identified how concentration can impact on the effectiveness of macroeconomic policies. This paper attempts to evaluate the relationship between industrial concentration, policies and economic volatility for a sample of 147 countries for the period 1970 to 2005. The study reports that less concentrated countries tend to have lower rates of output, consumption and investment growth volatility. In addition, while trade and capital account openness variables alone tend to diminish economic volatility, in concentrated economies opening both the capital and trade account can increase economic volatility.     

Do Aussie markets smile? Implied volatility functions and determinants

If options are correctly priced, the interpretation of volatility in the Black–Scholes model (as identifying the volatility of the underlying asset) is violated. The empirical relation between the model ‘implied volatility’ and the degree to which the option is in-the-money (moneyness) has been reported as resembling a U-shape (or ‘smile’) for options on currencies (and more of a ‘smirk’ for options on equities). In this article, using multivariate time-series analysis and employing an impulse response function, we investigate the structural relationships and dynamics of the volatility smile in relation to the option liquidity, key features of the underlying asset and market momentum. Our findings confirm evidence of a number of biases in the Black–Scholes model consistent with Chou et al. (2011) in regard to liquidity in both the underlying and the option itself, and with Peña et al. (1999) as to the importance of the option time to maturity. As well as delineating such biases as they co-relate both with each other and with the underlying asset volatility and momentum, we find that the pronounced smile is related to the differential sensitivities of in-the-money and out-of-the-money options, which itself suggests an explanation for the characteristic smile shape.