Range-based Volatility Research Articles

The informative role of trading volume in an expanding spot and futures market

This paper investigates the information content of trading volume and its relationship with range-based volatility in the Indian stock market for the period 1995–2007. We examine the dynamics of the two variables and their respective uncertainties using a bivariate dual long-memory model. We distinguish between volume traded before and after the introduction of futures and options trading. We find that in all three periods the impact of both the number of trades and the value of shares traded on volatility is negative. This result is consistent with the argument that the activity of informed traders is inversely related to volatility when the marketplace has increased liquidity, an increasing number of active investors and high consensus among investors when new information is released. We also find that (i) the introduction of futures trading leads to a decrease in spot volatility, (ii) volume decreases after the introduction of option contracts and (iii) there are significant expiration day effects on both the value of shares traded and volatility series.

On the usefulness of intraday price ranges to gauge liquidity in cap-based portfolios

We find that easy-to-observe price ranges are useful for estimating intraday liquidity. Following the literature on range-based volatility estimators, we go beyond the use of the closing price only and rely on the full range of prices. Based on high, low, opening, and closing (HLOC) prices, we show that a greater intensity in the price discovery process (as measured by the open–close range) and a higher level of price uncertainty (as captured by the High–Low range) lower ex-ante liquidity for small, mid, and large caps. Realized volatility (RV) fails to capture these effects. Although order books have become increasingly difficult to treat, there is some good news: it has never been easier to look at price ranges.

Analysis of Non-Synchronous Trading Effects on the Pricing of Exchange Traded Products

Exchange Traded Products (ETPs) have become important members of the investment universe. They are praised by institutional and retail investors alike for their low cost, transparency and efficient pricing mechanisms. ETPs trade much like equity securities but with a unique creation and redemption mechanism which typically aligns quoted prices with the Net Asset Value (NAV) of the underlying securities. This dissertation examines a class of ETPs whose underlying reference basket consists of securities listed on stock exchanges operating in a time zone different to the time zone of the ETP instrument itself, and whose currencies of the underlying securities are different to the currency of the ETP instrument. The ETP instruments reviewed comprise of the iShares MSCI Country Series and are all listed on the New York Stock Exchange (NYSE).The ETPs are classified into three groups depending on the degree of overlap between the exchange operating times on which their underlying securities are traded and the exchange operating times of the NYSE. These groups are non-synchronous for no overlapping hours, partially synchronous for some overlapping hours and synchronous for overlapping hours.By assessing a measure of range-based volatility during 15-minute intraday intervals throughout the NYSE trading day, an understanding of the volatility profile of these ETPs is determined and analysed. It is found that non-synchronous ETPs do exhibit a higher relative level of volatility when compared to the partially synchronous group. Within the partially synchronous group, evidence of a regime-shift is observed during the period when the market of the underlying securities transitions from open to closed during the NYSE trading session. Another factor observed in the relative volatility profile is the impact of foreign exchange translation. ETPs with underlying securities priced in an emerging market currency show higher relative levels of range-based volatility. However, both emerging market and developed market denominated securities baskets exhibit relatively higher levels of volatility during the opening and closing periods of the US trading day.The results point to the need for caution and understanding of the underlying reference basket when transacting in these ETPs as investors may inadvertently transact at a price which does not reflect the fair-market value of the underlying securities basket due to price distortions as a result of volatility.

A Component Markov Regime-Switching Autoregressive Conditional Range Model

In this paper, we develop a component Markov switching conditional volatility model based on the intraday range and evaluate its performance in forecasting the weekly volatility of the S&P 500 index. We compare the performance of the range-based Markov switching model with that of a number of well established return-based and range-based volatility models, namely EWMA, GARCH and FIGARCH models, the Markov Regime-Switching GARCH model of Klaassen (2002), the hybrid EWMA model of Harris and Yilmaz (2009), and the CARR model of Chou (2005). We show that the range-based Markov switching conditional volatility models produce more accurate out-of-sample forecasts, contain more information about true volatility, and exhibit similar or better performance when used for the estimation of value at risk.

Facts or fates of investors' losses during crises? Evidence from REIT-stock volatility and tail dependence structures

This study provides insight into diversification opportunities during the housing crisis by observing time-varying co-movements between real estate investment trust (REIT) and stock assets in 2000–2014. Out-of-sample forecasts, which are conducted according to the asset-allocation problem, reveal a swing in REIT-stock lower tail dependence during the global financial crisis. The lower-tail dependence exceeds 0.8, and thus implies stronger cross-asset linkages and fewer diversification opportunities in asset busts. These findings further suggest that the range-based volatility (asymmetric CARR) model is superior to the return-based (GJR-GARCH) framework, particularly as the global financial crisis prevails. Positive economic values of dynamic strategies are more evident during the crisis than pre- and post-crisis subperiods. The mortgage spread and VIX are informative in predicting REIT-stock tail dependences, and out-of-sample economic values improve as the mortgage and term spreads are considered.

A New Approach for Forecasting the Price Range With Financial Interval-Valued Time Series Data

This paper proposes an interval-based methodology to model and forecast the price range or range-based volatility process of financial asset prices. Comparing with the existing volatility models, the proposed model utilizes more information contained in the interval time series than using the range information only or modeling the high and low price processes separately. An empirical study of the U.S. stock market daily data shows that the proposed interval-based model produces more accurate range forecasts than the classic point-based linear models for range process, in terms of both in-sample and out-of-sample forecasts. The statistical tests show that the forecasting advantages of the interval-based model are statistically significant in most cases. In addition, some stability tests have been conducted for ascertaining the advantages of the interval-based model through different sample windows and forecasting periods, which reveals similar results. This study provides a new interval-based perspective for volatility modeling and forecasting of financial time series data.

Forecasting realized range volatility: a regime-switching approach

In this study, we introduce the regime changes to the heterogeneous autoregressive realized range-based volatility model (HAR-RRV). The different regimes are specified depending on the expectations of volatility of RRV volatility. Our out-of-sample findings indicate that considering regime switching can significantly improve the forecasting accuracy of volatility models.

Volatility on the Indian Stock Market: A Study

This study is carried out to understand the volatility behavior of the Indian stock market, taking into account the NSE as the role model. Historical volatility levels of CNX Nifty are computed using classical, range-based and drift independent volatility measures. It could be concluded that Yang Zhang’s measure of volatility is better and consistent in calculating the overall risk of the index under consideration.

Volatility forecast of country ETF: The sequential information arrival hypothesis

This paper uses daily data to examine whether the sequential information arrival hypothesis is supported in single country Exchange Traded Fund (ETF) market, and to model the forecast of ETF's volatility. The work is based on incorporating lagged trading volume into the ‘heterogeneous auto-regressive’ (HAR) model of regression realized range-based volatility (RRV) on realized range-based bi-power variance (RBV) (HAR–RRV–RBV-cum-Vol model, hereafter), in an attempt to improve the overall forecast of realized variance. We find that the forecasting performance of the HAR–RRV–RBV-cum-Vol model is better than other models for both in-sample and out-of-sample forecasts. The results support the sequential information arrival hypothesis in single country ETF market, by which lagged volume is available to predict current volatility.

A range-based volatility approach to measuring volatility contagion in securitized real estate markets

We use a newly-developed time-varying range-based volatility model to capture the dynamics of securitized real estate volatility. The novelty of the model is the use of a smooth transition copula function to capture the nonlinear comovements between major REIT markets in the presence of structural changes. We then investigate the impact of extreme events on the volatility dependence in a broad set of 13 developed countries over the period from 1990 to 2012. We find that information transmission through the volatility channel can exhibit either bi- or uni-directional causality. In addition, financial contagion following the subprime crisis is found between the U.S. and Australia.

An empirical model of fractionally cointegrated daily high and low stock market prices

This work provides empirical support for the fractional cointegration relationship between daily high and low stock prices, allowing for the non-stationary volatility of stock market returns. The recently formalized fractionally cointegrated vector autoregressive (VAR) model is employed to explain both the cointegration dynamics between daily high and low stock prices and the long memory of their linear combination, i.e., the range. Daily high and low stock prices are of particular interest because they provide valuable information about range-based volatility, which is considered a highly efficient and robust estimator of volatility. We provide a comparison of the Czech PX index with other world market indices: the German Deutscher Aktienindex (DAX), the U.K. Financial Times Stock Exchange (FTSE) 100, the U.S. Standard and Poor's (S&P) 500 and the Japanese Nihon Keizai Shimbun (NIKKEI) 225 during the 2003–2012 period, that is, before and during the financial crisis. We find that the ranges of all of the indices display long memory and are mostly in the non-stationary region, supporting the recent evidence that volatility might not be a stationary process. No common pattern is detected among all of the studied indices, and different behaviors are also observed in the pre-crisis and post-crisis periods. We conclude that the fractionally cointegrated VAR approach allowing for long memory is an interesting alternative for modelling range-based volatility.

An Empirical Study on Investment Performance using Properties of Realized Range-Based Volatility and Firm-Specific Volatility

An Empirical Study on Investment Performance using Properties of Realized Range-Based Volatility and Firm-Specific Volatility

Volatility transmission in energy futures markets

This study is novel in the application of a multivariate HAR-GARCH model for studying volatility transmission patterns in energy futures markets. In particular, the nature of volatility spillovers between futures on crude oil, natural gas and gas-oil is examined using range-based volatility proxies and splitting volatility in components defined over different time horizons. The results provide evidence that crude oil futures carry significant information for the volatility evolution of other energy futures traded on the ICE and reveal interesting insights in the sources of the documented volatility interrelations. Short-term shocks in Brent oil volatility significantly affect the volatility of gas-oil futures, while the impact of oil and gas-oil on natural gas is driven by the long-term volatility component. Additionally, Brent oil and gas-oil ICE futures volatilities exhibit strong positive dynamic correlation, whereas the remaining pairwise correlation curves are fluctuating around zero.

Volatility spillovers between the oil market and the European Union carbon emission market

This paper examines the dynamics of volatility transmission between EU emission allowances (EUA) and oil markets using a range-based volatility measure. We propose a multivariate conditional autoregressive range model with bivariate lognormal distribution to capture volatility dynamics and volatility spillovers between oil and EUA markets. Our findings for Phase II of the European Union Emissions Trading Scheme point to the existence of volatility dynamics and leverage effects and to no significant volatility spillovers between these markets. These results remained robust to other volatility measures and model specifications.

Optimally sampled realized range-based volatility estimators

Range-based volatility estimators are analyzed in both daily and intraday sampling frequency and are also compared to the realized volatility estimator. The family of realized range-based estimators is extended as three range-based estimators are introduced. These three realized Parkinson range-based estimators are estimated in an optimal sampling frequency. Empirical analysis concerns three major US spot equity indices. The descriptive statistics and the long-memory estimations are compared between the daily and realized range-based estimators, and across each group as well. The realized range-based estimators are also compared in terms of the properties of the jump components of volatility. Moreover, the relevant effects of jumps on volatility are assessed by the use of the class of Heterogeneous Autoregressive (HAR) models.

The extreme value in crude oil and US dollar markets

This study constructs a flexible range-based volatility model by considering extreme-value information to explore the volatility and dependence structures between the oil price and the US dollar exchange rate. An asset-allocation strategy is implemented to evaluate the economic value and confirm the efficiency of this model. The empirical results indicate that the use of extreme-value information can not only enhance the explanatory power of volatility structures, but also sort out the asymmetric volatility effect in the oil market. Besides, investors can obtain extra benefits of between 72 and 713 annualized basis points by incorporating extreme-value information into their asset-allocation strategies; less risk-averse investors can generate higher benefits. The empirical results have potentially important implications for asset allocation and risk management.

Regime-switching in volatility and correlation structure using range-based models with Markov-switching

This study examines latent shifts in the conditional volatility and correlation for the U.S. stock and T-bond data using the two-state Markov-switching range-based volatility and correlation models. This paper comes up with clear evidence of volatility regime-switching in stock indices and T-bond over the crisis period. As regards the process of correlation, we also find evidence of regime changes in correlations between stock indices and T-bond over several financial crises. We conclude that the phenomena of both volatility and correlation regime-switching are triggered by these financial crises. In addition, the range-based volatility and correlation model with regime-switching method could explicitly point out the true date of structure changes in the data generating process for volatility and correlation variables.

Global and regional range-based volatility spillover effects

This study extends the univariate Weibull conditional autoregressive range (CARR) model to establish a bivariate Weibull CARR (BWCARR) model to investigate the range-based volatility spillover effect. The empirical results indicate that a conditional autoregressive range relationship exists on the US, Japan, mainland China, Hong Kong and Taiwan stock markets. The new BWCARR model is more credible and efficient than the CARR model. Moreover, the range-based volatility for the US and Japan has an impact on Taiwan, indicating that there exists a range-based global and regional stock market spillover effect that has an impact on the Taiwanese stock market.

Range-Based Analysis of Volatility Spillovers in European Financial Markets

We analyze multivariate time series of daily high-low ranges of national equity market indices to measure intra-daily volatility dynamics across four continental European markets. We use a dynamic linear model of expected daily range which is a variant of Chou’s conditional autoregressive range model. We find significant, but not uniform, range-based volatility spillovers between the European markets. The strongest spillover comes from the previous day’s realized range of the US market index. We also find that average daily range increased sharply during the European financial crisis, and the degree of autoregressive persistence also increased uniformly.

A Markov-Switching Range-Based Volatility Model with Applications in Volatility Adjusted VAR Estimation

We propose a more flexible range-based volatility model which can capture volatility process better than conventional GARCH approach. Considering the regime switching process is appropriate for dealing the structure change embedded in the time series data. Range-based volatility CARR model with Markov-switching structure can assist us to describe the effect for exogenous shock to market data. After the data fitting and VaR estimation, we conclude that the range-based volatility method is better than the return-based GARCH model in volatility fitting. In particular, incorporating the possibility of regime switching into volatility process can boost the efficiency for VaR estimation. We also present an empirical application for demonstrating our model could characterize the unexpected switching of volatility process. Furthermore, comparing with non-regime switching volatility model, our model outperforms other alternatives on the estimation of volatility adjusted historical VaR.