Range-based Volatility Model Research Articles

Improving volatility forecasts: Evidence from range-based models

Volatility models based on the daily high-low range have become increasingly popular. The high and low prices are easily available, yet the range contains very useful information about volatility. It has been established in the literature that range-based volatility models outperform standard volatility models based on closing prices. However, little is known about which range-based model performs the best. We therefore evaluate two range-based volatility models, i.e. CARR and Range-GARCH with the standard GARCH model and two asymmetric GARCH models, i.e., GJR and EGARCH, based on the Monte Carlo experiments and a wide sample of currencies and stock indices. For simulated time series, the range-based models outperform the standard GARCH model and asymmetric models, and the performance of the Range-GARCH model and the CARR model is similar. However, for real financial time series (six currency pairs and nine stock indices) the Range-GARCH model outperforms the standard GARCH, GJR, EGARCH, and CARR models, while ranking of the competing models is ambiguous. We argue that Range-GARCH is the best from the competing models.

Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model

In this paper, we investigate the relation between time-varying risk aversion and renminbi exchange rate volatility using the conditional autoregressive range-mixed-data sampling (CARR-MIDAS) model. The CARR-MIDAS model is a range-based volatility model, which exploits intraday information regarding the intraday trajectory of the price. Moreover, the model features a MIDAS structure allowing for time-varying risk aversion to drive the long-run volatility dynamics. Our empirical results show that time-varying risk aversion has a significantly negative effect on the long-run volatility of renminbi exchange rate. Moreover, we observe that both intraday ranges and time-varying risk aversion contain important information for forecasting renminbi exchange rate volatility. The range-based CARR-MIDAS model incorporating time-varying risk aversion provides more accurate out-of-sample forecasts of renminbi exchange rate volatility compared to a variety of competing models, including the return-based GARCH, GARCH-MIDAS and GARCH-MIDAS incorporating time-varying risk aversion as well as range-based CARR, CARR-MIDAS and heterogeneous autoregressive (HAR), for forecast horizons of 1 day up to 3 months. This result is robust to alternative risk aversion measure, alternative MIDAS lags as well as alternative out-of-sample periods. Overall, our findings highlight the value of incorporating intraday information and time-varying risk aversion for forecasting the renminbi exchange rate volatility.

Holidays, weekends and range-based volatility

This study analyses the effect of non-trading periods on the forecasting ability of S&P500 index range-based volatility models. We find that volatility significantly diminishes on the first trading day after holidays and weekends, but not after long weekends. Our findings indicate that models that include autoregressive terms that interact with dummies that allow us to capture changes in volatility levels after interrupting periods provide greater explanatory power than simple autoregressive models. Therefore, the shorter the length of the non-trading periods between two trading days, the higher the overestimation of the volatility if this effect is not considered in volatility forecasting.

Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

ABSTRACT This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

Efficient modelling and forecasting with range based volatility models and its application

This paper considers an alternative method for fitting CARR models using the combined estimating functions (CEF) by showing its usefulness in applications in economics and quantitative finance. The associated information matrix for corresponding new estimates is derived to calculate the standard errors. Extensive simulation study is carried out to demonstrate its superiority relative to two other competitors: the linear estimating functions (LEF) and the maximum likelihood (ML). Results show that the CEF method is more efficient than the LEF and ML methods when the error distribution is mis-specified. Applying a real data set from financial market, we illustrate the applicability of the CEF method in practice and report some reliable forecast values for minimizing the risk in decision making process.

Forecasting the oil futures price volatility: A new approach

This study provides a new perspective of modelling and forecasting realized range-based volatility (RRV) for crude oil futures. We are the first to improve the Heterogeneous Autoregressive model of Realized Range-based Volatility (HAR-RRV) model by considering the significant jump components, signed returns and volatility of realized range-based volatility. The empirical results show that the volatility of volatility significantly exists in the oil futures market. Moreover, our new proposed models with significant jump components, signed returns and volatility of volatility can gain higher forecast accuracy than HAR-RRV-type models. The results are robust to different forecasting windows and forecasting horizons. Our new findings are strategically important for investors making better decisions.

Economic evaluation of asymmetric and price range information in gold and general financial markets

Gold is widely perceived as a good diversification or safe haven tool for general financial markets, especially in market turmoil. To fully understand the potential, this study constructs an asymmetric multivariate range-based volatility model to investigate the dependence and volatility structures of gold, stock, and bond markets and further to compare the difference between the financial crisis and post-financial crisis periods. We find a striking explanatory ability to volatility structures provided by the price range information and significant evidence of asymmetric dependence across gold, stock, and bond markets. We implement an asset-allocation strategy incorporating asymmetric dependence and price range information to explore their economic importance. The out-of-sample results show that between 35 and 517 basis points and between 90 and 1111 basis points are earned annually when acknowledging asymmetric dependence and price range information, respectively. These economic benefits are inversely related to the level of investors’ risk aversion and are particularly significant in the period of the global financial crisis.

Forecasting the realized range-based volatility using dynamic model averaging approach

In this study, we forecast the realized range-based volatility (RRV) using the heterogeneous autoregressive realized range-based volatility (HAR-RRV) model and its various extensions, which are called HAR-RRV-type models. We first consider the time-varying property of those models’ parameters using the dynamic model averaging (DMA) approach and evaluate the forecasting performance of three types: individual HAR-RRV-type models, combined models with constant weights, and combined models with time-varying weights. Our out-of-sample empirical results show that combined models with time-varying weights can not only generate more accurate forecasts, but also beat individual models and combined models with constant weights.

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.

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.

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.

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.

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.

Measuring and testing the long-term impact of terrorist attacks on the US futures market

This article investigates the long-term impact of the 11 September 2001 terrorist attacks on the maturity, volume and open interest effects for the S&P 500 index futures contracts. Adopting Chou (2005a, b)'s range-based volatility models, this article provides a number of interesting results. For the maturity effect, we find evidence for a very weak presence in the pre 9/11 period and no presence in the post 9/11 period, indicating that the maturity effect vanishes completely following the event of 9/11. Despite a strong presence of the volume effect in both periods, we detect a relative decrease in the presence during the post 9/11 period. The open interest effect shows a very weak presence during the pre 9/11 period and a strong presence during the post 9/11 period, indicating a stronger open interest effect following the event of 9/11. Furthermore, we show that there is a bi-directional causality relationship between futures volatility and trading volume during the pre 9/11 period, and that the causality relationship between the two variables becomes unidirectional during the post 9/11 period.

The economic value of range-based covariance between stock and bond returns with dynamic copulas

Abstract The covariance between stock and bond returns plays important roles in the setting up of asset allocation strategies and portfolio diversification. In the present study, we propose a multivariate range-based volatility model incorporating dynamic copulas into a range-based volatility model to describe the volatility and dependence structures of stock and bond returns. We then go on to assess the economic value of the covariance forecasts based on our proposed model under a mean-variance framework. The out-of-sample forecasting performance reveals that investors would be willing to pay between 39 and 2081 basis points per year to switch from a dynamic trading strategy under the return-based volatility model to a dynamic trading strategy under the range-based volatility model, with more risk-averse investors being willing to pay even higher switching fees. Furthermore, additional economic gains of between 33 and 1471 annualized basis points are achieved when taking the leverage effect into consideration.

The economic value of volatility timing using a range-based volatility model

There is growing interest in utilizing the range data of asset prices to study the role of volatility in financial markets. In this paper, a new range-based volatility model was used to examine the economic value of volatility timing in a mean–variance framework. We compared its performance with a return-based dynamic volatility model in both in-sample and out-of-sample volatility timing strategies. For a risk-averse investor, it was shown that the predictable ability captured by the dynamic volatility models is economically significant, and that a range-based volatility model performs better than a return-based one.

The Economic Value of Volatility Timing using a Range-Based Volatility Model

There is growing interest in utilizing the range data of asset prices to study the role of volatility in financial markets. In this paper, a new range-based volatility model is used to examine the economic value of volatility timing in a mean-variance framework. We compare its performance with a return-based dynamic volatility model in both in-sample and out-of-sample volatility timing strategies. For a risk-averse investor, it is shown that the predictable ability captured by the dynamic volatility models is economically significant, and that the range-based volatility model performs better than the return-based one.