Extreme Value Volatility Estimator Research Articles

Excess Volatility in Bitcoin: Extreme Value Volatility Estimation

This article investigates the excess volatility in Bitcoin prices using an unbiased extreme value volatility estimator. We capture the time-varying nature of the excess volatility using bootstrap, multi-horizon, sub-sampling and rolling-window approaches. We observe that Bitcoin price changes are almost efficient. Although Bitcoin prices exhibit high volatility and show signs of excess volatility for a few periods, it is decreasing over time. After controlling for the outliers, we also notice that the Bitcoin market shows signs of increasing maturity. Overall, Bitcoin prices show a sign of increasing efficiency with decreasing volatility. Our findings have implications for investors making investment decisions and for regulators making policy choices.

Estimating and Predicting Value-at-Risk in the presence of structural breaks: A Study based on unbiased extreme value volatility estimator

We provide a framework based on the unbiased extreme value volatility estimator to predict long and short position value-at-risk (VaR). The given framework incorporates the impact of asymmetry, structural breaks and fat tails in volatility. We generate forecasts of both long and short position VaR and evaluate the VaR forecasting performance of the proposed framework using various backtesting approaches for both long and short positions and compare the results with that of various alternative models. Our findings indicate that the proposed framework outperforms the alternative models in predicting the long and the short position VaR. Our findings also indicate that the VaR forecasts based on the proposed framework provides the least total loss score for various long and short positions VaR and this supports the superior properties of the proposed framework in forecasting VaR more accurately. The study contributes by providing a framework to predict more accurate VaR measure based on the unbiased extreme value volatility estimator.

Value-at-Risk in the Presence of Structural Breaks Using Unbiased Extreme Value Volatility Estimator

We provide a framework based on the unbiased extreme value volatility estimator to predict long and short position value-at-risk (VaR). The given framework incorporates the impact of asymmetry, structural breaks and fat tails in volatility. We generate forecasts of long and short position VaR for the cases when future structural breaks are known as well as unknown. We evaluate its VaR forecasting performance using various backtesting approaches for both long and short positions and compare the results with that from return based models. Our findings indicate that incorporating the impact of structural breaks in volatility indeed improves the accuracy of VaR forecasts of the proposed framework.

Modeling unbiased extreme value volatility estimator in presence of heterogeneity and jumps: A study with economic significance analysis

Based on the heterogeneous autoregressive (HAR) model, the study proposes the frameworks (HAR-AddRS and HAR-AddRS-J) to incorporate the impact of heterogeneity and volatility jumps in modeling the AddRS volatility estimator (Kumar & Maheswaran, 2014a, 2014b). The forecasting performance of the HAR-AddRS and HAR-AddRS-J models in comparison to the returns-based conditional volatility models is evaluated using the error statistic approach, Hansen (2005) superior predictive ability (SPA) test, and Hansen, Lunde et al. (2011) model confidence set (MCS) approach. The findings suggest that more accurate forecasts of daily volatility are obtained based on the HAR-AddRS and HAR-AddRS-J models than based on the returns-based conditional volatility models. The economic significance analysis results show that a substantial economic gain is achieved when the volatility forecasts based on the HAR-AddRS-J model are used to implement the trading strategies, however, the same is not true when the volatility forecasts are based on the returns-based conditional volatility models.

Modelling and forecasting unbiased extreme value volatility estimator: A study based on exchange rates with economic significance analysis

This study proposes the use of an alternate approach to generate more accurate forecasts of an unbiased AddRS estimator. The study also devises and implements trading strategies based on the volatility forecasts to highlight its economic significance. The findings indicate that the proposed framework provides more accurate forecasts of daily volatility in comparison to returns based and range based alternative volatility models. The findings based on economic significance analysis indicate that the risk averse investor can earn substantial gain by using the volatility forecasts of the proposed framework than by using the volatility forecasts of the alternative models.

Heterogeneous market hypothesis approach for modeling unbiased extreme value volatility estimator in presence of leverage effect: An individual stock level study with economic significance analysis

This paper explores the role of heterogeneity and leverage effect on the predictability of the AddRS volatility estimator (Kumar & Maheswaran, 2014a) using daily, weekly and monthly volatility components. Similar to the model setting of heterogeneous autoregressive (HAR) model (Corsi, 2009), we introduce the frameworks (HAR – AddRS and HAR – AddRS – L) to incorporate the impact of heterogeneity and leverage effect in modeling the AddRS volatility estimator. We find that the heterogeneity and leverage effect significantly impact the volatility prediction and when taken together produce a better in-sample fit. To evaluate the out-of-sample performance of our new volatility models, we compare the forecasting performance of our models with that of other traditional benchmark models forecasts using the error statistic approach and Hansen (2005) superior predictive ability (SPA) test. The results show that the HAR – AddRS and HAR – AddRS – L models provide more accurate volatility forecasts for the out-of-sample. We also undertake the economic significance analysis to highlight that a substantial economic gain is achieved when the volatility forecasts based on the HAR – AddRS – L model are used to implement various trading strategies, however, the same is not true when the volatility forecasts are based on the traditional returns-based conditional volatility models.

Structural Breaks in Volatility Transmission from Developed Markets to Major Asian Emerging Markets

The study investigates the volatility transmission from developed markets (the United States [US], the United Kingdom [UK] and Japan) to the major Asian emerging markets (India, China, Malaysia, Thailand and Indonesia) during a period from 1996 to 2015. We make use of the opening, high, low and closing prices to estimate unbiased extreme value volatility estimator and implement heterogeneous autoregressive distributed lag (HAR-DL) framework to study the spillover effects. Based on time-varying spillover analysis, we observe sudden changes in the spillover effect during the periods of major crises. Initially, we find evidence of contagion during the period of global financial crisis of 2007–2009. However, after accounting for conditional heteroscedasticity, we observe a decline in the strength of volatility transmission from developed markets to the Asian emerging markets. Moreover, the initial evidence of contagion is not detectable anymore. We also test the economic significance of the findings by implementing three trading strategies based on risk averse and risk-taking investors that make use of the forecasted variance based on HAR-DL specification. Our findings indicate that substantial average annualised gains in returns can be earned based on the lagged volatility components of the USA and the UK. JEL Classification: C32, C58, G01, G15

Modelling and Forecasting Unbiased Extreme Value Volatility Estimator: A Study Based on EUR/USD Exchange Rate

The paper provides a framework to model and forecast volatility of EUR/USD exchange rate based on the unbiased AddRS estimator as proposed by Kumar and Maheswaran [1]. The framework is based on the heterogeneous auto-regressive (HAR) model to capture the heterogeneity in a market and to ac-count for long memory in data. The results indicate that the framework based on the unbiased extreme value volatility estimator generates more accurate forecasts of daily volatility in comparison to alternative volatility models.

Forecasting energy futures volatility based on the unbiased extreme value volatility estimator

This paper uses the opening, high, low, and closing prices of five energy futures to estimate and model volatility based on the unbiased extreme value volatility estimator (the Add RS estimator). The statistical and distributional properties of the logarithm of the Add RS estimator support the use of the appropriate order linear long-memory Gaussian model (ARFIMA-Add RS model) to model the Add RS estimator. The out-of-sample evaluation analysis indicates that the volatility forecasts based on the ARFIMA-Add RS model provide superior forecasts of realised volatility of energy futures in relation to the alternative models from the GARCH family. This suggests that the ARFIMA-Add RS model can be a viable candidate for generating more accurate forecasts of realised volatility for energy futures. The study has important implications for governments, policy makers, oil importers, and oil traders.

Value-at-risk and expected shortfall using the unbiased extreme value volatility estimator

PurposeThis paper aims to propose a framework based on the unbiased extreme value volatility estimator (namely, the AddRS estimator) to compute and predict the long position and the short position value-at-risk (VaR) and stressed expected shortfall (ES). The precise prediction of VaR and ES measures has important implications toward financial institutions, fund managers, portfolio managers, regulators and business practitioners.Design/methodology/approachThe proposed framework is based on the Giot and Laurent (2004) approach and incorporates characteristics like long memory, fat tails and skewness. The authors evaluate its VaR and ES forecasting performance using various backtesting approaches for both long and short positions on four global indices (S&P 500, CAC 40, Indice BOVESPA [IBOVESPA] and S&P CNX Nifty) and compare the results with that of various alternative models.FindingsThe findings indicate that the proposed framework outperforms the alternative models in predicting the long and the short position VaR and stressed ES. The findings also indicate that the VaR forecasts based on the proposed framework provide the least total loss for various long and short position VaR, and this supports the superior properties of the proposed framework in forecasting VaR more accurately.Originality/valueThe study contributes by providing a framework to predict more accurate VaR and stressed ES measures based on the unbiased extreme value volatility estimator.

Structural breaks in unbiased volatility estimator: Modeling and forecasting

The study provides a framework to model the unbiased extreme value volatility estimator (The AddRS estimator) in presence of structural breaks. We observe that the structural breaks in the volatility based on the AddRS estimator can partly explain its long memory property. We evaluate the forecasting performance of the proposed framework and compare the results with the corresponding results of the models from the GARCH family. The forecasts evaluation exercises consider the cases when future breaks are known as well as unknown. Our findings indicate that the proposed framework outperform the sophisticated GARCH class of models in forecasting realized volatility. Moreover, we devise a trading strategy based on the forecasts of the variance to highlight the economic significance of the proposed framework. We find that a risk averse investor can make substantial gain using the volatility forecasts based on the proposed frameworks in comparison to the GARCH family of models.

Modeling and Forecasting Unbiased Extreme Value Volatility Estimator in Presence of Leverage Effect

This study proposes the frameworks (A-HAR-AddRS and HAR-AddRS-AGARCH) to account for leverage effect in modeling and forecasting the AddRS estimator (Kumar and Maheswaran, Econ Model 38:33–44, 2014b) based on heterogeneous autoregressive (HAR) model. We evaluate the forecasting performance of the A-HAR-AddRS and HAR-AddRS-AGARCH models using the error statistic approach, the superior predictive ability (SPA) approach and the model confidence set (MCS) approach and compare the results with the corresponding results from the return based asymmetric and regime switching volatility models. To illustrate it, we use the same indices as used by Kumar and Maheswaran (Int Rev Financ Anal 34:166–176, 2014a, Econ Model 38:33–44, 2014b), that is, S&P 500, CAC 40, IBOVESPA and S&P CNX Nifty. Our findings indicate that the A-HAR-AddRS and HAR-AddRS-AGARCH models provide more accurate forecasts of realized volatility than the returns based asymmetric and regime switching volatility models.

Sudden changes in crude oil price volatility: an application of extreme value volatility estimator

This study provides a framework based on an extension of the Conditional Autoregressive Range (CARR) model which incorporates the impact of sudden changes in unconditional volatility. The results of the CARR model with and without volatility breaks are compared with the results of the GARCH model with and without volatility breaks to assess whether the forecasting ability of the CARR model is superior when endogenously determined structural breaks in volatility are accounted for. We undertake our analysis on WTI and Brent crude oil and find that the CARR model with volatility breaks effectively captures the dynamics in the crude oil volatility.

Sudden changes in crude oil price volatility: an application of extreme value volatility estimator

This study provides a framework based on an extension of the Conditional Autoregressive Range (CARR) model which incorporates the impact of sudden changes in unconditional volatility. The results of the CARR model with and without volatility breaks are compared with the results of the GARCH model with and without volatility breaks to assess whether the forecasting ability of the CARR model is superior when endogenously determined structural breaks in volatility are accounted for. We undertake our analysis on WTI and Brent crude oil and find that the CARR model with volatility breaks effectively captures the dynamics in the crude oil volatility.

Sudden changes in extreme value volatility estimator: Modeling and forecasting with economic significance analysis

This study provides a framework based on an extension of the conditional autoregressive range (CARR) model which incorporates the impact of sudden changes in the unconditional volatility. This study proposes to use the RS estimator in the CARR model (called henceforth the CARRS model) instead of using the range. The results of the CARRS models with and without volatility breaks are compared with the results of the GARCH models with and without volatility breaks. We also compare the forecasting performance of CARRS models with the forecasting performance of EGARCH, TGARCH and FIGARCH models based on error statistics and regression approach. The findings indicate that the CARRS model with volatility breaks effectively captures the dynamics of volatility and provides better out-of-sample forecasts when compared with GARCH, EGARCH, TGARCH and FIGARCH models. We also devise a trading strategy to examine the economic significance of the proposed framework which indicates that the investor can make substantial gains (approximately 6%–10%) in return for most of cases based on volatility forecasts of CARRS model with volatility breaks. Results based on robustness check are consistent with our main findings.

Modeling and forecasting the additive bias corrected extreme value volatility estimator

In this paper, we provide a framework to model and forecast daily volatility based on the newly proposed additive bias corrected extreme value volatility estimator (the Add RS estimator). The theoretical framework of the additive bias corrected extreme value volatility estimator is based on the closed form solution for the joint probability of the running maximum and the terminal value of the random walk. Using the opening, high, low and closing prices of S&P 500, CAC 40, IBOVESPA and S&P CNX Nifty indices, we find that the logarithm of the Add RS estimator is approximately Gaussian and that a simple linear Gaussian long memory model can be applied to forecast the logarithm of the Add RS estimator. The forecast evaluation analysis indicates that the conditional Add RS estimator provides better forecasts of realized volatility than alternative range-based and return-based models.

A conditional extreme value volatility estimator based on high-frequency returns

This paper introduces a conditional extreme value volatility estimator (EVT) based on high-frequency returns. The relative performance of the EVT is compared with the discrete-time GARCH and implied volatility models for 1-day and 20-day-ahead forecasts of realized volatility. This is also a first attempt towards detecting any time-series variation in extreme value distributions using high-frequency intraday data. The information content of EVT is examined in the context of forecasting S&P 100 index volatility. Adjusted- R 2 values imply superior performance of the implied volatility index (VIX) and EVT in capturing time-series variation in realized volatility. The forecasting ability of various discrete-time GARCH models turns out to be inferior to VIX and EVT. According to the Theil inequality coefficient and the heteroscedasticity-adjusted root mean squared and mean absolute errors, (1) EVT provides more accurate forecasts than the VIX and GARCH volatility models; (2) VIX generally yields a less accurate characterization of realized volatility than EVT and GARCH models. These results have implications for financial risk management, and are thus relevant to both regulators and practitioners.