Modeling interest rate volatility: A Realized GARCH approach

Tian, Shuairu; Hamori, Shigeyuki

doi:10.1016/j.jbankfin.2015.09.008

Cited by 31 publications

(15 citation statements)

References 32 publications

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“…If the p-values greater than a specific threshold, which is also called MCS alpha, the corresponding model is supposed to have better predictability than the others (Hansen et al, 2005). However, there is no consensus on the specific value of the threshold p-value in MCS tests, and different literatures set the different MCS alpha values (Tian and Hamori, 2015;Pu et al, 2016). Table 5 reports the p-values of the MCS tests for GARCH-MIDAS-MU models.…”

Section: Out-of-sample Evaluationmentioning

confidence: 99%

Macroeconomic Uncertainty and Crude Oil Futures Volatility–Evidence from China Crude Oil Futures Market

Yang

2021

Front. Environ. Sci.

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This paper investigates whether the macroeconomic uncertainty factors can explain and forecast China’s INE crude oil futures market volatility. We use the GARCH-MIDAS model to investigate the explaining and predicting power of the macroeconomic uncertainties. We considered various geopolitical risk (GPR) indices, economic policy uncertainty (EPU) indices, and infectious disease pandemic (IDEMV) indices in our model. The empirical results suggest that the geopolitical risk, the geopolitical act risk, the global economic policy uncertainty, the economic policy uncertainty from the United Kingdom, and the economic policy uncertainty from Japan comprehensively integrate the information contained in the rest factors, and have superior predictive powers for INE crude oil future volatility. These findings highlight the importance of the impact of macroeconomic uncertainty factors has on the crude oil futures market, and indicate that the macroeconomic uncertainties need to be considered when explaining and forecasting crude oil futures market volatility.

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Section: Out-of-sample Evaluationmentioning

confidence: 99%

Macroeconomic Uncertainty and Crude Oil Futures Volatility–Evidence from China Crude Oil Futures Market

Yang

2021

Front. Environ. Sci.

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“…where m is the intercept and θ refers to the slope, suggesting the weighted effects of lagged variables, V t , on the long-run volatility in CSI 300. To make sure that the conditional variances are nonnegative, we use the following log transformation [64]. e following equation refers to the log transformation:…”

Section: E Garch-midas Modelmentioning

confidence: 99%

“…For the exact value of such a specific threshold in the MCS test, there is no consensus in previous literature. Tian and Hamori [64] and Pu et al [77] set the p value alpha to be 0.1, whereas Liu et al [50], Mei et al [62], and Liang et al [15] set the p value alpha to be 0.25. Since we use the same method as [50] and focus on the volatility prediction as Liang et al [15], we set the threshold p value to be 0.25.…”

Section: E Forecast Performance Of Different Categorical Gprmentioning

confidence: 99%

Geopolitical Risk and Stock Market Volatility in Emerging Economies: Evidence from GARCH-MIDAS Model

Yang

Zhang

et al. 2021

Discrete Dynamics in Nature and Society

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Previous studies have found that geopolitical risk (GPR) caused by geopolitical events such as terrorist attacks can affect the movements of asset prices. However, the studies on whether and how these influences can explain and predict the volatility of stock returns in emerging markets are scant and emerging. By using the data from China’s CSI 300 index, we provide some evidence on whether and how the GPR factors can explain and forecast the volatility of stock returns in emerging economies. We employed the GARCH-MIDAS model and the model confidence set (MCS) to investigate the mechanism of GPR’s impact on the China stock market, and we considered the GPR index, geopolitical action index, geopolitical threat index, and different country-specific GPR indices. The empirical results suggest that except for a few emerging economies such as Mexico, Argentina, Russia, India, South Africa, Thailand, Israel, and Ukraine, the global and most of the regional GPR have a significant impact on China’s stock market. This paper provides some evidence for the different effects of GPR from different countries on China’s stock market volatility. As for predictive potential, GPRAct (geopolitical action index) has the best predictive power among all six types of GPR indices. Considering that GPR is usually unanticipated, these findings shed light on the role of the GPR factors in explaining and forecasting the volatility of China’s market returns.

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“…Patton (2011) suggests using the realized volatility as an unbiased estimator. It is also more efficient than squared return if the log price follows a Brownian motion (Tian & Hamori, 2015). Realized volatility is the sum of squared high-frequency returns within a day.…”

Section: Datamentioning

confidence: 99%

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

Maciel

Ballini

2017

Rev. contab. finanç.

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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.

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Modeling interest rate volatility: A Realized GARCH approach

Cited by 31 publications

References 32 publications

Macroeconomic Uncertainty and Crude Oil Futures Volatility–Evidence from China Crude Oil Futures Market

Macroeconomic Uncertainty and Crude Oil Futures Volatility–Evidence from China Crude Oil Futures Market

Geopolitical Risk and Stock Market Volatility in Emerging Economies: Evidence from GARCH-MIDAS Model

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

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