共查询到12条相似文献，搜索用时 0 毫秒
1.
This study is the first to examine the impacts of overnight and intraday oil futures cross-market information on predicting the US stock market volatility the high-frequency data. In-sample estimations present that high overnight oil futures RV can lead to high RV of the S&P 500. Moreover, negative overnight returns are more powerful than positive components, implying the existence of the leverage effect. From statistical and economic perspectives, out-of-sample results indicate that the decompositions of overnight oil futures and intraday RVs, based on signed intraday returns, can significantly increase the models' predictive ability. Finally, when considering the US stock market overnight effect, the decompositions are still useful to predict volatility, especially during high US stock market fluctuations and high and low EPU states. 相似文献
2.
In this paper, we introduce the functional coefficient to heterogeneous autoregressive realized volatility (HAR‐RV) models to make the parameters change over time. A nonparametric statistic is developed to perform a specification test. The simulation results show that our test displays reliable size and good power. Using the proposed test, we find a significant time variation property of coefficients to the HAR‐RV models. Time‐varying parameter (TVP) models can significantly outperform their constant‐coefficient counterparts for longer forecasting horizons. The predictive ability of TVP models can be improved by accounting for VIX information. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
3.
Yi Hong;Xiaofan Xu;Chen Yang; 《Journal of forecasting》2024,43(8):3104-3127
This paper investigates the high-frequency volatility modeling and prediction for crude oil futures in China, a new asset class emerging in recent years. Two volatility measures, the realized variance () and realized bi-power variations () are constructed at various frequencies by virtue of 1-minute crude oil futures prices. The distinctive components of these volatility estimators are further identified to exploit the information contents in the in-sample explanatory power of the realized variance dynamics and the out-of-sample prediction of realized variance across different horizons, leading to four new HAR-RV-type models. First, the empirical results show that the continuous component of the weekly realized variance, representing investors' trading behavior in the medium-term, is the dominant factor driving up volatility trends in China's crude oil futures market over a range of market conditions. Second, the monthly jump component in realized variance presents the significant in-sample explanatory power, and yet marginally improves prediction performance in realized variance during the two out-of-sample periods. Finally, these results are robust toward various market/model setups, over day- and night-trading hours, and across a range of prediction horizons and relative to prediction benchmarks. 相似文献
4.
Basel M. A. Awartani 《Journal of forecasting》2008,27(3):267-278
Empirical high‐frequency data can be used to separate the continuous and the jump components of realized volatility. This may improve on the accuracy of out‐of‐sample realized volatility forecasts. A further improvement may be realized by disentangling the two components using a sampling frequency at which the market microstructure effect is negligible, and this is the objective of the paper. In particular, a significant improvement in the accuracy of volatility forecasts is obtained by deriving the jump information from time intervals at which the noise effect is weak. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
5.
Qianjie Geng;Xianfeng Hao;Yudong Wang; 《Journal of forecasting》2024,43(2):309-325
Parameter instability and model uncertainty are two key problems affecting forecasting outcomes. In this paper, we propose a time-dependent weighted least squares with ridge constraint (TWLS-Ridge) to solve the above two problems in the forecasting procedure. The new TWLS-Ridge approach is applied to the heterogenous autoregressive realized volatility model and its various extensions. The empirical results suggest that TWLS-Ridge produces more accurate volatility forecasts than several alternative models dealing with parameter instability and model uncertainty. The superior performance of TWLS-Ridge is robust under different forecast horizons, evaluation periods, and loss functions. An investor with mean–variance preference can improve utility using TWLS-Ridge forecasts of oil volatility instead of ordinary least squares model forecasts. 相似文献
6.
To forecast realized volatility, this paper introduces a multiplicative error model that incorporates heterogeneous components: weekly and monthly realized volatility measures. While the model captures the long‐memory property, estimation simply proceeds using quasi‐maximum likelihood estimation. This paper investigates its forecasting ability using the realized kernels of 34 different assets provided by the Oxford‐Man Institute's Realized Library. The model outperforms benchmark models such as ARFIMA, HAR, Log‐HAR and HEAVY‐RM in within‐sample fitting and out‐of‐sample (1‐, 10‐ and 22‐step) forecasts. It performed best in both pointwise and cumulative comparisons of multi‐step‐ahead forecasts, regardless of loss function (QLIKE or MSE). Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
7.
This study compares the volatility and density prediction performance of alternative GARCH models with different conditional distribution specifications. The conditional residuals are specified as normal, skewedHyphen;t or compound Poisson (jump) distribution based upon a nonlinear and asymmetric GARCH (NGARCH) model framework. The empirical results for the S&P 500 and FTSE 100 index returns suggest that the jump model outperforms all other models in terms of both volatility forecasting and density prediction. Nevertheless, the superiority of the nonHyphen;normal models is not always significant and diminished during the sample period on those occasions when volatility experiences an obvious structural change. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
8.
In this paper, we investigate the time series properties of S&P 100 volatility and the forecasting performance of different volatility models. We consider several nonparametric and parametric volatility measures, such as implied, realized and model‐based volatility, and show that these volatility processes exhibit an extremely slow mean‐reverting behavior and possible long memory. For this reason, we explicitly model the near‐unit root behavior of volatility and construct median unbiased forecasts by approximating the finite‐sample forecast distribution using bootstrap methods. Furthermore, we produce prediction intervals for the next‐period implied volatility that provide important information about the uncertainty surrounding the point forecasts. Finally, we apply intercept corrections to forecasts from misspecified models which dramatically improve the accuracy of the volatility forecasts. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
9.
This paper examines the information content of implied volatility for crude oil options as it relates to future realized volatility. Using data for the period 1996 to 2011 we find that implied volatility is an effective predictor of the month‐ahead realized volatility. We show that implied volatility subsumes the information content of contemporaneous volatility, and it contains incremental information on future volatility after controlling for contemporaneous volatility. Furthermore, incorporating risk‐neutral skewness, and especially kurtosis, improves the forecasting of realized volatility. Overall, the association between implied volatility and month‐ahead realized volatility is consistent with evidence documented for other asset classes, leading us to conclude that implied volatility serves as a reasonable proxy for expected volatility. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
10.
Gongyue Jiang;Gaoxiu Qiao;Lu Wang;Feng Ma; 《Journal of forecasting》2024,43(6):2378-2398
From the cross-market perspective, this paper investigates crude oil volatility index (OVX) forecasts by proposing a hybrid method, which combines the data-driven SVR technique and parametric models. In terms of parametric models, we utilize GARCH-type models with jumps, and the forecasting effects of five non-parametric jumps (including interday and intraday jump tests) of stock market are also explored. Empirical results show that our approach can substantially increase forecasting accuracy. In addition, the model confidence set test and robust test reaffirm the superiority of the novel hybrid method. From the assessment of economic significance, the advantages of the hybrid method for volatility index forecasting are further confirmed. All these findings imply that jumps of stock market can be helpful in forecasting OVX, especially after the introduction of the hybrid method. Our work can certainly provide a new insight for volatility forecasting and cross-market research. 相似文献
11.
Janchung Wang 《Journal of forecasting》2009,28(4):277-292
This study attempts to apply the general equilibrium model of stock index futures with both stochastic market volatility and stochastic interest rates to the TAIFEX and the SGX Taiwan stock index futures data, and compares the predictive power of the cost of carry and the general equilibrium models. This study also represents the first attempt to investigate which of the five volatility estimators can enhance the forecasting performance of the general equilibrium model. Additionally, the impact of the up‐tick rule and other various explanatory factors on mispricing is also tested using a regression framework. Overall, the general equilibrium model outperforms the cost of carry model in forecasting prices of the TAIFEX and the SGX futures. This finding indicates that in the higher volatility of the Taiwan stock market incorporating stochastic market volatility into the pricing model helps in predicting the prices of these two futures. Furthermore, the comparison results of different volatility estimators support the conclusion that the power EWMA and the GARCH(1,1) estimators can enhance the forecasting performance of the general equilibrium model compared to the other estimators. Additionally, the relaxation of the up‐tick rule helps reduce the degree of mispricing. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
12.
Using the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk. 相似文献