Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates

Choi, Ji Eun; Shin, Dong Wan

doi:10.1002/for.2530

Cited by 8 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The third model, proposed by Corsi and Reno (2012), is the LHAR-CJ model. In addition to return jumps, this model also captures the leverage effect where a negative return shock leads to larger volatility increase than a positive shock of the same size (see Choi & W, 2018;Wang et al, 2021, for instance). The LHAR-CJ model is specified as follows:…”

Section: The Lhar-cj Modelmentioning

confidence: 99%

Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators

Fei

Liu

Wen

2023

Journal of Forecasting

View full text Add to dashboard Cite

In this paper, we study the predictive performance of two kinds of volatility estimators: the realized volatility (RV) type and duration‐based ones. This is motivated by the theoretical and empirical support for these distinct estimators. We use intraday data for 218 component stocks of the CSI 300 index in the Chinese equity market from 2010–2019 and perform in‐ and out‐of‐sample 1‐, 5‐, and 22‐day ahead volatility forecasts from combinations of volatility models and these estimators. We show that, although empirically more efficient with the US data, the duration‐based estimators fail to compete statistically, or in terms of economic value, with RV‐type ones in the Chinese market. We perform a comprehensive set of simulations to rationalize these results and show that duration‐based estimators underperform as they cannot handle the occasional heightened level of volatility in the Chinese market.

show abstract

Section: The Lhar-cj Modelmentioning

confidence: 99%

Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators

Fei

Liu

Wen

2023

Journal of Forecasting

View full text Add to dashboard Cite

show abstract

“…We have chosen the 15% out-of-sample size because it is a very common choice in the forecasting literature (Cho and Shin, 2016;Choi and Shin, 2018;Kim and Shin, 2019; many others). The lag…”

Section: Out-of-sample Forecastmentioning

confidence: 99%

How to improve oil consumption forecast using google trends from online big data?: the structured regularization methods for large vector autoregressive model

Choi

Shin

2022

Communications for Statistical Applications and Methods

Self Cite

View full text Add to dashboard Cite

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

show abstract

“…The main advantage of RV is that it is directly observable. Therefore, a large body of literature describes and forecasts the RV of stock markets (see, e.g., Choi & Shin, 2018;Corsi, 2009;Engle & Gallo, 2006;Ghysels, Santa-Clara, & Valkanov, 2006;Hansen, Huang, & Shek, 2012;Qu & Ji, 2016;Santos & Ziegelmann, 2014;Wang, Pan, & Wu, 2017). It is worth noting that volatility is a crucial input of derivative pricing, hedging, portfolio selection, and risk management (see, e.g., Andersen, Bollerslev, & Diebold, 2007;Bollerslev, Hood, Huss, & Pedersen, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…It is worth noting that volatility is a crucial input of derivative pricing, hedging, portfolio selection, and risk management (see, e.g., Andersen, Bollerslev, & Diebold, 2007;Bollerslev, Hood, Huss, & Pedersen, 2017). Therefore, a large body of literature describes and forecasts the RV of stock markets (see, e.g., Choi & Shin, 2018;Corsi, 2009;Engle & Gallo, 2006;Ghysels, Santa-Clara, & Valkanov, 2006;Hansen, Huang, & Shek, 2012;Qu & Ji, 2016;Santos & Ziegelmann, 2014;Wang, Pan, & Wu, 2017). Although there are a large number of studies on volatility forecasting, forecasting volatility accurately is still a daunting task.…”

Section: Introductionmentioning

confidence: 99%

The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

Zhang

Wahab

et al. 2019

Journal of Forecasting

View full text Add to dashboard Cite

In this study, we explore the effect of cojumps within the agricultural futures market, and cojumps between the agricultural futures market and the stock market, on stock volatility forecasting. Also, we take into account large and small components of cojumps. We have several noteworthy findings. First, large jumps may lead to more substantial fluctuations and are more powerful than small jumps. The effect of cojumps and their decompositions on future volatility are mixed. Second, a model including large and small cojumps between the agricultural futures market and the stock market can achieve a higher forecasting accuracy, implying that large and small cojumps contain more useful predictive information than cojumps themselves. Third, our conclusions are robust based on various robustness tests such as the realized kernel, expanding forecasts, different forecasting windows, different jump tests, and different threshold values.

show abstract

Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates

Cited by 8 publications

References 39 publications

Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators

Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators

How to improve oil consumption forecast using google trends from online big data?: the structured regularization methods for large vector autoregressive model

The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

Contact Info

Product

Resources

About