Article
Environmental Sciences
Tianli Guo, Songbai Song, Weijie Ma
Summary: The study develops a new SETAR-GARCH model for groundwater depth forecasting, showing that combining GARCH model with SETAR model can improve interval forecasting performance, providing new insights for nonstationary and nonlinear groundwater depth prediction.
WATER RESOURCES RESEARCH
(2021)
Article
Engineering, Civil
Mohammad Nazeri Tahroudi, Rasoul Mirabbasi, Yousef Ramezani, Farshad Ahmadi
Summary: This study investigates two efficient approaches for bivariate simulation and compares their applicability in simulating the river discharge in Talezang Basin, Iran. The Copula-GARCH model is found to be more accurate than the optimized SVR model, with increased accuracy at the minimum and maximum values of the data.
WATER RESOURCES MANAGEMENT
(2022)
Article
Economics
Lajos Horvath, Lorenzo Trapani
Summary: We propose a family of CUSUM-based statistics to detect changepoints in the deterministic part of the autoregressive parameter in a RCA sequence. Our tests can be applied regardless of stationarity and the independence of error term and autoregressive coefficient. Weighted CUSUM statistics are introduced to ensure the detection of breaks at sample endpoints, and simulations show the effectiveness of our procedures. The theory is applied to financial time series.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2023)
Article
Economics
Zhanxiong Xu, Zhibiao Zhao
Summary: This study introduces an efficient estimator by constrainedly weighting information across quantiles, which can eliminate the effect of preliminary estimator and achieve good estimation efficiency simultaneously. Compared to the Cramer-Rao lower bound, the relative efficiency loss of the new estimator has a conservative upper bound close to zero in practical situations. Monte Carlo studies show that the proposed method has substantial efficiency gain and better prediction performance in empirical applications to GDP and inflation rate modeling.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2022)
Article
Statistics & Probability
Qianqian Zhu, Songhua Tan, Yao Zheng, Guodong Li
Summary: This article proposes a novel conditional heteroscedastic time series model using the framework of quantile regression processes in the ARCH(& INFIN;) form of the GARCH model. The model can provide varying structures for conditional quantiles of the time series across different quantile levels, including the commonly used GARCH model as a special case. The article introduces a self-weighted composite quantile regression estimator to remedy the accuracy deterioration at high quantile levels due to data scarcity. Simulation experiments and an empirical example illustrate the performance and usefulness of the new model.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2023)
Article
Meteorology & Atmospheric Sciences
Yousef Ramezani, Mohammad Nazeri Tahroudi, Carlo De Michele, Rasoul Mirabbasi
Summary: In this study, VAR-GARCH, copula, and copula-GARCH models were used for joint frequency analysis of storms in the Aras river basin in northwestern Iran. The VAR model was used to consider heteroskedasticity in the series, and two-dimensional copulas were used for bivariate analysis. The VAR-GARCH model showed higher accuracy in storm simulations compared to copula and copula-GARCH models. The generated curves from the analysis can be used as a flood warning system in the basin.
THEORETICAL AND APPLIED CLIMATOLOGY
(2023)
Article
Environmental Sciences
Huimin Wang, Songbai Song, Gengxi Zhang, Olusola O. Ayantobo, Tianli Guo
Summary: This study assesses the applicability of SV models to streamflow modeling in the Yellow River basin, and finds that SV models can better describe streamflow series with time-varying variance and accurately capture the occurrence of peak streamflow.
WATER RESOURCES RESEARCH
(2023)
Article
Economics
Christopher Oconnor
Summary: Standard methodologies used to identify vulnerable households rely on distributional assumptions that may lead to classification errors. This paper demonstrates that quantile models can improve this identification by relaxing these assumptions. Quantile models are robust and easy to implement, making them suitable for policymakers. Applying this strategy to data from Uganda shows that it more accurately identifies the future poor compared to standard approaches. The study highlights the benefits of relaxing distributional assumptions when identifying vulnerable populations.
ECONOMIC MODELLING
(2023)
Article
Engineering, Environmental
Babak Mohammadi, Saeid Mehdizadeh, Farshad Ahmadi, Nguyen Thi Thuy Lien, Nguyen Thi Thuy Linh, Quoc Bao Pham
Summary: This study focused on developing hybrid time series models to estimate air temperature parameters more accurately, with statistical metrics used to evaluate model performance. The results showed that the hybrid models outperformed the single models, and the combination of MLP and AR-ARCH models can provide more accurate temperature estimations. Additionally, temperature data from nearby stations can be utilized to predict the temperatures at desired locations.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Operations Research & Management Science
Rita Laura D'Ecclesia, Daniele Clementi
Summary: This study aims to identify the best approach to track equity returns implied volatility using parametric and ANN approaches, and the research shows that the ANN approach is the most accurate.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Economics
Patrick F. F. Patrocinio, Valderio A. A. Reisen, Pascal Bondon, Edson Z. Z. Monte, Ian M. M. Danilevicz
Summary: In this paper, we propose an M-quantile approach that combines quantile and M-regression to estimate the conditional volatility in the presence of abrupt observations or heavy-tailed distributions. Monte Carlo experiments demonstrate that the M-quantile approach outperforms M-regression and quantile methods in terms of robustness against additive outliers. The effectiveness of the method is illustrated using two financial datasets.
COMPUTATIONAL ECONOMICS
(2023)
Article
Business, Finance
Yanlin Shi
Summary: This paper discusses the inherent robustness of the generalized autoregressive score (GAS) model, demonstrating that consistent estimators can still be obtained even in the presence of large outliers. Simulation studies and empirical analysis confirm the superiority of GAS over GARCH models.
FINANCE RESEARCH LETTERS
(2022)
Article
Economics
Dong Jin Lee, Tae-Hwan Kim, Paul Mizen
Summary: This paper introduces a new method for analyzing the impact of shocks on time series using a quantile impulse response function (QIRF). By changing the quantile index, it is possible to observe the effects of shocks on the entire conditional distribution, especially at the upper and lower tails as well as the mean. In addition to proposing the QIRF, the paper also presents a new way to jointly estimate multiple quantile functions.
JOURNAL OF ECONOMIC DYNAMICS & CONTROL
(2021)
Article
Mathematics
Eunju Hwang
Summary: This paper examines stationary autoregressive models with heavy-tailed G-GARCH or augmented GARCH noises, deriving limit theory for the least squares estimator of the autoregression coefficient. The asymptotic distributions of the estimators are established for different tail indices of the G-GARCH innovations, and it is shown that the LSE consistency depends on the tail index. This work extends the existing limit theory by considering errors with conditional heteroscedastic variance and heavy tails, without imposing restrictions on the rate of rho(n).
Article
Business, Finance
Kaiji Motegi, Yoshitaka Iitsuka
Summary: This paper investigates the dynamic interdependence between geographically non-overlapping Japanese real estate investment trusts (J-REITs) in terms of their stock returns, considering property type and market returns. The study finds significant impacts of central J-REITs on local J-REITs in conditional mean, indicating potential arbitrage opportunities. After the COVID-19 crisis, the central-to-local impacts have become stronger for all property types, suggesting that portfolio diversification is harder to achieve during turmoil.
NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE
(2023)