Forecasting volatility in Exchange Traded Funds in the Tehran Exchange (ETF) using realized volatility jump models (HAR, HAR-J, HARQ, HARQ-J)

Subject Areas : Risk Management

Shiva Hallaji ¹ , Mahdi Madanchi Zaj ² , Fereydoun Ohadi ³ , Hamidreza Vakilifard ⁴

1 - Department of Financial Management, Islamic Azad University Science and Research Branch,Tehran,Iran
2 - Department of Financial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
3 - Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
4 - Department of Accounting, Islamic Azad University Science and Research Branch,Tehran,Iran

Received: 2023-11-18 Accepted : 2024-09-15 Published : 2024-09-15

Keywords: Forecasting, Jump, Volatility,

Abstract :

Objective: This study examines the prediction of volatility through jump risk in Exchange Traded Funds (ETFs) on the Tehran Stock Exchange, aiming to play a crucial role in the pricing options of financial assets through volatility forecasting mechanisms.

Research Methodology: The study employs three main families of heterogeneous autoregressive (HAR) models for volatility forecasting, incorporating jumps within an econometric framework by estimating heterogeneous autoregressive models. By comparing various models, the study utilizes high-frequency data-based realized volatility to provide precise measurements. The data set includes daily and 15-minute interval data collected from six equity and fixed-income funds over the financial period from 2020 to 2022.

Findings: The results indicate that among the funds, the predictive power of volatility is higher in equity funds. Additionally, the findings reveal that the second-order heterogeneous autoregressive model with jumps (HAR-J) is the most effective heterogeneous autoregressive model for modeling and forecasting realized volatility, as determined by the mean squared error and quasi-likelihood criteria. Furthermore, strong evidence supports that second-power variation-based models outperform their counterparts in predicting realized volatility, offering more accurate forecasts and better volatility estimations than second-order heterogeneous autoregressive models.

Originality/Scientific Value: This study provides insights into the differences among heterogeneous autoregressive models, specifically within the heterogeneous autoregressive model group, so that its results can be used by analysts and fund managers for improved financial decision-making.