shiva hallaji

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.

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.

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.

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.

This study examines the accuracy of Heterogeneous Autoregressive (HAR) models in forecasting the Conditional Value-at-Risk (CVaR) of Exchange-Traded Funds (ETFs) on the Tehran Stock Exchange. The significance of this study stems from the need for better risk management in financial markets, where volatility and jumps significantly affect investment decisions.

Data from nine equity, index, and fixed-income funds were analyzed intraday with high frequency (daily and fifteen-minute intervals) from 2019 to 2022. Three main families of HAR models were evaluated by considering the relevant variables.

The results revealed that models based on second-order variations outperformed others in forecasting Realized Volatility (RV). Additionally, CVaR prediction was more accurate for index funds than for equity and fixed-income funds, with the HARQ model demonstrating superior performance.

Originality/Value: 

This study investigates the application of HAR models in predicting ETF risks and provides a novel framework for risk management and investment decision making, particularly in the Iranian financial market.