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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.

Location-allocation in a supply chain is a strategic level decision due to high cost, impossibility of change and the scope of its impact on other decisions and activities, and the selection of suppliers and production and transportation policies is at a tactical level. Integrating different levels of supply chain decisions helps overall costs reduction and performance improvement. To this end, in this paper, a two-stage mean-conditional value at risk model is used to allocate locations and calculate the flow of materials and manufactured products of a multi-product-multi-level supply chain. In this model, distributors and suppliers face the possibility of disruption, and to prevent disruption on the side of suppliers, they can be equipped by spending additional costs. Sources of uncertainty in the model include shipping costs, end-user demand, and the possibility of disruption to distribution centers and suppliers. The research model uses the conditional value at risk along with the risk aversion factor to control the risk of long distances. The designed model is eventually transformed into a single-level linear programming with the help of Monte Carlo simulation. At the end, while solving a numerical example, the model is implemented and the related sensitivity analysis is presented.</em>