Threshold heterogeneous autoregressive modeling for realized volatility

Analysis of Realized Volatility in Tehran Stock Exchange using Heterogeneous Autoregressive Models Approach

Objective: The present study aims atinvestigating the behavior of realized volatility for high-frequency data of Tehran Stock Index from April28th, 2012 to August 8th, 2018. Methods: Three different types of HAR models including of HAR-RV-CJ, HAR-RV and HAR-RVJ were used to analyze the Realized Volatility. Results: The obtained results of three diverse models revealed that the estimated Reali...

Modeling Gold Volatility: Realized GARCH Approach

F orecasting the volatility of a financial asset has wide implications in finance. Conditional variance extracted from the GARCH framework could be a suitable proxy of financial asset volatility. Option pricing, portfolio optimization, and risk management are examples of implications of conditional variance forecasting. One of the most recent methods of volatility forecasting is Real...

A Threshold Stochastic Volatility Model with Realized Volatility

Rapid development in the computer technology has made the financial transaction data visible at an ultimate limit level. The realized volatility, as a proxy for the ”true” volatility, can be constructed using the high frequency data. This paper extends a threshold stochastic volatility specification proposed in So, Li and Lam (2002) by incorporating the high frequency volatility measures. Due t...

Performance of Heterogeneous Autoregressive Models of Realized Volatility: Evidence from U.S. Stock Market

This paper deals with heterogeneous autoregressive models of realized volatility (HAR-RV models) on high-frequency data of stock indices in the USA. Its aim is to capture the behavior of three groups of market participants trading on a daily, weekly and monthly basis and assess their role in predicting the daily realized volatility. The benefits of this work lies mainly in the application of he...

Har Modeling for Realized Volatility Forecasting