An analysis of the volatility and return spillover of venture capital ETFs in the Tehran Stock Exchange

Subject Areas : Financial Econometrics and Quantitative Methods

Meysam Kaviani ¹ (Department of Management and Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran.)
Maryam Gavara ² (Department of Management and Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran.)
Zahra Nazari ³ (Department of Financial Management, Karaj Branch, Islamic Azad University, Karaj, Iran.)

Received: 2023-08-28 Accepted : 2024-01-10 Published : 2024-01-27

Keywords: Return Spillover, Venture ETFs, Volatility,

Abstract :

Purpose: Venture capital is an investment model for launching new businesses such as startups and small companies with long-term growth potential, and venture funds provide financial resources from the time the idea is formed to the end of the project. As the name suggests, high risk tolerance and courage are required to start and invest in these funds, and the purpose of this research is to investigate the fluctuations and overflow of returns between these funds and its benchmark index (total index). Research Methodology: This research has utilized time series data of five risky ETFs and index returns between 2018 and 2023. Heterogeneity models including generalized autoregressive conditional heteroskedasticity (GARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models have been employed for data analysis. Findings: The findings of the research indicate that all tradable ETFs have return overflow from the benchmark index to ETF returns. Additionally, the returns of tradable ETFs and the benchmark index exhibit stability in volatility, and the presence of asymmetric volatility shows that negative news has a greater impact on volatility compared to positive news. Originality/Scientific Value: The results of this research can offer venture fund managers and investors a novel perspective for evaluating the performance and risk of these funds by considering both positive and negative news using volatility models.

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