Examining Risk Perception and Cost of Capital in Emerging Market Projects Using the DCC-GARCH Model

Abstract

Objective: The aim of this study is to accurately estimate the cost of capital and manage risk in energy projects based on public-private partnerships (PPP) in emerging markets. Methodology: To explore the mechanism of determining capital costs and evaluating capital budgeting, Dynamic Conditional Correlation (DCC) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were employed. Additionally, the pure-play method was used as a tool for measuring systematic risk in energy-related projects in emerging markets. Findings: The results showed that the DCC-GARCH model is capable of more accurately predicting volatility and capital costs in emerging markets. Moreover, the use of the pure-play method for calculating systematic risk led to reduced volatility and improved capital cost estimation. The findings also indicated that market volatility and risks associated with economic and financial policies in emerging markets have a significant impact on capital costs. Conclusion: This study demonstrates that the application of advanced financial models such as DCC-GARCH and the pure-play method can lead to more accurate capital cost estimations in PPP projects and provide a better understanding of risks in emerging markets. These models offer valuable tools for risk management in the dynamic economic conditions of emerging markets.