Country Risk and Cost of Equity Dataset for Romania (with Serbia and Hungary Benchmarks), 2015–2024
Description
This dataset contains the quarterly implementation of the Frontier Market Valuation Model (FMVM) for Romania over the period 2015–2024, together with benchmark FMVM series for Serbia and Hungary. The FMVM decomposes the equity risk premium into four additive components: Country Risk Premium (CRP), Liquidity Premium (LP), Behavioral Premium (BP), and Institutional Quality Premium (IQP). These components are combined with global market factors to compute a quarterly FMVM-implied cost of equity for each country. The Romania_FMVM_Quart.xlsx file provides the complete quarter-by-quarter dataset for Romania, including all FMVM inputs (Rf, GERP, CRP, LP, BP, IQP) and the resulting FMVM cost of equity. It captures Romania’s transition from post-2015 stabilization to the pre-COVID period, the COVID-19 shock, and the subsequent energy/Ukraine crisis, allowing the analysis of structural changes in risk pricing. The Romania_FMVM_Structural_Changes.xlsx file summarizes FMVM component averages across four structural periods: (1) 2015–2017 stabilization, (2) 2018–2019 FTSE preparation, (3) 2020–2021 COVID shock, and (4) 2022–2024 energy and geopolitical tensions. These aggregates allow researchers to study how sovereign, liquidity, behavioral, and institutional risks evolve under different macro-financial regimes. The FMVM_Compare_Romania_Serbia_Hungary.xlsx file provides a cross-country comparison dataset. Serbia represents a higher-risk frontier benchmark with elevated liquidity and institutional premia, while Hungary represents a more stable EU emerging-market benchmark with lower CRP and LP relative to Romania. This structure enables multi-country FMVM analysis and highlights Romania’s intermediate position between emerging and frontier market characteristics. Together, these three files offer a transparent and replicable data foundation for research on country risk, cost of equity estimation, market segmentation, and comparative valuation in Central and Eastern Europe. The dataset is designed for academic publication, policy analysis, and applied finance research requiring multi-premium risk decomposition beyond traditional CAPM or sovereign-spread models.
Files
Steps to reproduce
This dataset can be fully reproduced using publicly available data and the FMVM methodology described in the paper. Follow the steps below: 1. Collect Global Financial Inputs Obtain quarterly values for the U.S. 10-year Treasury yield (Rf) and the global equity risk premium (GERP) from Damodaran’s data library. These values are common across all three countries. 2. Assign Sovereign Default Spreads and CRP Retrieve Romania’s, Serbia’s, and Hungary’s sovereign ratings and corresponding default spreads from Damodaran’s “Country Default Spreads and Risk Premiums” table for each year. Convert default spreads into annual Country Risk Premiums (CRP) using: CRP=Default Spread×1.34 For Hungary, apply a 40-basis-point adjustment relative to Romania to reflect its stronger credit rating. 3. Construct Liquidity Premium (LP) Using stock-exchange statistics and regional liquidity characteristics, assign quarterly LP values that reflect market depth and trading conditions. Romania’s LP varies by macro-regime; Serbia’s LP is consistently higher due to thin liquidity; Hungary’s LP is lower and smoother, with adjustments for COVID-period stress. 4. Construct Behavioral Premium (BP) Estimate BP using market volatility, sentiment conditions, and behavioral sensitivity. Romania’s BP peaks during COVID-19; Serbia’s BP remains structurally elevated; Hungary’s BP remains modest except during volatility spikes. Insert these quarterly BP values directly into the dataset. 5. Construct Institutional Quality Premium (IQP) Obtain annual governance indicators (WGI, CPI) and normalize them into quarterly IQP series. Romania shows gradual improvement post-2022; Serbia retains higher institutional risk; Hungary’s institutional premium increases slightly over time. Interpolate annual values to quarterly frequency. 6. Compute FMVM Cost of Equity For each country and quarter, compute the FMVM-implied cost of equity using: Ke,tFMVM=Rf,t+GERPt+CRPt+LPt+BPt+IQPt Populate this for all quarters from 2015Q1 to 2024Q4. 7. Create Structural-Period Averages Group Romania’s quarterly data into four structural periods: (1) 2015–2017; (2) 2018–2019; (3) 2020–2021; (4) 2022–2024. Compute average CRP, LP, BP, IQP, and FMVM cost of equity for each period. 8. Build the Cross-Country Comparison File Combine Romania, Serbia, and Hungary quarterly FMVM series into a unified dataset with a country identifier. Ensure column consistency: Quarter, Rf, GERP, CRP, LP, BP, IQP, FMVM. Following these steps exactly reproduces all three Excel files: the Romania quarterly dataset, the structural-change summary, and the cross-country FMVM comparison dataset.
Institutions
- Universita Ca' Foscari