Estonia FMVM Equity Risk Premium Decomposition Dataset (2015–2024)
Description
This dataset provides quarterly estimates of Estonia’s equity risk premium (ERP) and its structural components over the period 2015–2024, constructed using a multi-premium framework based on the Frontier Market Valuation Model (FMVM). The dataset decomposes the ERP into four additive layers—sovereign risk (CRP), liquidity risk (LP), behavioral risk (BP), and institutional-quality risk (IQP)—to identify the distinct channels that influence required returns in a small, open Euro-area market. The dataset includes three components: (1) Quarterly FMVM series for Estonia (2015Q1–2024Q4), containing Rf, GERP, CRP, LP, BP, IQP, and the final cost of equity. (2) Structural-period classification, assigning each quarter to one of four Baltic macro-financial regimes (post-crisis stabilization, AML reforms, COVID-19 shock, energy-security shock). (3) Baltic comparison panel (Estonia, Latvia, Lithuania), enabling cross-country benchmarking of sovereign, liquidity, behavioral, and institutional premia. Liquidity and behavioral premia are constructed from monthly microstructure and volatility–sentiment factors aggregated to quarterly frequency. Institutional-quality premia are derived from WGI and CPI governance indicators. Sovereign risk premia follow Damodaran’s rating- and spread-based methodology. All variables are standardized for comparability across time and countries. This dataset supports empirical analysis of the cost of equity in thin but institutionally strong markets and is designed for transparency and replicability. It accompanies the paper “Estonia’s Equity Risk Premium in a Baltic Comparative Perspective: A Decomposition of Sovereign, Liquidity, Behavioral, and Institutional Premia.” Researchers may use the dataset for equity valuation, risk decomposition, comparative capital-market analysis, or policy assessment related to liquidity and institutional reform.
Files
Steps to reproduce
Collect global inputs: Risk-free rate (Rf): quarterly 10-year U.S. Treasury yield. Global equity risk premium (GERP): historical and implied premia from MSCI World, MSCI Europe, and S&P 500. Obtain sovereign-risk data: Quarterly sovereign bond spreads or CDS spreads for Estonia, Latvia, and Lithuania. Compute the sovereign risk premium (CRP) using Damodaran’s method: CRP = Spread × (σ_equity / σ_bond), where volatilities are estimated from daily price series. Construct monthly liquidity factors: From Nasdaq Baltic data, extract turnover ratios, Amihud illiquidity, bid–ask spreads, zero-return days, and free-float or concentration metrics. Standardize each measure and combine them into a monthly liquidity factor. Aggregate liquidity to quarterly frequency: For each quarter, average the three monthly liquidity factors. Convert the quarterly factor into a liquidity premium (LP) using a calibrated linear scaling. Construct monthly behavioral factors: Estimate daily conditional volatility using a GARCH/EGARCH model. Combine volatility measures with sentiment indicators (consumer confidence, economic sentiment, news tone) to form a monthly behavioral factor. Average monthly values to obtain the quarterly behavioral premium (BP). Build institutional-quality premium (IQP): Use WGI and CPI scores to form an institutional factor. Interpolate annual data to quarterly frequency. Map the institutional factor to IQP via a linear scaling calibrated to cross-country differences. Assemble quarterly FMVM series: Combine Rf, GERP, CRP, LP, BP, and IQP to compute the total ERP and final cost of equity for 2015–2024. Apply structural-period labels: Assign each quarter to one of four regimes: stabilization, AML reforms, COVID-19 shock, energy-security shock. Create the comparison panel: Repeat steps 2–7 for Latvia and Lithuania. Merge all three countries into a unified dataset for cross-country benchmarking.
Institutions
- Universita Ca' Foscari