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Modeling Volatility Dynamics in the U.S. Public Real Estate Stock Market: Evidence from Conditional Volatility Decomposition and Sector Benchmarking
This study examines volatility dynamics between the U.S. public real estate stock market and the broader equity market, with a focus on sectoral conditional volatility sensitivity to market conditional volatility across different volatility regimes. Using daily stock price data from to , conditiona...
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| Format: | Thesis |
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AUC Knowledge Fountain
2026
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| Summary: | This study examines volatility dynamics between the U.S. public real estate stock market and the broader equity market, with a focus on sectoral conditional volatility sensitivity to market conditional volatility across different volatility regimes. Using daily stock price data from to , conditional volatilities of real estate stocks and the market index are estimated through a GARCH-framework that incorporates Monday and Friday dummy variables in both the conditional mean and conditional variance equations as controls for calendar-based anomalies. The estimated conditional volatilities are used to estimate volatility-to-volatility elasticities at the firm and aggregate levels and are benchmarked against other equity sectors. Granger causality tests are performed to further evaluate predictive volatility spillover between real estate and the market.
The results indicate that volatility elasticity is heterogenous across firms and sectors. For public real estate, aggregated sector volatility elasticities exhibit regime dependent dynamics, strengthening substantially under high-volatility conditions. Evidence of predictive volatility transmission is limited and concentrated among a small subset of firms. Weekday effects are statistically significant for a non-negligible subset of firms, particularly in the conditional variance equation, supporting their inclusion as controls.
The study contributes to the financial contagion literature by examining volatility linkages over a long-time horizon, allowing regime-dependent volatility relationships to be identified beyond a single crisis episode. |
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