Univariate GARCH Models for Forecasting Real Estate Volatility and Risk Prediction
(2023) NEKN02 20231Department of Economics
- Abstract
- The “Fundamental Review of the Trading Book” calculates the capital requirements using Expected Shortfall (ES) backtests since Value at risk (VaR) neglects losses in the tail. Additionally, real estate investments have increased significantly, which warrants an exploration of the asset class. This study applies eight different univariate GARCH models to six distinct property indices; Global, Asia Pacific, Europe, Latin America, Mid-East & Africa, and North America using three distributions, namely skewed normal distribution (snorm), skewed Student’s t-distribution (sstd) and skewed generalized error distribution (sged). Subsequently, the variance is forecasted one day ahead, which is used to derive VaR and ES. This approach is repeated at... (More)
- The “Fundamental Review of the Trading Book” calculates the capital requirements using Expected Shortfall (ES) backtests since Value at risk (VaR) neglects losses in the tail. Additionally, real estate investments have increased significantly, which warrants an exploration of the asset class. This study applies eight different univariate GARCH models to six distinct property indices; Global, Asia Pacific, Europe, Latin America, Mid-East & Africa, and North America using three distributions, namely skewed normal distribution (snorm), skewed Student’s t-distribution (sstd) and skewed generalized error distribution (sged). Subsequently, the variance is forecasted one day ahead, which is used to derive VaR and ES. This approach is repeated at two confidence levels and the resulting models are evaluated by backtesting procedures. Applying asymmetric conditional volatility models improves forecast accuracy in most cases and it emerges that the assumed distribution influences the results heavily. Specific recommendations are dependent on index makeup and its return structure. Estimating and backtesting ES adds value to this paper, as this is the requirement in new Basel regulations. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9123441
- author
- Martin, Maximilian LU and Cagetti, Dario LU
- supervisor
-
- Hans Byström LU
- organization
- course
- NEKN02 20231
- year
- 2023
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Real estate, GARCH, Expected Shortfall, Value at Risk, Backtesting
- language
- English
- id
- 9123441
- date added to LUP
- 2023-11-24 08:57:01
- date last changed
- 2023-11-24 08:57:01
@misc{9123441, abstract = {{The “Fundamental Review of the Trading Book” calculates the capital requirements using Expected Shortfall (ES) backtests since Value at risk (VaR) neglects losses in the tail. Additionally, real estate investments have increased significantly, which warrants an exploration of the asset class. This study applies eight different univariate GARCH models to six distinct property indices; Global, Asia Pacific, Europe, Latin America, Mid-East & Africa, and North America using three distributions, namely skewed normal distribution (snorm), skewed Student’s t-distribution (sstd) and skewed generalized error distribution (sged). Subsequently, the variance is forecasted one day ahead, which is used to derive VaR and ES. This approach is repeated at two confidence levels and the resulting models are evaluated by backtesting procedures. Applying asymmetric conditional volatility models improves forecast accuracy in most cases and it emerges that the assumed distribution influences the results heavily. Specific recommendations are dependent on index makeup and its return structure. Estimating and backtesting ES adds value to this paper, as this is the requirement in new Basel regulations.}}, author = {{Martin, Maximilian and Cagetti, Dario}}, language = {{eng}}, note = {{Student Paper}}, title = {{Univariate GARCH Models for Forecasting Real Estate Volatility and Risk Prediction}}, year = {{2023}}, }