G32 - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; GoodwillReturn

Prices of houses in flood risk zones are subject to a price discount reflecting the risk of losses caused by floods. First, the article establishes a framework for pricing flood risk using the expected discounted loss approach, based on the capital asset pricing model and the Gumbel mixture model of estimated likelihood and impacts of flood risk events. The measure advantage is dimensionality reduction and interpretability. Second, the resulting measure of flood risk is tested to assess whether it can explain differences in house prices using a large data sample from the Czech Republic in 2024. I show that the flood risk measure, expected discounted loss, can be a significant predictor of house prices, but its explanatory power depends critically on the data source used for determining flood risk zones. The results indicate that the market does price flood risk but tends to underestimate its magnitude. Moreover, it does not adjust the weight assigned to flood risk even after severe flood events. Lastly, I discuss the potential use of the obtained flood risk loss distributions for the calculation of Value at Risk and other risk measures of portfolios, including direct real estate or real estate used as collateral.

The study presents a holistic approach to modeling flood risk of real estate properties. The method combines the hydrological flow simulation model and a model of financial losses. Two use cases of the model are discussed. First, a stress testing method, based on historical scenario simulations, is presented. Next, a Value at Risk approach using the Generalized extreme value distribution and the Gumbel copula is discussed. Both methods are then tested on a large sample of Czech house data. The results show that the model can replicate the order of historical flood magnitudes under the historical scenarios. Moreover, the Value at Risk approach can generate scenarios unseen in recent history. The model could be a useful flood losses modeling tool for banks, insurance companies, real estate investment companies or state agencies. A special case for stressing credit risk parameters for mortgage portfolios is discussed in more detail.

The aim of this paper is to examine whether banks engage in profit smoothing using loan loss provisions under the new provisioning rules according to IFRS 9. Due to relatively loose definitions of provisioning principles and use of macroeconomic predictions under IFRS 9, there is certain managerial discretion expected allowing banks to reduce the variability of profits over time using loan loss provisions. The hypothesis that banks use loan loss provisions to smooth their profits under IFRS 9 was tested with panel regression analysis on the panel of 27 EU member countries for period 1Q2015 – 2Q2021. The evidence of profit smoothing was not confirmed neither in IFRS 9, nor in IAS 39 period, therefore, the hypothesis was rejected on 1% significance level.