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Concepts of Housing Affordability MeasurementsOriginal contributions
David Mazáček
FFA Working Papers 5:008 (2023)672 
This study investigates the challenges of affordable housing, delving into its concept and the diverse metrics used for measuring housing affordability, which influences the formulation of relevant affordable housing policies. The primary focus of this paper centers on defining the concept of affordable housing, exploring its implications for enhancing quality of life, and addressing the complexities involved in measuring its affordability accurately. Building upon the research, the paper proposes a possible optimal methodology for measuring housing affordability. This method suggests employing a price/rent-to-income ratio, encompassing a comprehensive...
A Comparison of Neural Networks and Bayesian MCMC for the Heston Model Estimation (Forget Statistics – Machine Learning is Sufficient!)Original contributions
Jiří Witzany, Milan Fičura
FFA Working Papers 5:007 (2023)690
The main goal of this paper is to compare the classical MCMC estimation method with a universal Neural Network (NN) approach to estimate unknown parameters of the Heston stochastic volatility model given a series of observable asset returns. The main idea of the NN approach is to generate a large training synthetic dataset with sampled parameter vectors and the return series conditional on the Heston model. The NN can then be trained reverting the input and output, i.e. setting the return series, or rather a set of derived generalized moments as the input features and the parameters as the target. Once the NN has been trained, the estimation...
The words have power: the impact of news on exchange ratesOriginal contributions
Teona Shugliashvili
FFA Working Papers 5:006 (2023)1290
Using the big data of news texts and a novel, news extended exchange rate model, we investigate the impact of media news on major exchange rates. To present the impact of the U.S. Dollar related news on EUR/USD and GBP/USD, we first use a machine learning model and detect which news topics relate to U.S. Dollar. Next, we calculate the attention to the U.S. Dollar related news topics over time. Eventually, we visualize how Exchange rates react to shocks in the attention to the U.S. Dollar related news topics. The impulse response functions of U.S. Dollar bilateral rates show that exchange rates respond to the U.S. Dollar related news and to the economic...
Changes to Bank Capital Ratios and their Drivers Prior and During COVID-19 Pandemic: Evidence from EUOriginal contributions
Pavel Jankulár, Zdeněk Tůma
FFA Working Papers 5:005 (2023)611
We contribute to literature on banks´ strategies to increasing capital requirements in the period of 2017-2021. We analyze a sample of 85 European banks and differentiate between subgroups according to bank's size, capitalization and riskiness. We examine their responses to higher capital requirements following the issuance of finalized Basel III reforms and increased regulatory and supervisory scrutiny after the COVID-19 outbreak. We found evidence that banks´ adjustments in the direction of higher capital ratio were more pronounced and faster in the COVID-19 period, and that they depended on banks´ specific characteristics and positions. Identified...
Copula-Based Trading of Cointegrated Cryptocurrency PairsOriginal contributions
Masood Tadi, Jiří Witzany
FFA Working Papers 5:004 (2023)995
This research introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies. To identify the most suitable pairs, the study employs linear and non-linear cointegration tests along with a correlation coefficient measure and fits different copula families to generate trading signals formulated from a reference asset for analyzing the mispricing index. The strategy's performance is then evaluated by conducting back-testing for various triggers of opening positions, assessing its returns and risks. The findings indicate that the proposed method outperforms buy-and-hold trading strategies in terms of both profitability...
Impact of size and volume on cryptocurrency momentum and reversalOriginal contributions
Milan Fičura
FFA Working Papers 5:003 (2023)710
We analyse how cryptocurrency size and trading volume impact the momentum and reversal dynamics of their returns. We show that the previously reported weekly return reversal occurs for small and illiquid coins only (t-stat = -7.31), while the large and liquid coins exhibit weekly momentum effect instead (t-stat = 2.33). Long-term returns exhibit reversal effects, which are, however, insignificant for the large and liquid coins. We further analyse the impact of high momentum on future cryptocurrency returns, measured as the distance of previous-week closing price from the k-week high. High momentum has not been analysed on cryptocurrency markets before,...
Key determinants of new residential real estate prices in PragueOriginal contributions
David Mazáček, Jiří Panoš
FFA Working Papers 5:002 (2023)936
The real estate residential market plays a crucial role in the economy and personal savings of a significant portion of the population. In recent years, the pricing of new apartments in Prague and other major European cities has experienced rapid growth, but also a sharp decline during the global financial crisis of 2008. The purpose of this paper is to describe the relationship between the selling price per square meter of new residential developments in Prague and the macroeconomic determinants, as well as real estate sector variables. The econometric model developed for this study is based on quarterly observations from 2005 to 2021 and utilizes...
Machine Learning Applications to Valuation of Options on Non-liquid MarketsOriginal contributions
Jiří Witzany, Milan Fičura
FFA Working Papers 5:001 (2023)582
Recently, there has been a considerable interest in machine learning (ML) applications to valuation of options. The main motivation is the speed of calibration or, for example, calculation of the credit valuation adjustments (CVA). It is usually assumed that there is a relatively liquid market with plain vanilla option quotations that can be used to calibrate (using an ML model) the volatility surface, or to estimate parameters of an advanced stochastic model. In the second stage the calibrated volatility surface (or the model parameters) are used to value given exotic options, again using a trained NN (or another ML model). The NNs are typically trained...