National Repository of Grey Literature 6 records found  Search took 0.00 seconds. 
Cusp catastrophe theory: Application to the housing market
Kořínek, Vojtěch ; Kukačka, Jiří (advisor) ; Nevrla, Matěj (referee)
The bachelor's thesis applies the stochastic cusp catastrophe model to the housing market of the United States. Weekly data over the period from 2007 to 2017 are used. The current catastrophe theory literature related to the housing market is reviewed, the models found are assessed and expanded. Specifically, we have identified three deficiencies of the catastrophe models applied to housing market in the current literature and our contribution lies in the elimination of these deficiencies. In order to satisfy the constant volatility assumption of the model, the state variable is normalized by the estimated volatility derived from GARCH. Furthermore, multiple control variables are added to the model to represent the activity of fundamentalists and chartists. The results suggest that the cusp catastrophe model fits the data better than the linear and logistic models. The normalization of the state variable improves the model performance while the introduction of the additional control variables does not produce better results. Keywords Housing market, catastrophe theory, stochastic cusp catastrophe model, hous- ing bubble, real estate, fundamental investors, speculation. 1
Price Determinants of Flats Purchased for the First Time in Prague
Pelnář, Daniel ; Cahlík, Tomáš (advisor) ; Nevrla, Matěj (referee)
Being able to correctly estimate the true intrinsic value of a flat is important for various economic agents. This paper is concerned with the price determinants of first-time- purchased flats in Prague. It is mostly about the hedonic pricing model and its applications using data from Vivus which is one of the larger flat developers operating in Prague. Ordinary least squares was the estimation method of choice in this study. The main results are as follows. The residual analysis showed no extremely overvalued or undervalued flats based on our chosen models. Moreover, the estimated increase in prices of average sized flats in Uhříněves was 36.76% from 2017 to 2019. This is a much larger magnitude if compared with the period of the financial crisis where an average sized flat in Na Vyhlídce increased in its price by 12.83% from 2007 to 2009. It is interesting to see that even during a recession, the prices of Prague flats were raising.
Impact of Terrorism on Stock Markets
Koščo, Marek ; Červinka, Michal (advisor) ; Nevrla, Matěj (referee)
Terrorism generally induces negative mood in the society. Financial markets performance exhibits the contingency on the mood of their trading parti- cipants. The thesis enhances the understanding of this interrelated entities by analysing the situation from 2000 to 2015 at the 20 world largest mar- kets. Their composite indices are put under scrutiny employing a multifactor model, a difference equation and a logit model. The impact is confirmed and further discussed, while the logit model provides a simple framework for forecasting index returns just after an attack with more than 25 casualties. Keywords global financial markets, terrorism, multifactor model, difference equation, logit model
Modely dynamické podmíněné korelace a jejich aplikace při mitigaci rizika portfolia
Ševčík, Martin ; Frýd, Lukáš (advisor) ; Nevrla, Matěj (referee)
This bachelor thesis investigates asymmetry in returns of corn, gold and crude oil (both spot and futures) and hedging effectiveness of these commodities when employing DCC family models for hedge ratio estimation. The asymmetry in conditional variances was found to be significant only in case of crude oil spot and futures returns and asymmetry in conditional correlation of spot and futures returns was not shown to be significant in neither of the investigated commodities. With respect to the hedging performance, we conclude that differences in hedging performance measured by hedging effectiveness index are negligible and thus do not support superiority of DCC family models over OLS, which served as a benchmark. Historical Value at Risk, on the contrary, identified the DCC with asymmetry in conditional variance (despite asymmetry not being significant) to be appropriate for corn hedging, however not for the other two commodities, where the OLS based hedge ratio performed similarly or even better than the DCC family models. The main contribution of the thesis thus lays in empirical investigation of asymmetry in returns of selected commodities and testing hedging potential of DCC family based hedge ratio.
Using the log-periodic power-law model to detect bubbles in stock market
Kožuch, Samuel Maroš ; Krištoufek, Ladislav (advisor) ; Nevrla, Matěj (referee)
Stock market crashes were considered as an chaotic even for a long time. However, more than a decade ago a specific behavior was observed, which accompanied most of the crashes: an accelerating growth of price and log-periodic oscillations. The log-periodic power law was found to have an ability to capture the behavior prior to crash and even predict the most probable time of the crash. The log-periodic power law requires a complicated fitting method to find the estimated values of its seven parameters. In the thesis, an alternative simpler fitting method is proposed, which is equally likely to find the true estimates of parameters, thus generating an equally good fit of log-periodic power law. Furthermore, four stock indices are fitted to log-periodic power law and examined for possible log-periodic oscillations in different time periods, including a very recent period of 2017. In all of the analyzed indices, a log-periodic oscillations could be observed. One index, analyzed in past period, was fitted to log-periodic power law, which was able to capture the oscillations and predict the critical time of crash. In the rest of the selected stocks, which were analyzed in a recent period, the critical time was estimated with varying results.
Systemic Risk in the European Financial and Energy Sector: Dynamic Factor Copula Approach
Nevrla, Matěj ; Baruník, Jozef (advisor) ; Buzková, Petra (referee)
In the thesis we perform analysis of systemic risk in the financial and energy sector in Europe. As the econometric tool for estimating dependencies across the subjects we employ factor copula model with GAS dynamics of Oh & Patton (2013b). We apply this model to daily CDS spreads. Based on the estimated results we perform Monte Carlo simulations in order to obtain future values of CDS spreads and measure probability of systemic events. We conclude that substantially higher systemic risk is present within the financial sector. We also find that the most systemic companies from both sectors come from Spain. JEL Classification C53, C55, C58, G17 Keywords Credit Default Swap, Energy Sector, Factor Copula, Financial Sector, Generalized Autore- gressive Score Model, Systemic Risk Author's e-mail Supervisor's e-mail

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