G19 - General Financial Markets: OtherReturn

The tourism industry has flourished considerably over the years in Turkey and has competed with top international destinations. This study aims to identify the structural breaks specific to Turkey with a developing capital market and examine the causality relationship between tourism index return and financial, macroeconomic and tourism industry development indicators from January 2005 to February 2022. The MS-VAR econometric model based on two regimes detects nonlinear and asymmetrical structures in the dataset. Our findings indicate local effects of shocks on financial and macroeconomic indicators during regime transition periods. Furthermore, there are unidirectional causality relationships between real exchange rate, tourism index returns and the value of trading volume, but no relationship is found between tourism index return and other indicators, namely, bank loans granted to the tourism industry and tourist arrivals.

The paper integrates elements of microstructural network models of the banking sector developed by Cont and Shaanning (2017) and Duarte and Eisenbach (2018) to simulate endogenously the fire sales contagion channel of systemic risk. The scale of the effect is illustrated on the EBA supervisory stress test results for 2018, based on which the secondary impact of fire sales increases aggregate losses by 69%. The model is used to identify banks with the highest contribution to systemic risk. Alternative systemicity predicators are proposed based on the results. The second section discusses the ability of the model to incorporate behavioural and regulatory aspects associated with the systemic risk, with the main focus on the impact of (i) calibration of regulatory leverage ratio limits, (ii) leverage ratio targeting, (iii) decrease in market liquidity, and (iv) change in the shape of the market price impact function on the dynamics of systemic losses due to the fire sales contagion.

The aim of the article is to answer the question if the Czech stock market price dynamics is generated by non-linear deterministic dynamic process. To solve this complex problem requires using sophisticated computational operations to analyze huge amount of data input. To overcome this obstacle the visual recurrence analysis is applied in this article. This method enables visualization of the state space reconstructed from a time series in the so called recurrent plot. Further, it quantifies various geometric structures occurred in recurrent plots and gives us more exact information about the nature of the underlying process generating the time series. This analysis is then applied to the most liquid stock returns and the Czech stock market index PX series