The US Financial Market Uncertainty and Its Spillover to European Stock Markets

• Thesis title in Czech: Nejistota na Finančních Trzích v USA a její Přelévání na Evropské Akciové Trhy
• Thesis title in English: The US Financial Market Uncertainty and Its Spillover to European Stock Markets
• Key words: Finanční trh, Spojené státy americké, přeliv, efekt, nejistota, finance, ekonomie, trh, Evropa, medzinárodní efekt
• English key words: Financial market, United States, Spillover, effect, Uncertainty, Finance, Economics, Market, Europe, International
• Academic year of topic announcement: 2019/2020
• Thesis type: diploma thesis
• Thesis language: angličtina
• Department: Institute of Economic Studies (23-IES)
• Supervisor: prof. Roman Horváth, Ph.D.
• Author: hidden - assigned by the advisor
• Date of registration: 09.06.2020
• Date of assignment: 09.06.2020
• Date and time of defence: 15.09.2021 09:00
• Venue of defence: Opletalova - Opletalova 26, O314, Opletalova - místn. č. 314
• Date of electronic submission: 27.07.2021
• Date of proceeded defence: 15.09.2021
• Opponents: prof. Ing. Evžen Kočenda, M.A., Ph.D., DSc.

References

Fraiberger, S. P., Lee, D., Puy, D., & Rancier, R. (2018). Media sentiment and international asset prices. The
World Bank.

Baker, S. R., Bloom, N., and Davis, S. J., 2016, Measuring Economic Policy Uncertainty, Quarterly Journal of
Economics, 131 (4), 1593-1636.

Baker, M., Wurgler, J. (2007). “Investor sentiment in the stock market.” Journal of Economic
Perspectives 21, 129-151.

Jordà, O, 2005, “Estimation and Inference of Impulse Responses by Local Projections American Economic
Review 95(1), March 2005, 161-182

Tetlock, P. C., 2007, “Giving content to investor sentiment: The role of media in the stock market,” Journal of
Finance, 62(3), 1139–1168

Caggiano, G., Castelnuovo, E., & Figueres, J. M. (2020). Economic policy uncertainty spillovers in booms and
busts. Oxford Bulletin of Economics and Statistics, 82(1), 125-155.

Belke, A. and Osowski, T. (2019). ‘International effects of Euro area versus U.S. policy uncertainty: a FAVAR
approach’, Economic Inquiry, Vol. 57, pp. 453–481

Preliminary scope of work in English

Motivation:
In times of globalization, the countries around the world are being more and more connected. This connection applies also on asset markets, where the behavior of subjects on the market in one country can lead to affecting the market on the other side of the world. In this paper I aim to study uncertainty built on the US financial markets and how its spillover affects mainly Europe, but also other parts of the world.

Recent studies were primarily focusing on economic policy uncertainty index. Baker et al. (2016) addressed question whether business cycles are driven by economic policy uncertainty or how much is the macroeconomic performance sensitive on this uncertainty. Caggiano et al. (2020) focused their study on investigating economic policy uncertainty as well, although in terms of spillover to Canadian economics. On the other hand, Smales (2019) studied spillover effect of geopolitical uncertainty to stock markets and how it affects demand and supply for energy resources.

Although the focus in recent years was not aimed specifically on financial market uncertainty and especially on its international spillover effect. Horvath and Kapounek (2020) developed a new monthly index of financial market uncertainty on the US market. It is based on articles of eleven major US newspaper. One of good features what this index provides is the amount of various sub-indexes which can be used to build more detailed analysis and it provides us with historical data as well.

Hypotheses:
1. Hypothesis #1: The Effect of US Financial Market Uncertainty Spillover on European Financial Markets
2. Hypothesis #2: The US Financial Market Uncertainty Effect on European Economics
3. Hypothesis #3: Comparison of The US Financial Market Uncertainty Spillover on Different Major Markets

Methodology:
In order to study connection between the US financial market uncertainty and international financial markets and European major economic variables, I will follow approach used by Fraiberger et al (2018). They used local projection method (Jordà, 2005) to estimate cumulative response of asset prices to media sentiment shocks.

According to Jordà (2005), impulse response can be calculated by a sequence of projections. In contrast to more fundamental VAR estimation, coefficients are directly estimated so that standard errors obtained from estimation provide us with more efficient inference. In his research he proves that local projection method compared to VAR method is more robust to misspecification and interpretation of results is more straightforward.

Following Fraiberger et al. (2018), the dependent variable in my model will be the cumulative asset market returns in particular country between day t and (t+h). The independent variables in my model will be variable for the uncertainty at the US financial market itself and various controlling variables ensuring the presence of volatility, liquidity within the market, country-fixed effect and daily asset market returns.

For estimation of this model, I am going to use OLS estimation. The error term in the local projection framework
follows MA process of order h-1. Therefore, the Newey and West (1987) estimator will be used for correction of
autocorrelation and heteroskedasticity.

Expected Contribution:
I will conduct a study of international spillover effect coming from uncertainty on the US financial market. In contrast to previous studies, I will focus mainly on financial market uncertainty and study different parts of US market more deeply. Most of the studies in past were focusing on studying effect of uncertainty within the country. My aim is to focus more on international aspect of this phenomena.

Outline:
1. Motivation: I will discuss why I want to focus on the US financial market uncertainty and its spillover effect
2. Financial markets: I will briefly describe the situation on the US and European financial markets.
3. Data: I will explain index what I will be using as well how it was developed.
4. Methods: I will explain local projection method and equation of my model more deeply.
5. Results: I will discuss my baseline regressions and robustness checks.
6. Conclusion: I will summarize my findings.