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Nezhelskii, Maksim UL

Doctoral thesis (2022)

This thesis consists of three main chapters, which study different topics of financial economics. The first two chapters are applied theory studies of heterogenous agents in continuous time, where the ... [more ▼]

This thesis consists of three main chapters, which study different topics of financial economics. The first two chapters are applied theory studies of heterogenous agents in continuous time, where the primary focus is endogenised portfolio choice of risky assets by agents in a general equilibrium framework. While chapter 1 studies risky-asset allocation in general, trying to match inequality data, chapter 2 models housing choice and studies the effects of different shocks on the real estate market. These first two chapters represent working papers which are written jointly with Christos Koulovatianos. Chapter 3 is an empirical paper on post-earnings announcement drift and how to better capture this anomaly using a bigger set of publicly available information. The third working paper is written jointly with Anna Ignashkina. Chapter 1 is entitled “Income and wealth inequality in heterogeneous-agent models in con- tinuous time.” In this chapter we analyse wealth inequality and how it is affected by the heterogeneity of the risk-taking pattern. Wealth inequality in the United States reached un- precedented levels over the last thirty years. The puzzle of the heavy tail of wealth distribution remains unresolved. We build a heterogeneous-agent model in continuous time with endogenous portfolio choice to test if risk-taking of the wealthy can explain the thick upper tail of wealth distribution for the US data. We incorporate the recent evidence of Guvenen et al. (2014) of income process’s non-normality in our model. We find that asset holdings play an important role in explaining increased inequality, especially when accompanied by non-normal income process. In both general equilibrium and partial setting we show that the non-normality of the income process contributes significantly to the formation of the convex risk-taking pattern against income. We also find that the rise in volatility of capital markets observed in the last 30 years can explain trends in inequality and interest rates. Chapter 2 is entitled “A Heterogeneous-Agent Model of Household Mortgages in Luxem- bourg: Responses to the Covid-19 Shock.” As it is well-known, the covid-19 pandemic lockdowns 8 did not have an impact on every worker in the same way. More social professions were affected in a more adverse manner by the lockdowns, experiencing severe income losses, while many ser- vices professions could continue working remotely as before, experiencing no income losses. In order to study the impact of these asymmetric idiosyncratic income shocks on household balance sheets in Luxembourg and on house prices, we calibrate a continuous-time heterogeneous-agent model of homeownership to pre- and post-covid income data. We compute the transition dy- namics of the net-worth distribution of households and study alternative scenarios of shocks to the mortgage rates that may stem from overall credit market conditions and central-bank policies. Our general results are that the mortgage market in Luxembourg is resilient. Yet, our model raises alert for some vulnerable households and provides a tool for policy evaluation in the future. Chapter 3 is entitled “Information aggregation and post-earnings announcement drift.” In this chapter we propose a new measure of surprise information that aggregates different signals coming together with earnings reports, complementing the standard earnings-surprise measure for analysis of Post-earnings announcement drift (PEAD). We find that new factors, such as revenue surprises and aggregated non-financial information available in earnings reports, are important determinants of post-earnings returns. Surprisingly, these new factors amplify, rather than mitigate, the PEAD anomaly. In dynamic portfolios, weekly returns to PEAD increase by 72 basis points, if more financial metrics are taken into account, compared to the standard approach. Similarly, with analyses of textual metrics, we demonstrate that changes in the text are associated with a longer drift. [less ▲]

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