We investigate the relationships among search symbols, trading performance, and investor participation. We use two specific datasets from Google Trends’ search volume index. The search volume by number ticker significantly predicts high returns and high investor participation when applied by active retail investors investing in large firms. This does not hold true for less active retail investors who use Chinese company name tickers as their search terms. Our results indicate that the heuristic usage of number tickers to search for company information helps active retail investors to obtain better trading performance compared with less active retail investors who use Chinese company name tickers.

Russian monetary policy has been challenged by large and continuous private capital outflows and a sharp drop in oil prices during 2014. Both contributed to significant depreciation pressures on the ruble and led the central bank to give up its exchange rate management strategy. Against this background, this work estimates a small open economy model for Russia, featuring an oil price sector and extended by a specification of the foreign exchange market to correctly account for systematic central bank interventions. We find that shocks to the oil price and private capital flows substantially affect domestic variables such as inflation and output. Simulations for the estimated actual strategy and alternative regimes suggest that the vulnerability of the Russian economy to external shocks can substantially be lowered by adopting some form of inflation targeting. Strategies to target the nominal exchange rate or the ruble price of oil prove to be inferior.

Bank regulators interfere with the efficient allocation of resources for the sake of financial stability. Based on this trade-off, I compare how different capital requirements affect default probabilities and the allocation of market shares across heterogeneous banks. In the model, banks‘ productivity determines their optimal strategy in oligopolistic markets. Higher productivity gives banks higher profit margins that lower their default risk. Hence, capital requirements indirectly aiming at high-productivity banks are less effective. They also bear a distortionary cost: Because incumbents increase interest rates, new entrants with low productivity are attracted and thus average productivity in the banking market decreases.

We implement a novel empirical strategy for measuring and studying a strong form of nationalism—the willingness to fight and die in a war for national independence—using name choices corresponding to a previous war leader. Based on data on almost half a million soldiers, we first show that having been given a first name that is synonymous with the leader(s) of the Croatian state during World War II predicts volunteering for service in the 1991–1995 Croatian war of independence and dying during the conflict. Next, we use the universe of Croatian birth certificates and the information about nationalism conveyed by first names to suggests that in ex-Yugoslav Croatia, nationalism rose continuously starting in the 1970s and that its rise was curbed in areas where concentration camps were located during WWII. Our evidence on intergenerational transmission of nationalism is consistent with nationalist fathers purposefully reflecting the trade-off between within-family and society-wide transmission channels of political values. We also link the nationalist values we proxy using first name choices to right-wing voting behavior in 2015, 20 years after the war.

Starting from well-known empirical stylized facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness, we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalized innovations. These generalized innovations may for example follow a Student t, a generalized hyperbolic, an alpha-stable or a Generalized Pareto distribution (GPD). Our results indicate that the GPD distribution provides the strongest signals for avoiding tail risks. This is not surprising as the GPD distribution arises as a limit of tail behaviour in extreme value theory and therefore is especially suited to deal with tail risks. Out-of-sample backtests on 11 years of DAX futures data, indicate that the dynamic tail-risk protection strategy effectively reduces the tail risk while outperforming traditional portfolio protection strategies. The results are further validated by calculating the statistical significance of the results obtained using bootstrap methods. A number of robustness tests including application to other assets further underline the effectiveness of the strategy. Finally, by empirically testing for second-order stochastic dominance, we find that risk averse investors would be willing to pay a positive premium to move from a static buy-and-hold investment in the DAX future to the tail-risk protection strategy.