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Lackmustest Monte Paschi
(2017)
We theoretically and empirically study large-scale portfolio allocation problems when transaction costs are taken into account in the optimization problem. We show that transaction costs act on the one hand as a turnover penalization and on the other hand as a regularization, which shrinks the covariance matrix. As an empirical framework, we propose a flexible econometric setting for portfolio optimization under transaction costs, which incorporates parameter uncertainty and combines predictive distributions of individual models using optimal prediction pooling. We consider predictive distributions resulting from highfrequency based covariance matrix estimates, daily stochastic volatility factor models and regularized rolling window covariance estimates, among others. Using data capturing several hundred Nasdaq stocks over more than 10 years, we illustrate that transaction cost regularization (even to small extent) is crucial in order to produce allocations with positive Sharpe ratios. We moreover show that performance differences between individual models decline when transaction costs are considered. Nevertheless, it turns out that adaptive mixtures based on high-frequency and low-frequency information yield the highest performance. Portfolio bootstrap reveals that naive 1=N-allocations and global minimum variance allocations (with and without short sales constraints) are significantly outperformed in terms of Sharpe ratios and utility gains.
We propose a long-run risk model with stochastic volatility, a time-varying mean reversion level of volatility, and jumps in the state variables. The special feature of our model is that the jump intensity is not affine in the conditional variance but driven by a separate process. We show that this separation of jump risk from volatility risk is needed to match the empirically weak link between the level and the slope of the implied volatility smile for S&P 500 options.
We develop a state-space model to decompose bid and ask quotes of CDS into two components, fair default premium and liquidity premium. This approach gives a better estimate of the default premium than mid quotes, and it allows to disentangle and compare the liquidity premium earned by the protection buyer and the protection seller. In contrast to other studies, our model is structurally much simpler, while it also allows for correlation between liquidity and default premia, as supported by empirical evidence. The model is implemented and applied to a large data set of 118 CDS for a period ranging from 2004 to 2010. The model-generated output variables are analyzed in a difference-in-difference framework to determine how the default premium, as well as the liquidity premium of protection buyers and sellers, evolved during different periods of the financial crisis and to which extent they differ for financial institutions compared to non-financials.
Litmus test Monte Paschi
(2017)
We establish a benchmark result for the relationship between the loanable funds and the money-creation approach to banking. In particular, we show that both processes yield the same allocations when there is no uncertainty and thus no bank default. In such cases, using the much simpler loanable funds approach as a shortcut does not imply any loss of generality.
After the Lehman-Brothers collapse, the stock index has exceeded its pre-Lehman-Brothers peak by 36% in real terms. Seemingly, markets have been demanding more stocks instead of bonds. Yet, instead of observing higher bond rates, paradoxically, bond rates have been persistently negative after the Lehman-Brothers collapse. To explain this paradox, we suggest that, in the post-Lehman-Brothers period, investors changed their perceptions on disasters, thinking that disasters occur once every 30 years on average, instead of disasters occurring once every 60 years. In our asset-pricing calibration exercise, this rise in perceived market fragility alone can explain the drop in both bond rates and price-dividend ratios observed after the Lehman-Brothers collapse, which indicates that markets mostly demanded bonds instead of stocks.
Master of science in international economics and economic policy : guidelines winter term 2017/2018
(2017)
Public employees in many developing economies earn much higher wages than similar privatesector workers. These wage premia may reflect an efficient return to effort or unobserved skills, or an inefficient rent causing labor misallocation. To distinguish these explanations, we exploit the Kenyan government’s algorithm for hiring eighteen-thousand new teachers in 2010 in a regression discontinuity design. Fuzzy regression discontinuity estimates yield a civil-service wage premium of over 100 percent (not attributable to observed or unobserved skills), but no effect on motivation, suggesting rent-sharing as the most plausible explanation for the wage premium.
The German savings and cooperative banks of the 19th century were precursors of modern microfinance. They provided access to financial services for the majority of the German population, which was formerly excluded from bank funding. Furthermore, they did this at low costs for themselves and affordable prices for their clients. By creating networks of financially viable and stable financial institutions covering the entire country, they contributed significantly to building a sound and “inclusive” financial infrastructure in Germany. A look back at the history of German savings and cooperative banks and combining these experiences with the lessons learned from modern microfinance can guide current policy and be valuable for present and future models of microfinance business.
Missed opportunity
(2017)
For some time now, structural macroeconomic models used at central banks have been predominantly New Keynesian DSGE models featuring nominal rigidities and forwardlooking decision-making. While these features are widely deemed crucial for policy evaluation exercises, most central banks have added more detailed characterizations of the financial sector to these models following the Great Recession in order to improve their fit to the data and their forecasting performance. We employ a comparative approach to investigate the characteristics of this new generation of New Keynesian DSGE models and document an elevated degree of model uncertainty relative to earlier model generations. Policy transmission is highly heterogeneous across types of financial frictions and monetary policy causes larger effects, on average. The New Keynesian DSGE models we analyze suggest that a simple policy rule robust to model uncertainty involves a weaker response to inflation and the output gap in the presence of financial frictions as compared to earlier generations of such models. Leaning-against-the-wind policies in models of this class estimated for the Euro Area do not lead to substantial gains. With regard to forecasting performance, the inclusion of financial frictions can generate improvements, if conditioned on appropriate data. Looking forward, we argue that model-averaging and embracing alternative modelling paradigms is likely to yield a more robust framework for the conduct of monetary policy.
Financial market interactions can lead to large and persistent booms and recessions. Instability is an inherent threat to economies with speculative financial markets. A central bank’s interest rate setting can amplify the expectation feedback in the financial market and this can lead to unstable dynamics and excess volatility. The paper suggests that policy institutions may be well-advised to handle tools like asset price targeting with care since such instruments might add a structural link between asset prices and macroeconomic aggregates. Neither stock prices nor indices are a good indicator to base decisions on.