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This paper studies a consumption-portfolio problem where money enters the agent's utility function. We solve the corresponding Hamilton-Jacobi-Bellman equation and provide closed-form solutions for the optimal consumption and portfolio strategy both in an infinite- and finite-horizon setting. For the infinite-horizon problem, the optimal stock demand is one particular root of a polynomial. In the finite-horizon case, the optimal stock demand is given by the inverse of the solution to an ordinary differential equation that can be solved explicitly. We also prove verification results showing that the solution to the Bellman equation is indeed the value function of the problem. From an economic point of view, we find that in the finite-horizon case the optimal stock demand is typically decreasing in age, which is in line with rules of thumb given by financial advisers and also with recent empirical evidence.
This paper analyzes the bail-in tool under the Bank Recovery and Resolution Directive (BRRD) and predicts that it will not reach its policy objective. To make this argument, this paper first describes the policy rationale that calls for mandatory private sector involvement (PSI). From this analysis, the key features for an effective bail-in tool can be derived.
These insights serve as the background to make the case that the European resolution framework is likely ineffective in establishing adequate market discipline through risk-reflecting prices for bank capital. The main reason for this lies in the avoidable embeddedness of the BRRD’s bail-in tool in the much broader resolution process, which entails ample discretion of the authorities also in forcing private sector involvement. Moreover, the idea that nearly all positions on the liability side of a bank’s balance sheet should be subjected to bail-in is misguided. Instead, a concentration of PSI in instruments that fall under the minimum requirements for own funds and eligible liabilities (MREL) is preferable.
Finally, this paper synthesized the prior analysis by putting forward an alternative regulatory approach that seeks to disentangle private sector involvement as a precondition for effective bank-resolution as much as possible form the resolution process as such.
Since 2014 the ECB has implemented a massive expansion of monetary policy including large-scale asset purchases and negative policy rates. As the euro area economy has improved and inflation has risen, questions concerning the future normalization of monetary policy are starting to dominate the public debate.
The study argues that the ECB should develop a strategy for policy normalization and communicate it very soon to prepare the ground for subsequent steps towards tightening. It provides analysis and makes proposals concerning key aspects of this strategy. The aim is to facilitate the emergence of expectations among market participants that are consistent with a smooth process of policy normalization.
We analyze the market reaction to the sentiment of the CEO speech at the Annual General Meeting (AGM). As the AGM is typically preceded by several information disclosures, the CEO speech may be expected to contribute only marginally to investors’ decision-making. Surprisingly, however, we observe from the transcripts of 338 CEO speeches of German corporates between 2008 and 2016 that their sentiment is significantly related to abnormal stock returns and trading volumes following the AGM. Using a novel business-specific German dictionary based on Loughran and McDonald (2011), we find a negative association of the post-AGM returns with the speeches’ negativity and a positive association with the speeches’ relative positivity (i.e. positivity relative to negativity). Relative positivity moreover corresponds with a lower trading volume in a short time window surrounding the AGM. Investors hence seem to perceive the sentiment of CEO speeches at AGMs as a valuable indicator of future firm performance.
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.
A counterparty credit limit (CCL) is a limit imposed by a financial institution to cap its maximum possible exposure to a specified counterparty. Although CCLs are designed to help institutions mitigate counterparty risk by selective diversification of their exposures, their implementation restricts the liquidity that institutions can access in an otherwise centralized pool. We address the question of how this mechanism impacts trade prices and volatility, both empirically and via a new model of trading with CCLs. We find empirically that CCLs cause little impact on trade. However, our model highlights that in extreme situations, CCLs could serve to destabilize prices and thereby influence systemic risk.
We show an ambivalent role of high-frequency traders (HFTs) in the Eurex Bund Futures market around high-impact macroeconomic announcements and extreme events. Around macroeconomic announcements, HFTs serve as market makers, post competitive spreads, and earn most of their profits through liquidity supply. Right before the announcement, however, HFTs significantly widen spreads and cause a rapid but short-lived drying-out of liquidity. In turbulent periods, such as after the U.K. Brexit announcement, HFTs shift their focus from market making activities to aggressive (but not necessarily profitable) directional strategies. Then, HFT activity becomes dominant and market quality can degrade.
Optimal trend inflation
(2017)
We present a sticky-price model incorporating heterogeneous Firms and systematic firm-level productivity trends. Aggregating the model in closed form, we show that it delivers radically different predictions for the optimal inflation rate than canonical sticky price models featuring homogenous Firms:
(1) the optimal steady-state inflation rate generically differs from zero and,
(2) inflation optimally responds to productivity disturbances.
Using micro data from the US Census Bureau to estimate the inflation-relevant productivity trends at the firm level, we find that the optimal US inflation rate is positive. It was slightly above 2 percent in the year 1986, but continuously declined thereafter, reaching about 1 percent in the year 2013.
Monetary policy communication is particularly important during unconventional times, because high uncertainty about the economy, the introduction of new policy tools and possible limits to the central bank’s toolkit could hamper the predictability of policy actions. We study how monetary policy communication should and has worked under such circumstances. Our main results relate to announcements of asset purchase programmes and the use of forward guidance. We show that announcements of asset purchase programmes have lowered market uncertainty, particularly when accompanied by a contextual release of implementation details such as the envisaged size of the programme. We also show that forward guidance reduces uncertainty more effectively when it is state‐contingent or when it provides guidance about a long horizon than when it is open‐ended or covers only a short horizon, and that the credibility of forward guidance is strengthened if the central bank also has embarked on an asset purchase programme.
Recent work has analyzed the forecasting performance of standard dynamic stochastic general equilibrium (DSGE) models, but little attention has been given to DSGE models that incorporate nonlinearities in exogenous driving processes. Against that background, we explore whether incorporating stochastic volatility improves DSGE forecasts (point, interval, and density). We examine real-time forecast accuracy for key macroeconomic variables including output growth, inflation, and the policy rate. We find that incorporating stochastic volatility in DSGE models of macroeconomic fundamentals markedly improves their density forecasts, just as incorporating stochastic volatility in models of financial asset returns improves their density forecasts.