This paper provides some evidence for the volatile, persistent and countercyclical Sharpe ratio. In the data, conditional Sharpe ratio volatility is almost 72 percent annualized with a first order autocorrelation of 0.85. The difference between trough and peak of the conditional Sharpe ratio is more than 1 annualized. I show that a model with time-varying productivity risk is more promising in matching the conditional moments of asset returns compared to a standard real business cycle model. A model with time-invariant productivity risk generates Sharpe ratio volatility that is one order of magnitude smaller than the model with time-varying productivity volatility. The time varying model also produces a positive and relatively high first order autocorrelation of the same time series, whereas the former can only generates a small and negative autocorrelation, and hence at odd with the data.
A laser tweezers system for transporting dissociated neurons into small “cages” on a culture dish was constructed, and it was studied extensively. The system consists of an inverted microscope, a 1064 nm or 980 nm laser module, a beam expander, a motorized mechanical stage, a CCD camera, and steering mirrors. A laser beam is generated by the IR laser module, and the beam is expanded by the beam expander to match the size of the back aperture of the objective. The beam is then steered into the objective where it is focused to a point. The system uses this single, tightly focused laser beam to trap a neuron. Once a neuron is trapped and lifted, the mechanical stage is moved to locate the neuron above its destination. The system will know the location of the neurocages and will automatically move neurons to their destination.
Two main competing approaches of the business cycle arose in the eighties: the real Business Cycles theory and the New-Keynesian Macroeconomics. Following the seminal papers of Kydland and Prescott [1982], Long and Plosser [1983] and King, Plosser, and Rebelo [1988], RBC theorists consider economic fluctuations as the optimal responses of economic agents to exogeneous real shocks. The aim of these first models was to explain macroeconomic fluctuations only with technological shocks; demand shocks were unnecessary and nominal shocks were de facto absent from these purely real models. Hénin [1989] noticed some attempts to introduce money in such a flex-price competitive framework: the King and Plosser [1984] model introduces financial intermediation and get some insight on the (inside) money-output correlation when technology shocks occur. Nevertheless, the outside money-output link is missing and no quantitative validation of the model is proposed. Outside money shocks are introduced in a cash-in-advance economy by Cooley and Hansen [1989] (constraint on consumption) and Hairault and Portier [1991a] (constraint on consumption and investment) or in a model with money in the utility function by Hairault and Portier [1991b]. These models do not provide a good description of the money output correlation and are not able to reproduce the shape and level of the real variables impulse responses to monetary shocks for a realistic calibration. King [1990] obtains the triangular shape of the monetary business cycle in a staggered contracts model where money is introduced via a quantitative equation. Nevertheless, this quantitative equation has no microeconomic foundations in the model and no simulated moments are computed. Ambler and Phaneuf [1991] also use staggered contracts in a reduced- form model which provides good approximation of monetary impulse responses, but without explicit microfoundations.
Furthermore, the RBC literature initiated a validation method based upon the ability of the model to mimic some macroeconomic stylized facts by stochastic simulations. On the other hand, the New-Keynesian macroeconomy expanded microfounded macroeconomic models with Keynesian features such as underemployment equilibria, coordination failures, market power, real and nominal rigidities and the importance of nominal shocks in the business cycle.
