MANAGERIAL HEDGING AND PORTFOLIO MONITORING

Incentive compensation induces correlation between the portfolio of managers and the cash flow of the firms they manage. This correlation exposes managers to risk and hence gives them an incentive to hedge against the poor performance of their firms. We study the agency problem between shareholders and a manager when the manager can hedge his compensation using financial markets and shareholders can monitor the manager's portfolio in order to keep him from hedging, but monitoring is costly. We find that the optimal incentive compensation and governance provisions have the following properties: (i) the manager's portfolio is monitored only when the firm performs poorly, (ii) the manager's compensation is more sensitive to firm performance when the cost of monitoring is higher or when hedging markets are more developed, and (iii) conditional on the firm's performance, the manager's compensation is lower when his portfolio is monitored, even if no hedging is revealed by monitoring. Moreover, the model suggests that the optimal level of portfolio monitoring is higher for managers of firms whose performance canbehedged more easily, such as larger firms and firms in more developed financial markets. (JEL: G30, D82)     

Over-relaxation methods and coupled Markov chains for Monte Carlo simulation

This paper is concerned with improving the performance of certain Markov chain algorithms for Monte Carlo simulation. We propose a new algorithm for simulating from multivariate Gaussian densities. This algorithm combines ideas from coupled Markov chain methods and from an existing algorithm based only on over-relaxation. The rate of convergence of the proposed and existing algorithms can be measured in terms of the square of the spectral radius of certain matrices. We present examples in which the proposed algorithm converges faster than the existing algorithm and the Gibbs sampler. We also derive an expression for the asymptotic variance of any linear combination of the variables simulated by the proposed algorithm. We outline how the proposed algorithm can be extended to non-Gaussian densities.     

The 2007 financial crisis: strategic actors and processes of construction of a concrete system

The recent financial and economic crisis, defined “a once in a century credit tsunami” by former President of Federal Reserve, Alan Greenspan, has produced relevant damages in all economic sectors, making many people much poorer. For this reason, many scientific contributions have addressed the causes of the crisis, focusing mostly on the ‘bad practices’ in lending and credit securitization procedures as well as in corporate governance mechanisms ruling the banking system. Our work is based on an organizational perspective and it reviews the crisis under a theoretical model that combines the political and new institutionalist studies, in order to show evidence of the intense network of relationships and interests underlying the financial system government. In doing so, we identify the key players acting as institutional entrepreneurs that, levering on their resources and power, have contributed to construct and reshape the institutional framework—normative and symbolic—ruling the so called Great Moderation period.     

MARKET POWER AND INFORMATION REVELATION IN DYNAMIC TRADING

We study a strategic model of dynamic trading where agents are asymmetrically informed over common value sources of uncertainty. There is a continuum of buyers and a finite number n of sellers. All buyers are uninformed, while at least one seller is privately informed about the true state of the world. When n = 1, full information revelation never occurs in equilibrium and the only information transmission happens in the first period. With n > 1 the outcome depends both on the structure of the sellers' information and, even more importantly, on the intensity of competition allowed by the existing trading rules. When there is intense competition (absence of clienteles), information is fully and immediately revealed to the buyers in every equilibrium for n large enough, regardless of the number of informed sellers. On the other hand, for trading arrangements characterized by less intense forms of competition (presence of clienteles), for any n we always have equilibria where information is never fully revealed. Moreover, in that case, when only one seller is informed, for many parameter configurations there are no equilibria with full information revelation, even for large n. (JEL: C72, C78, D82, D83)     

The new strategy for the concise presentation of sampling errors in the Italian Structural Business Statistics Survey

Reporting sampling errors of survey estimates is a problem that is commonly addressed when compiling a survey report. Because of the vast number of study variables or population characteristics and of interest domains in a survey, it is almost impossible to calculate and to publish the standard errors for each statistic. A way of overcoming such problem would be to estimate indirectly the sampling errors by using generalized variance functions, which define a statistical relationship between the sampling errors and the corresponding estimates. One of the problems with this approach is that the model specification has to be consistent with a roughly constant design effect. If the design effects vary greatly across estimates, as in the case of the Business Surveys, the prediction model is not correctly specified and the least-square estimation is biased. In this paper, we show an extension of the generalized variance functions, which address the above problems, which could be used in contexts similar to those encountered in Business Surveys. The proposed method has been applied to the Italian Structural Business Statistics Survey case.     

The uniformly minimum variance unbiased estimator of odds ratio in case–control studies under inverse sampling

The stated goal of this paper is to propose the uniformly minimum variance unbiased estimator of odds ratio in case–control studies under inverse sampling design. The problem of estimating odds ratio plays a central role in case–control studies. However, the traditional sampling schemes appear inadequate when the expected frequencies of not exposed cases and exposed controls can be very low. In such a case, it is convenient to use the inverse sampling design, which requires that random drawings shall be continued until a given number of relevant events has emerged. In this paper we prove that a uniformly minimum variance unbiased estimator of odds ratio does not exist under usual binomial sampling, while the standard odds ratio estimator is uniformly minimum variance unbiased under inverse sampling. In addition, we compare these two sampling schemes by means of large-sample theory and small-sample simulation.     

Dynamic Conditional Correlation: On Properties and Estimation

This article addresses some of the issues that arise with the Dynamic Conditional Correlation (DCC) model. It is proven that the DCC large system estimator can be inconsistent, and that the traditional interpretation of the DCC correlation parameters can result in misleading conclusions. Here, we suggest a more tractable DCC model, called the cDCC model. The cDCC model allows for a large system estimator that is heuristically proven to be consistent. Sufficient stationarity conditions for cDCC processes of interest are established. The empirical performances of the DCC and cDCC large system estimators are compared via simulations and applications to real data.