HEC Paris

About: HEC Paris is a education organization based out in Jouy-en-Josas, France. It is known for research contribution in the topics: Investment (macroeconomics) & Market liquidity. The organization has 584 authors who have published 2756 publications receiving 104467 citations. The organization is also known as: Ecole des Hautes Etudes Commerciales & HEC School of Management Paris.

Optimal Patent Protection in a Two-Sector Economy

Abstract: This article addresses the issue of optimal patent protection in an economy with a downstream and an upstream sector. The key insight is that higher patent protection in the downstream sector raises the incentives of agents to do R&D in that sector but discourages innovation in the upstream sector because of a market size effect. Hence, higher patent protection in the upstream sector accelerates growth whereas higher patent protection in the downstream sector slows it down. If some innovation is socially desirable, optimal patent protection is necessarily higher in the upstream than in the downstream sector.

News Trading and Speed

Abstract: We compare the optimal trading strategy of an informed speculator when he can trade ahead of incoming news (is "fast"), versus when he cannot (is "slow"). We find that speed matters: the fast speculator's trades account for a larger fraction of trading volume, and are more correlated with short-run price changes. Nevertheless, he realizes a large fraction of his profits from trading on long-term price changes. The fast speculator's behavior matches evidence about high frequency traders. We predict that stocks with more informative news are more liquid even though they attract more activity from informed high frequency traders.

The Sense and Non-Sense of Holdout Sample Validation in the Presence of Endogeneity

Abstract: Market response models that use field-generated data are required to address potential endogeneity in the regressors to obtain consistent parameter estimates. Another requirement is that market response models predict well in a holdout sample. Combining both requirements, it may seem reasonable to subject an endogeneity-corrected model to a holdout prediction task, and this is quite common in the academic marketing literature. One may be inclined to expect that the consistent parameter estimates obtained via instrumental variable (IV) estimation predict better than the biased ordinary least squares (OLS) estimates. This paper shows that this expectation is incorrect. That is, if the holdout sample is similar to the estimation sample so that the regressors are endogenous in both samples, holdout sample validation favors regression estimates that are not corrected for endogeneity (i.e., OLS) over estimates that are corrected for endogeneity (i.e., IV estimation). A key take-away is that if consistent parameter estimates are the primary model objective, the model should be validated with an exogenous (rather than endogenous) holdout sample. If prediction is the primary model objective, we recommend refraining from correcting for endogeneity with IV estimation.

The pricing of forward-starting asian options

Abstract: Asian options are path-dependent options whose payoff is based on an average. In some cases, the underlying asset of the option is an average; in others, the strike price itself is computed as an average of the underlying asset recent prices. Asian currency options, as an example, allow corporate users to cover seasonal and so-called strategic foreign exchange positions. Averages are also found in some recent warrant issues, where they are mostly used as poison pills to preclude hostile takeovers. This paper derives a closed-form solution for the valuation of European asian options whose strike price is an average. Both “plain vanilla” average options—i.e. those for which the time interval taken into account for the strike average calculation is the life of the option—and forward-starting average options are considered. The valuation formula is obtained by relying upon a slight linear approximation. Although some previous contributions in the literature already use approximation techniques, our approach contrary to others allows the derivation of a formal upper bound to the approximation error. The numerical values given by this formula are then compared to those generated by an antithetic variate Monte-Carlo method. Based on this comparison and the computation of an upper bound to the approximation error, it is shown that the closed-form solution performs quite well and is obviously computationally efficient.