Stefano Giglio

The Performance of Italian Family Firms

In this paper, we study the performance of Italian listed family firms in the period 1998-2003. We measure their performance by using both accounting and market data. We first study the relative performance of family firms compared to widely held firms. Then we investigate whether performance is affected by the type of family firm (i.e., whether the CEO is a member of the family or is an outsider). We find that the data and the methodology used to measure performance strongly affect the results. When performance is measured by accounting data (ROA), using a static model, we find evidence in favour of a superior performance of family firms. Such evidence is not confirmed by the application of the same model to market measures of performance. However, we report statistical evidence that the correct econometric specification for market data is a dynamic model. The results of estimation of the dynamic model for the market measures of performance are more consistent with those based on the static model for the accounting measures of performance.

No-Bubble Condition: Model-Free Tests in Housing Markets

We test for the existence of housing bubbles associated with a failure of the transversality condition that requires the present value of payments occurring infinitely far in the future to be zero. The most prominent such bubble is the classic rational bubble. We study housing markets in the U.K. and Singapore, where residential property ownership takes the form of either leaseholds or freeholds. Leaseholds are finitematurity, pre-paid, and tradable ownership contracts with maturities often exceeding 700 years. Freeholds are infinite-maturity ownership contracts. The price difference between leaseholds with extremely-long maturities and freeholds reflects the present value of a claim to the freehold after leasehold expiry, and is thus a direct empirical measure of the transversality condition. We estimate this price difference, and find no evidence for failures of the transversality condition in housing markets in the U.K. and Singapore, even during periods when a sizeable bubble was regularly thought to be present.

Excess Volatility: Beyond Discount Rates

We document a form of excess volatility that is irreconcilable with standard models of prices, even after accounting for variation in discount rates. We compare prices of claims on the same cash flow stream but with different maturities. Standard models impose precise internal consistency conditions on the joint behavior of long and short maturity claims and these are strongly rejected in the data. In particular, long maturity prices are significantly more variable than justified by the behavior at short maturities. Our findings are pervasive. We reject internal consistency conditions in all term structures that we study, including equity options, currency options, credit default swaps, commodity futures, variance swaps, and inflation swaps.

Taming the Factor Zoo

We propose a model-selection method to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what a high-dimensional set of existing factors explains. Our methodology explicitly accounts for potential model-selection mistakes, unlike the standard approaches that assume perfect variable selection, which rarely occurs in practice and produces a bias due to the omitted variables. We apply our procedure to a set of factors recently discovered in the literature. While most of these new factors are found to be redundant relative to the existing factors, a few — such as profitability — have statistically significant explanatory power beyond the hundreds of factors proposed in the past. In addition, we show that our estimates and their significance are stable, whereas the model selected by simple LASSO is not.

Inference on Risk Premia in the Presence of Omitted Factors

Standard estimators of risk premia in linear asset pricing models are biased if some priced factors are omitted. We propose a three-pass method to estimate the risk premium of an observable factor, which is valid even when not all factors in the model are specified or observed. We show that the risk premium of the observable factor can be identified regardless of the rotation of the other control factors, as long as they together span the true factor space. Motivated by this rotation invariance result, our approach uses principal components of test asset returns to recover the factor space and additional cross-sectional and time-series regressions to obtain the risk premium of the observed factor. Our estimator is also equivalent to the average excess return of a mimicking portfolio maximally correlated with the observed factor using appropriate regularization. Our methodology also accounts for potential measurement error in the observed factor and detects when such a factor is spurious or even useless. The methodology exploits the blessings of dimensionality, and we therefore apply it to a large panel of equity and non-equity portfolios to estimate risk premia for several workhorse factors.