What's good for GM...? Using auto industry stock returns to forecast business cycles and test the Q-theory of investment

Business cycles ; General Motors Corporation ; Investments

Asymmetric cross-sectional dispersion in stock returns: evidence and implications

This paper documents that daily stock returns of both firms and industries are more dispersed when the overall stock market rises than when it falls. This positive relation is conceptually distinct from - and appears unrelated to - asymmetric return correlations. I argue that the source of the relation is positive skewness in sector-specific return shocks. I use this asymmetric behavior to explain a previously-observed puzzle: aggregate trading volume tends to be higher on days when the stock market rises than when it falls. The idea proposed here is that trading is more active on days when the market rises because on those days there is more non-market news on which to trade. I find that empirically, the bulk of the relation between volume and the signed market return is explained by variations in non-market volatility.Stock market ; Econometric models

Term premia and interest rate forecasts in affine models

I find that the standard class of affine models produces poor forecasts of future changes in Treasury yields. Better forecasts are generated by assuming that yields follow random walks. The failure of these models is driven by one of their key features: the compensation that investors receive for facing risk is a multiple of the variance of the risk. This means that risk compensation cannot vary independently of interest rate volatility. I also describe and empirically estimate a class of models that is broader than the standard affine class. These 'essentially affine' models retain the tractability of the usual models, but allow the compensation for interest rate risk to vary independently of interest rate volatility. This additional flexibility proves useful in forming accurate forecasts of future yields.Government securities ; Econometric models ; Forecasting

Credit Derivatives in Banking: Useful Tools for Managing Risk?

We model the effects on banks of the introduction of a market for credit derivatives; in particular, credit-default swaps. A bank can use such swaps to temporarily transfer credit risks of their loans to others, reducing the likelihood that defaulting loans trigger the bank's financial distress. Because credit derivatives are more flexible at transferring risks than are other, more established tools such as loan sales without recourse, these instruments make it easier for banks to circumvent the "lemons" problem caused by banks' superior information about the credit quality of their loans. However, we find that the introduction of a credit-derivatives market is not necessarily desirable because it can cause other markets for loan risk-sharing to break down.

Testing for herding in the Athens Stock Exchange during the crisis period

This paper investigates herding behavior in the Athens Stock Exchange focusing on the recent crisis period. We employ a survivor bias free dataset of all listed stocks from 2007 to May 2015. We apply the cross sectional dispersion approach and provide results that extend and are comparable with previous studies regarding the Greek stock market. The empirical results indicate the presence of herding under different market states. Employing the quantile regression method, there is herding in the high quantiles of the cross sectional return dispersion. Finally, we document the impact of size effect on herding estimations