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The Sun Insurance Office, 1710-1960.
Forward Foreign Exchange Rates, Expected Spot Rates, and Premia: A Signal- Extraction Approach
In this paper, we implement a methodology to identify and measure premia in the pricing of forward foreign exchange. The methodology involves application of signal-extraction techniques from the engineering literature. Diagnostic tests indicate that these methods are quite successful in capturing the essence of the time-series properties of premium terms. The estimated premium models indicate that premia show a certain degree of persistence over time and that more than half the variance in the forecast error that results from the use of current forward rates as predictors of future spot rates is accounted for by variation in premium terms. The methodology can be applied straightforwardly to the measurement of unobservables in other financial markets.
Forward Foreign Exchange Rates, Expected Spot Rates, and Premia: A Signal‐Extraction Approach
ABSTRACT In this paper, we implement a methodology to identify and measure premia in the pricing of forward foreign exchange that involves application of signal‐extraction techniques from the engineering literature. Diagnostic tests indicate that these methods are quite successful in capturing the essence of the time‐series properties of premium terms. The estimated premium models indicate that premia show a certain degree of persistance over time and that more than half the variance in the forecast error that results from the use of current forward rates as predictors of future spot rates is accounted for by variation in premium terms. The methodology can be applied straightforwardly to the measurement of unobservables in other financial markets.
Nonstandard Errors
ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.