The notion that the current market price of a physical commodity (its cash price or currency) will be equal to its anticipated future price based on the market's forward rate. Like anything that relies on interest rate projections, this outlook can change as economic conditions change.

In statistical terms, "bias" is generally considered to be the variance between a prediction and the actual outcome, so an unbiased predictor is one that, one average, closely forecasts the future behavior of the variable under consideration. For example, if a futures contract is considered an unbiased predictor of oil prices, then when the contract expires the price of oil should correspond with the anticipated price.

What is an Unbiased Estimator? An unbiased estimator is a measure for approximating a population parameter. In other words, if the estimator (i.e. the sample mean) equals the parameter (i.e. the population mean), it means it's an unbiased estimator.

Z,A parameter's expected value in an unbiased estimator is equal to its true value. An unbiased estimator estimates parameter that are approximately correct.

The sample mean is a random variable that estimates the population mean. The expected value of the sample mean and the population mean µ are equal. Therefore, the sample mean is an unbiased estimator of the population mean.

The theory of unbiased estimation is very important to the theory of point estimation, since in reality, it is important that the unbiased estimator have no systematical errors.

A currency with lower interest rates will trade at a forward premium compared to a currency with a higher interest rate. For example, the U.S. dollar trades at a forward premium against the Canadian dollar while the Canadian dollar trades at a forward discount against the U.S. dollar.

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An unbiased estimator is an estimator that has a zero bias.

An unbiased predictor is a forecast that closely predicts the future behavior of a variable.

Some examples include using the average instead of the median, or using a sample with too few observations to represent a population.

Bias means difference between expected value and true value of parameter being estimated.

Futures contracts expire at some point in time in the future.

Futures contracts predict oil prices.

Bias is considered to be the variance between a prediction and the actual outcome.

Yes, there are advantages to using biased estimators over unbiased ones. For example, if you have only one observation from your data set, then it would be impossible for you to use an unbiased estimate because you do not have enough information about your data set. In this case, it would be better for you to use a biased estimate so that you can make some sort of prediction about what your data set might look like in the future based on what little information that you do have.

If it corresponds with the anticipated price when the contract expires, then it would be considered an unbiased predictor.