If you can figure out how to get two pieces of information out of Jira, you can provide very reasonable estimates by utilizing Little's Law. You'll need to know the date you began a task, and the day it was considered done. The more history you have, the more accurate this will be.
Start Date - End Date = Cycle Time
In Excel, to exclude weekends (important later)
The cycle time is the number of days it takes you to complete a single item, from the time you began working on it. Perform this calculation for all of your previously completed tasks, then calculate your average cycle time.
The next step is to figure out how much work you can do in an iteration. We use two week iterations, so I'll use 10 business days as an example.
Tasks per iteration = Avg. Cycle Time / Days in Iteration
I have an average cycle time of 3.5 days.
(1 item / 3.5 days) * ( 10 days / 1 iteration)
10 items / 3.5 iterations = 2.86 items / iteration
So I can accomplish (on average) 2.86 items every 2 weeks.
Now it's trivial to take the number of items in my queue, divide it by the average number of items I can do in an iteration and determine when the last one will be done.
10 items in queue / ( 2.86 items / iteration)
Last item in queue will be done in 3.50 iterations.
But when will my other items be done? Instead of dividing the total number of items in the queue by the average number done, replace it with its priority ranking.
Ex: When will my 5th rank task be done?
5 / 2.86 = 1.75 iterations
Once you've mastered this, you can take your estimations further out by looking at when an item is entered into the backlog and when it is done. This is called Lead Time. The calculations are the same, but provide a longer term view.
Of important note is that none of this relies on how good you are at guessing how long a particular item will take. It relies purely on historical data of how long it actually took you to get things done in the past.