Financial report context:
the goldman sachs group , inc . and subsidiaries management 2019s discussion and analysis the risk committee of the board and the risk governance committee ( through delegated authority from the firmwide risk committee ) approve market risk limits and sub-limits at firmwide , business and product levels , consistent with our risk appetite statement . in addition , market risk management ( through delegated authority from the risk governance committee ) sets market risk limits and sub-limits at certain product and desk levels . the purpose of the firmwide limits is to assist senior management in controlling our overall risk profile . sub-limits are set below the approved level of risk limits . sub-limits set the desired maximum amount of exposure that may be managed by any particular business on a day-to-day basis without additional levels of senior management approval , effectively leaving day-to-day decisions to individual desk managers and traders . accordingly , sub-limits are a management tool designed to ensure appropriate escalation rather than to establish maximum risk tolerance . sub-limits also distribute risk among various businesses in a manner that is consistent with their level of activity and client demand , taking into account the relative performance of each area . our market risk limits are monitored daily by market risk management , which is responsible for identifying and escalating , on a timely basis , instances where limits have been exceeded . when a risk limit has been exceeded ( e.g. , due to positional changes or changes in market conditions , such as increased volatilities or changes in correlations ) , it is escalated to senior managers in market risk management and/or the appropriate risk committee . such instances are remediated by an inventory reduction and/or a temporary or permanent increase to the risk limit . model review and validation our var and stress testing models are regularly reviewed by market risk management and enhanced in order to incorporate changes in the composition of positions included in our market risk measures , as well as variations in market conditions . prior to implementing significant changes to our assumptions and/or models , model risk management performs model validations . significant changes to our var and stress testing models are reviewed with our chief risk officer and chief financial officer , and approved by the firmwide risk committee . see 201cmodel risk management 201d for further information about the review and validation of these models . systems we have made a significant investment in technology to monitor market risk including : 2030 an independent calculation of var and stress measures ; 2030 risk measures calculated at individual position levels ; 2030 attribution of risk measures to individual risk factors of each position ; 2030 the ability to report many different views of the risk measures ( e.g. , by desk , business , product type or entity ) ; 2030 the ability to produce ad hoc analyses in a timely manner . metrics we analyze var at the firmwide level and a variety of more detailed levels , including by risk category , business , and region . the tables below present average daily var and period-end var , as well as the high and low var for the period . diversification effect in the tables below represents the difference between total var and the sum of the vars for the four risk categories . this effect arises because the four market risk categories are not perfectly correlated . the table below presents average daily var by risk category. .

Financial table:
$ in millions: interest rates; year ended december 2017: $ 40; year ended december 2016: $ 45; year ended december 2015: $ 47
$ in millions: equity prices; year ended december 2017: 24; year ended december 2016: 25; year ended december 2015: 26
$ in millions: currency rates; year ended december 2017: 12; year ended december 2016: 21; year ended december 2015: 30
$ in millions: commodity prices; year ended december 2017: 13; year ended december 2016: 17; year ended december 2015: 20
$ in millions: diversification effect; year ended december 2017: -35 ( 35 ); year ended december 2016: -45 ( 45 ); year ended december 2015: -47 ( 47 )
$ in millions: total; year ended december 2017: $ 54; year ended december 2016: $ 63; year ended december 2015: $ 76

Additional report context:
our average daily var decreased to $ 54 million in 2017 from $ 63 million in 2016 , due to reductions across all risk categories , partially offset by a decrease in the diversification effect . the overall decrease was primarily due to lower levels of volatility . our average daily var decreased to $ 63 million in 2016 from $ 76 million in 2015 , due to reductions across all risk categories , partially offset by a decrease in the diversification effect . the overall decrease was primarily due to reduced exposures . goldman sachs 2017 form 10-k 91 .

Question: what was the percentage change in average daily var in the interest rates risk category between 2016 and 2017?
Answer: -11%