the goldman sachs group , inc . and subsidiaries notes to consolidated financial statements in the tables above : 2030 the gross fair values exclude the effects of both counterparty netting and collateral netting , and therefore are not representative of the firm 2019s exposure . 2030 counterparty netting is reflected in each level to the extent that receivable and payable balances are netted within the same level and is included in counterparty netting in levels . where the counterparty netting is across levels , the netting is included in cross-level counterparty netting . 2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts . significant unobservable inputs the table below presents the amount of level 3 assets ( liabilities ) , and ranges , averages and medians of significant unobservable inputs used to value the firm 2019s level 3 derivatives . level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december $ in millions 2017 2016 .

Table: $ in millions level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2017 level 3 assets ( liabilities ) and range of significant unobservable inputs ( average/median ) as of december 2016 interest rates net $ -410 ( 410 ) $ -381 ( 381 ) correlation ( 10 ) % (  % ) to 95% ( 95 % ) ( 71%/79% ( 71%/79 % ) ) ( 10 ) % (  % ) to 86% ( 86 % ) ( 56%/60% ( 56%/60 % ) ) volatility ( bps ) 31 to 150 ( 84/78 ) 31 to 151 ( 84/57 ) credit net $ 1505 $ 2504 correlation 28% ( 28 % ) to 84% ( 84 % ) ( 61%/60% ( 61%/60 % ) ) 35% ( 35 % ) to 91% ( 91 % ) ( 65%/68% ( 65%/68 % ) ) credit spreads ( bps ) 1 to 633 ( 69/42 ) 1 to 993 ( 122/73 ) upfront credit points 0 to 97 ( 42/38 ) 0 to 100 ( 43/35 ) recovery rates 22% ( 22 % ) to 73% ( 73 % ) ( 68%/73% ( 68%/73 % ) ) 1% ( 1 % ) to 97% ( 97 % ) ( 58%/70% ( 58%/70 % ) ) currencies net $ -181 ( 181 ) $ 3 correlation 49% ( 49 % ) to 72% ( 72 % ) ( 61%/62% ( 61%/62 % ) ) 25% ( 25 % ) to 70% ( 70 % ) ( 50%/55% ( 50%/55 % ) ) commodities net $ 47 $ 73 volatility 9% ( 9 % ) to 79% ( 79 % ) ( 24%/24% ( 24%/24 % ) ) 13% ( 13 % ) to 68% ( 68 % ) ( 33%/33% ( 33%/33 % ) ) natural gas spread $ ( 2.38 ) to $ 3.34 ( $ ( 0.22 ) /$ ( 0.12 ) ) $ ( 1.81 ) to $ 4.33 ( $ ( 0.14 ) /$ ( 0.05 ) ) oil spread $ ( 2.86 ) to $ 23.61 ( $ 6.47/$ 2.35 ) $ ( 19.72 ) to $ 64.92 ( $ 25.30/$ 16.43 ) equities net $ -1249 ( 1249 ) $ -3416 ( 3416 ) correlation ( 36 ) % (  % ) to 94% ( 94 % ) ( 50%/52% ( 50%/52 % ) ) ( 39 ) % (  % ) to 88% ( 88 % ) ( 41%/41% ( 41%/41 % ) ) volatility 4% ( 4 % ) to 72% ( 72 % ) ( 24%/22% ( 24%/22 % ) ) 5% ( 5 % ) to 72% ( 72 % ) ( 24%/23% ( 24%/23 % ) )

in the table above : 2030 derivative assets are shown as positive amounts and derivative liabilities are shown as negative amounts . 2030 ranges represent the significant unobservable inputs that were used in the valuation of each type of derivative . 2030 averages represent the arithmetic average of the inputs and are not weighted by the relative fair value or notional of the respective financial instruments . an average greater than the median indicates that the majority of inputs are below the average . for example , the difference between the average and the median for credit spreads and oil spread inputs indicates that the majority of the inputs fall in the lower end of the range . 2030 the ranges , averages and medians of these inputs are not representative of the appropriate inputs to use when calculating the fair value of any one derivative . for example , the highest correlation for interest rate derivatives is appropriate for valuing a specific interest rate derivative but may not be appropriate for valuing any other interest rate derivative . accordingly , the ranges of inputs do not represent uncertainty in , or possible ranges of , fair value measurements of the firm 2019s level 3 derivatives . 2030 interest rates , currencies and equities derivatives are valued using option pricing models , credit derivatives are valued using option pricing , correlation and discounted cash flow models , and commodities derivatives are valued using option pricing and discounted cash flow models . 2030 the fair value of any one instrument may be determined using multiple valuation techniques . for example , option pricing models and discounted cash flows models are typically used together to determine fair value . therefore , the level 3 balance encompasses both of these techniques . 2030 correlation within currencies and equities includes cross- product type correlation . 2030 natural gas spread represents the spread per million british thermal units of natural gas . 2030 oil spread represents the spread per barrel of oil and refined products . range of significant unobservable inputs the following is information about the ranges of significant unobservable inputs used to value the firm 2019s level 3 derivative instruments : 2030 correlation . ranges for correlation cover a variety of underliers both within one product type ( e.g. , equity index and equity single stock names ) and across product types ( e.g. , correlation of an interest rate and a currency ) , as well as across regions . generally , cross-product type correlation inputs are used to value more complex instruments and are lower than correlation inputs on assets within the same derivative product type . 2030 volatility . ranges for volatility cover numerous underliers across a variety of markets , maturities and strike prices . for example , volatility of equity indices is generally lower than volatility of single stocks . 2030 credit spreads , upfront credit points and recovery rates . the ranges for credit spreads , upfront credit points and recovery rates cover a variety of underliers ( index and single names ) , regions , sectors , maturities and credit qualities ( high-yield and investment-grade ) . the broad range of this population gives rise to the width of the ranges of significant unobservable inputs . 130 goldman sachs 2017 form 10-k .

Question: what is the percentage change in credit net from 2016 to 2017?
Answer: -39.9%
Calculation: divide(subtract(1505, 2504), 2504)