pre_text: 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 .
post_text: 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 .
table: Level 3 Assets (Liabilities) and Range of Significant Unobservable Inputs (Average/Median) as of December
table: <i>$ in millions</i> 2017 2016
table: Interest rates, net $(410) $(381)
table: Correlation (10)% to 95% (71%/79%) (10)% to 86% (56%/60%)
table: Volatility (bps) 31 to 150 (84/78) 31 to 151 (84/57)
table: Credit, net $1,505 $2,504
table: Correlation 28% to 84% (61%/60%) 35% to 91% (65%/68%)
table: Credit spreads (bps) 1 to 633 (69/42) 1 to 993 (122/73)
table: Upfront credit points 0 to 97 (42/38) 0 to 100 (43/35)
table: Recovery rates 22% to 73% (68%/73%) 1% to 97% (58%/70%)
table: Currencies, net $(181) $3
table: Correlation 49% to 72% (61%/62%) 25% to 70% (50%/55%)
table: Commodities, net $47 $73
table: Volatility 9% to 79% (24%/24%) 13% to 68% (33%/33%)
table: Natural gas spread $(2.38) to $3.34 ($(0.22)/$(0.12)) $(1.81) to $4.33 ($(0.14)/$(0.05))
table: Oil spread $(2.86) to $23.61 ($6.47/$2.35) $(19.72) to $64.92 ($25.30/$16.43)
table: Equities, net $(1,249) $(3,416)
table: Correlation (36)% to 94% (50%/52%) (39)% to 88% (41%/41%)
table: Volatility 4% to 72% (24%/22%) 5% to 72% (24%/23%)
question: Analyse this data from a financial earnings document. what is the percentage change in credit net from 2016 to 2017?