Task: Table-QA (source dataset: FinQA)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| year of first payment eligibility   | percent ofliving benefitaccount values   |
|:------------------------------------|:-----------------------------------------|
| 2010 and prior                      | 1% ( 1 % )                               |
| 2011                                | 0% ( 0 % )                               |
| 2012                                | 7% ( 7 % )                               |
| 2013                                | 24% ( 24 % )                             |
| 2014                                | 19% ( 19 % )                             |
| 2015                                | 5% ( 5 % )                               |
| 2016                                | 6% ( 6 % )                               |
| 2017                                | 18% ( 18 % )                             |
| 2018 and after                      | 20% ( 20 % )                             |
| total                               | 100% ( 100 % )                           |

Question:
what is the change in fair value of financial market instruments as part of the hedging strategy during 2010?


pre_text: years .
the company does not yet have a robust set of annuitization experience because most of its clients 2019 policyholders are not yet eligible to annuitize utilizing the gmib .
however , for certain clients there are several years of annuitization experience 2013 for those clients the annuitization function reflects the actual experience and has a maximum annuitization rate per annum of 8 percent ( a higher maximum applies in the first year a policy is eligible to annuitize utilizing the gmib 2013 it is over 13 percent ) .
for most clients there is no currently observable relevant annuitization behavior data and so we use a weighted aver- age ( with a heavier weighting on the observed experience noted previously ) of three different annuitization functions with maximum annuitization rates per annum of 8 percent , 12 percent , and 30 percent , respectively ( with significantly higher rates in the first year a policy is eligible to annuitize utilizing the gmib ) .
as noted elsewhere , our gmib reinsurance treaties include claim limits to protect ace in the event that actual annuitization behavior is significantly higher than expected .
during 2010 , the company made various changes to assumptions ( primarily annuitization and lapse ) and methods used to calculate the fair value .
the changes had a net effect of reducing fair value of the liability by $ 98 million ( where the dollar impact of each change was measured in the quarter in which the change was implemented ) .
during 2010 , we recorded realized losses of $ 64 million primarily due to increasing net fair value of reported glb reinsurance liabilities resulting substantially from the impact of falling interest rates .
this excludes realized losses of $ 150 mil- lion during 2010 on derivative hedge instruments held to partially offset the risk in the va guarantee reinsurance portfolio .
these derivatives do not receive hedge accounting treatment .
refer to 201cnet realized gains ( losses ) 201d for a breakdown of the realized gains on glb reinsurance and the realized losses on the derivatives for 2010 and 2009 .
ace tempest life re employs a strategy to manage the financial market and policyholder behavior risks embedded in the reinsurance of va guarantees .
risk management begins with underwriting a prospective client and guarantee design , with particular focus on protecting ace 2019s position from policyholder options that , because of anti-selective behavior , could adversely impact our obligation .
a second layer of risk management is the structure of the reinsurance contracts .
all va guarantee reinsurance contracts include some form of annual or aggregate claim limit ( s ) .
the exact limits vary by contract , but some examples of typical con- tract provisions include : 2022 annual claim limits , as a percentage of reinsured account or guaranteed value , for gmdbs and gmibs ; 2022 annual annuitization rate limits , as a percentage of annuitization eligible account or guaranteed value , for gmibs ; and 2022 per policy claim limits , as a percentage of guaranteed value , for gmabs .
a third layer of risk management is the hedging strategy which is focused on mitigating long-term economic losses at a portfolio level .
ace tempest life re owned financial market instruments as part of the hedging strategy with a fair value of $ 21 million and $ 47 million at december 31 , 2010 , and 2009 , respectively .
the instruments are substantially collateralized by our counterparties , on a daily basis .
we also limit the aggregate amount of variable annuity reinsurance guarantee risk we are willing to assume .
the last substantive u.s .
transaction was quoted in mid-2007 and the last transaction in japan was quoted in late 2007 .
the aggregate number of policyholders is currently decreasing through policyholder withdrawals and deaths at a rate of 5-10 per- cent annually .
note that glb claims cannot occur for any reinsured policy until it has reached the end of its 201cwaiting period 201d .
the vast majority of policies we reinsure reach the end of their 201cwaiting periods 201d in 2013 or later , as shown in the table below .
year of first payment eligibility percent of living benefit account values .
post_text: .
label: -26
qa: answer: -26
ann_text_rows: 17
steps: op: minus1-1
arg1: 21
arg2: 47
res: -26
program: subtract(21, 47)
gold_inds: text_17: ace tempest life re owned financial market instruments as part of the hedging strategy with a fair value of $ 21 million and $ 47 million at december 31 , 2010 , and 2009 , respectively .
exe_ans: -26
tfidftopn: text_6: the changes had a net effect of reducing fair value of the liability by $ 98 million ( where the dollar impact of each change was measured in the quarter in which the change was implemented ) .
text_11: ace tempest life re employs a strategy to manage the financial market and policyholder behavior risks embedded in the reinsurance of va guarantees .
program_re: subtract(21, 47)
model_input: text_17
ace tempest life re owned financial market instruments as part of the hedging strategy with a fair value of $ 21 million and $ 47 million at december 31 , 2010 , and 2009 , respectively .
table_1
year of first payment eligibility the 2010 and prior of percent ofliving benefitaccount values is 1% ( 1 % ) ;
table_10
year of first payment eligibility the total of percent ofliving benefitaccount values is 100% ( 100 % ) ;