Bootstrapping


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Bootstrapping

Creating a theoretical spot rate curve using one yield projection as the basis for the yield of the next maturity. Bootstrapping follows the work of Efron. It involves a Monte Carlo approach.

Bootstrap

1. To start a company with personal finances rather than through loans or venture capital. This is obviously a large risk to the entrepreneur as he/she has no recourse should the business fail. On the other hand, it allows the entrepreneur to maintain control of the business and has the potential to be very successful. It is famously said that Ross Perot established Electronic Data Systems with $1,000 in personal savings; he maintained complete control of the company until its IPO six years later. This is an example of bootstrapping. See also: Seed money.

2. To calculate the yield curve on a zero-coupon Treasury bill. Because the U.S. Treasury does not issue new T-bills constantly, bootstrapping is used to create a yield curve by filling in the missing yields on the T-bills.
References in periodicals archive ?
The concept of financial bootstrapping for small businesses originated from Bhide (1992) and it has received increased academic attention, particularly in developed economies (Winborg, 2015).
The first three columns are the results without weighting (i), the second three columns are the results with population weights only (ii), and the last three columns are the results with both population weights and bootstrapping (iii).
In addition to examining bootstrapping behavior, REML estimation, as implemented in PROC MIXED, was also conducted for the purpose of comparison.
Bootstrapping has particular current salience in the context of the Patient Protection and Affordable Care Act (ACA) of 2010.
Bootstrapping is a lot easier with Excel since the advent of xlsx files, because each bootstrap sample occupies several columns, and xls files were limited to 256 columns.
Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution.
Bootstrapping provides an alternative strategy that can realistically inform the practitioner by a more accurate assessment of the variability inherent in a system or process.
Respondents with college-level education used self-funded bootstrapping methods more frequently (5% level of significance) than business owners who did not attend college.
In other words, dialectical bootstrapping enables different opinions to be created and combined in the same mind.
Existing scholarly research on bootstrapping is limited, despite the widespread use of bootstrapping strategies in actual practice among start-up entrepreneurs.
1994, A Practical Guide to Measuring Reserve Variability Using: Bootstrapping, Operational Time and a Distribution Free Approach, Presented at the 1994 General Insurance Convention, Institute of Actuaries and Faculty of Actuaries.
The effect of bootstrapping was studied by examining whether major profile patterns were replicated when sample sizes were reduced.