Which is more important: the bond market (inflation) or reshoring industry?

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Luke Groman outlines 2 factions within the US government:

  1. Whatever is good for the bond market should be US Policy:
    • We need to fight inflation.  This is the Larry Summers view that we need high unemployment to fight inflation even if this means a deep recession/recession
  2. We need to reshore, even if this leads to high inflation.
    • A faction within the Pentagon realized the the current dollar-based system requires us to outsource our manufacturing because a strong dollar makes US exports uncompetitive.
    • This outsourcing of manufacturing weakens the military supply-chain and has prompted the military to publish on this subject in 2010 and 2018.
    • The decision to sanction Russia may have been intentionally designed to precipitate a shift away from the dollar’s status as global reserve asset (but not US dollar as global reserve currency)

 
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Threats to the Petrodollar: Ukraine War & BRICS

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“Foreign Affairs” semi-openly admits to “Petrodollar” defense

Foreign-Affairs-The Dollar Still Dominates



 

What is the Petrodollar?

  • The Petrodollar system is based on buyers paying for oil in US dollars. [footnote]The US made an agreement with Saudi Arabia in 1974 that it would require buyers to pay for oil in dollars. This increases demand for dollars, which increases demand for interest-bearing dollars (US Treasuries), which means more people are willing to finance US debt.[/footnote]
  • Petrodollar recycling is when oil producers lend their suplus dollars back the the US, financing the US debt or buying US assets. [footnote]After the US quadrupled its grain export prices shortly after the 1971 gold suspension, the oil-exporting countries quadrupled their oil prices. US diplomats had let Saudi Arabia and other Arab countries know that they could charge as much as they wanted for their oil, but that the US would treat it as an act of war not to keep their oil proceeds in US dollar assets.[/footnote] [footnote]When countries hold dollars, they usually buy US treasury bonds (US debt), so as to get interest on their dollars. The more people that want US dollars, the easier it is for the US to run perpetual deficits and finance that debt. [/footnote]
  • In the Russia/Ukraine war, Russia required “unfriendly” buyers to pay for their energy in rubles, creating a “petroruble“.
  • In a break from the past, Saudi Arabia is now considering selling oil to China in Chinese yuan. [footnote]This decreases demand for US dollars, hence demand for US Treasuries (US debt)[/footnote]

Why this “Foreign Affairs” article is interesting:

I’m not surprised by the content of Carla Norrlöf’s recent “Foreign Affairs” article.  I’ve seen most of this material before from less credentialed sources on the internet (and in books), but I think it is outside of the Overton window for television. If a guest on a network like CNBC started talking about the Petrodollar, I think the producer would cut to commercial break.

  • What I’m surprised by is that an establishment organ like Foreign Affairs would semi-openly admit to some of what critics of the “Petrodollar” say. [footnote]In the past, I’ve seen people deny that it matters whether oil is priced in dollars and whether the dollar is a dominant global reserve currency.  Professor Norrlöf ‘s article confirms that not all the critics are “delusional conspiracy theorists.” [/footnote]
  • Wikipedia describes the author — Carla Norrlöf  — as a Swedish-Ethiopian political scientist. She is Associate Professor of Political Science at the University of Toronto.  Her research focuses on hegemony, as well as the international political economy of trade, investment and security.

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Calculating Interest Rates with Excel

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Here’s an article I wrote for Microfinance Transparency in February 24, 2010:

Chuck Waterfield and Alexandra Fiorillo, MFTransparency’s CEO and VP respectively, have been doing many presentations about how interest rates can be calculated using our excel tool, but we haven’t yet featured a story on our blog about our data collection process and our corresponding excel tool. Although technical, interest rate calculations are really at the heart of MFTransparency’s mission and calculating accurate interest rates is vital to providing transparent pricing data. So today, I would like to give you a brief demonstration of the IRR and XIRR Excel functions, as a way to provide background for the techniques we’ve used to automate interest rate calculations on our website.

For those of you less familiar with excel, this spreadsheet software offers numerous formulas allowing quick and easy calculations within each spreadsheet. As it is particularly geared towards financial use, there are ready-made formulas specifically meant for calculating interest rates. The most basic (but still powerful) calculation is the internal rate of return.

IRR() : Internal Rate of Return

The internal rate of return formula is capable of taking a cash flow and returning the per-period interest rate. It assumes equal lengths of time between each amount in the cash flow. Let’s first start with a sample spreadsheet of loan payments, and use the IRR function to calculate the interest rate.

Screenshot: Excel IRR function

You can see that by applying the IRR formula, we get an “Internal Rate of Return” for the loan. This IRR can then be multiplied by the number of periods in a year to get the APR. Annual Percentage Rate is the standardized format most commonly used in the United States.

  • APR = IRR * n, where n is the number of payments per year.
  • 24.09% = 0.0200757 * 12

The EIR takes into account the effect of compound interest and can be calculated using the formula. This is the standardized interest rate often reported in European countries:

  • EIR = ((1+IRR)^n) -1)
  • 26.94% = ((1+ 0.0200757)^12)-1)

The IRR function is sufficient when there are equal (or near equal) periods between repayments, but what about when repayments occur irregularly? Prior to Excel 2007, there was no easy solution… but thankfully the wizards at microsoft have now provided us with a solution:

XIIR(): accounts for actual payment dates

As mentioned above, the XIRR function is useful for loans with irregular repayment schedules, and is only available in newer versions of Excel (2007) and in recent versions of Open Office.

Excel 2007 XIIR function

Notice that the XIRR function takes into account the payment dates in addition to the payment amounts. It actually provides us with the EIR (so annualized interest rate WITH compounding) for the cash flow in question. We won’t get into the math behind this, but suffice it to say that this formula is powerful, and a significant step up in allowing accurate calculations of interest rates. If you download the sample spreadsheet and play with the numbers, you can see the effect that an early first payment and a short month (February) have on the XIIR result.

  • better EIR = Excel XIIR()
  • XIRR() = 32.16%

So XIRR is a more accurate way to calculate the interest rate because it takes into account both actual payment dates and the effect of compound interest.

These tools are what allow MFTransparency to calculate accurate interest rates that are comparable between MFIs, despite different/irregular repayment schedules, additional fees, etc. So while they may seem mundane, they are actually the crux of transparent pricing!

In a future post, I’ll explain how software programmers can efficiently add advanced Excel 2007-like XIRR calculations to their software programs.

Further Information

For those of you who would like to know more about interest rate calculation, I encourage you to check out the following:

The next post in this series, “Calculating Interest Rates Using Newton’s Method” is a more advanced version of this article that explains the algorithm behind the XIIR formula, and how this technique can be applied in programming languages like C#, python, or Java.

P.S. For investors, Excel’s XIRR feature can also be used to calculate a Personal Investment Rate of Return, which is more relevant than the values that appear in a fund’s prospectus because it takes into account the investor’s actual purchase history.

Calculating interest rates using Newton’s method

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Here’s a post I wrote for Microfinance Transparency on March 5, 2010

In a previous blog post, I described how to use a spreadsheet like Microsoft Excel to calculate interest rates. Again, interest rate calculations are at the core of MFTransparency’s ability to provide accurate data that can be compared across various products offered by numerous MFIs. In the last post we looked at Excel’s IRR and XIRR functions and concluded that XIRR is more accurate because it takes into account the actual payment dates of the loan and thus allows us to calculate annualized interest rates even with irregular repayment schedules.

But for the more technical among us, I realize that even this may not be sufficient. Today I’m going to demonstrate how to write a computer program that is as accurate as Excel 2007’s XIRR function. This article is likely to be of less broad interest, but it provides transparency into how we will calculate interest rates for future data collection trips; and it may be useful for MFIs that wish to automate interest rate calculations for a larger data set than can be handled with Excel.

Let’s start with the EIR formula and describe two techniques.

  • EIR = cf*(1+rate)^n

cf = cashflow, n=number of periods/year

Simple Guess and Check

The first technique is to make a guess about the interest rate and then run the numbers through the EIR formula to see how close you are. You then iterate, guessing somewhere in the middle of your previous guesses, or widening your area by doubling. The advantage of this technique is that it is simple and it gets you the right answer eventually (or at least fairly accurately given enough guesses).

Newton’s method

A more advanced way to solve the EIR formula is to use Calculus. It’s still a “guessing” technique, but it is much more efficient and elegant.

It’s easier to visualize this technique if we draw a graph and plot an initial guess, with the goal of finding the point on the graph where it crosses the x axis.

We start by making an initial guess and then figuring out what the “tangent” line at that point would be. This is the same thing as the derivative of the EIR calculation:

  • EIR = cf*(1+rate)^n
  • f’rate = cf*n*(rate+1)^(n-1) = pink tangent line is the derivative

cf = cashflow, n=number of periods/year

We can then figure out where the tangent line intersects the x axis and use that to make a much more accurate second guess.

The speed advantage the Newton-Raphson method has over a simple guess and check technique is quite remarkable. It is common to be able to achieve a result that matches Excel to within 8 decimal places in 5-10 iterations.

Implementing Newton-Raphson

Fortunately for me, I didn’t have to implement the entire Newton-Raphson algorithm myself because the programming language I use already has a library to do this. I just give it the EIR function and the derivative function and it does the rest.

For the programmers out there, here are a few links to implementations in various languages:

  • C#: uses bisection rather than Newton’s method
  • Java
  • Python

Related Information:

For those of you who would like to know more, please explore the following links:

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