Wadyathink?
Hey everyone, preparing for Lvl 2 in May. I've finished the studying all the material and am currently working through the sections I struggle the most in. I would say Fixed Income is my biggest pain point currently and am trying to spend time over the next week consolidating the info and getting a firmer grasp on each concept.
One thing I was curious of is Duration and Convexity in Bonds with Embedded Options.They seem to function similarly to Delta and Gamma in Derivatives. Are these two ideas in essence the same?
Would really appreciate any guidance you may have! Sorry if it's a basic question, like I said, not my most favorite section.
I appreciate it!
Hi! Guys I have a few basic and rather embarrasing questions, seeing as I'm a 1st semester master student, but I feel our orientation this semester was a joke, and we were left to fend for ourselves.
I'm not even in baltimore, so obviously havent made many friends :/
After how this semester is going, I'm daunted by the prospect of the next semester. I wanted to ask you guys for some manageable class recommendations.
I wanted to ask about Nonlinear Optimization I w. Prof. Amitabh Basu and Intro to Convexity w. Prof. Soledad Villar? I need to take one optimization class (grad level), so if you think there are better options than these, please suggest them :)
And 2 really basic questions... Also, when does class registration start for Fall? I've read June 14 online. And is there a way the department offers any help/suggestions for classes to pick?
Thank you so much! I hope you all are staying strong! Only a month left
I'm 29 years old. Fit, healthy, and experience little to no back pain. I've just started seeing a biokineticist because I'm worried about curve progression and pain later on in life. I've started a daily regime of exercise focused on spine mobilization and strength.
I've also slowly started researching different types of surgeries and their benefits and drawbacks. I've been reading about ASC surgery which sounds very interesting and far less invasive than spinal fusion, but I'm not sure if surgery is something I want, or if it's even necessary. My goal is to keep up the exercises and reassess in a year's time to see if there's been an improvement and make a call from there.
Anyway, I know how you guys love xrays so I thought I'd share.
https://preview.redd.it/d2q0lys82un61.jpg?width=3698&format=pjpg&auto=webp&s=7b6b49f9ee7ac5c458a61e88bce8eba74535bdc2
Are both of these formulas valid?
Convexity (approximiation) = (PV- - PV+ - 2PV0) / PV0 * change in YTM^2
Convexity = (MacDur^2 + MacDur + Dispersion) / (1 + CFY)^2
Also in the second formula what exacly is the dispersion term (obviously the dispersion of CFs but what value would I plug) and how is the CFY (cash flow yield) different from YTM (yied to maturity)?
Thanks
Hey guys! I’m currently studying for Exam FM using Coaching Actuaries. CA has an entire section devoted to Duration and Convexity, but I don’t see anything about it on the syllabus for this exam. I’m obviously going to go over it, but wondering if I shouldn’t stress that section so much? I guess I’m just confused if they can ask questions that were not explicitly listed on the syllabus. Thanks in advance for your help!
There comes a point in any big selloff in Treasury bonds when the move becomes so pronounced that it starts to feed on itself. Increases in yields force a crucial group of investors to sell Treasuries, which in turn leads to further increases in yields. Two months into this rout, that moment appears to have arrived, and it’s beginning to send shudders throughout all corners of U.S. financial markets.
The forced sellers are investors in the $7 trillion mortgage-backed bond market. Their problem is that when Treasury yields -- which strongly influence home-loan rates -- suddenly rise sharply, many Americans lose interest in refinancing their old mortgages. A reduced stream of refinancings means mortgage-bond investors are left waiting for longer to collect payments on their investments. The longer the wait, the more financial pain they feel as they watch market rates climb higher without being able to take advantage of them. Their answer: unload the Treasury bonds they hold with long maturities or adjust derivatives positions -- a phenomenon known as convexity hedging -- to compensate for the unexpected jump in duration on their mortgage portfolios. The extra selling just as the market just as the market is already weakening has a history of exacerbating upward moves in Treasury yields -- including during major “convexity events” in 1994 and 2003.
Read more at: https://www.bloombergquint.com/onweb/convexity-hedging-haunts-markets-already-reeling-from-bond-rout Copyright © BloombergQuint
This go around, the Federal Reserve’s massive presence in the mortgage-bond market -- it’s adding about $40 billion of the securities each month to its balance sheet -- has created something of a stabilizing force that has kept the market’s hedging needs in check. Even so, waves of mortgage investors adjusting their portfolios could still have an outsized impact on rates that reverberates across asset classes, market watchers say. “Everyone -- except the Fed -- is a convexity hedger at some point because as your portfolio keeps getting longer with the rise in rates it will become increasingly painful,” said Joshua Younger, head of U.S. interest-rate derivatives strategy at JPMorgan Chase & Co. “There’s more flexibility now for those who need to hedge so rates rising won’t cause the train to go off the rails. But even a train on the rails can be difficult to stop.”
Ten-year Treasury yields surged as much as 0.23 percentage point to a more than one year high of
... keep reading on reddit ➡Hi, i am currently working on my final project and because of that i am learning opencv. My goal is find hand gestures. Right now studying on a simple project. Project subject is try to find contours, hull and defects on a hand photo. But when i run code get this error:
Traceback (most recent call last):
line 23, in <module>
defects = cv.convexityDefects(cnt, hull)
TypeError: Expected Ptr<cv::UMat> for argument 'convexhull'
And my code is here:
import cv2 as cv
import numpy as np
path = r'C:\Users\Archosan\Desktop\Python\hand.jpg'
img = cv.resize(cv.imread(path), (350, 700))
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
ret, thresh = cv.threshold(gray, 230, 255, cv.THRESH_BINARY_INV)
contours, hierarchy = cv.findContours(thresh.copy(), cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)[:2]
cnt = contours[0]
hull = []
for i in range(len(contours)):
hull.append(cv.convexHull(contours[i], False))
cv.drawContours(img, hull, i, (0, 0, 255), 2, 8)
if len(hull) > 0:
defects = cv.convexityDefects(cnt, hull)
for i in range(defects.shape[0]):
s, e, f, d = defects[i, 0]
start = tuple(cnt[s][0])
end = tuple(cnt[e][0])
far = tuple(cnt[f][0])
cv.circle(img, far, 5, [0, 0, 255], -1)
cv.drawContours(img, contours, -1, (0, 255, 0), 3)
cv.imshow('Image', img)
cv.imshow('Thresh', thresh)
cv.waitKey(0)
I am new at python programming so hull and defects lines, i found them on internet. Can anyone help me for this issue? Thanks and sorry for bad english.
Hello! I am about 380 pages deep into Infinite Jest and though it has been explained a bit in this time I seem to have some trouble understanding what exactly the Concavity is. Is it a massive dump on the North East? Who owns it? When did it begin?
If this is explained later in the book please just tell me to keep reading and I'll be on my way. Thanks!
Could someone help and explain how the formula for convexity is algebraically re-organized to approximate the bond price change?
i.e. How does this:
Convexity = ((P+) + (P-) - (2Po)) / (2 x ((Po)(change in Y)²)))
become this:
convexity adjustment = .5 x convexity x (change in Y)^2
Does it have to do with how there are essentially two convex areas on either side of the line tangent (aka duration) to the price/yield curve?
Interesting mechanics impacting bond markets and, specifically, MBS portfolios. This is one of the reasons I invest in the asset class indirectly: let the respective mgmt teams deal with it.
As US bonds fall and 10y rises, this pushes mortgages rates higher slowing / stopping refinancing activities, which increases the duration of existing MBS portfolios that hold 15-30y notes. This duration increase forces the portfolios to hold lower interest assets longer while they lose value to the rising rates. The hedges that offset this mechanic accelerates the downward pressure on bond prices and serves as a feedback loop.
https://www.bloomberg.com/news/articles/2021-02-25/convexity-hedging-haunts-markets-already-reeling-from-bond-rout
p.s.: R.I.P. any risk-parity traders doing the UPRO/TMF play or similar strategy dependent on correlations holding.
Hi all! I've been going over a thought for a few days and was wondering whether or not this sub knew whether it was 'a thing' or not? Allow me to explain...
I'm from a physics/engineering background, and I only recently learnt the definition of what it means for a shape to be convex in the technical sense - that the line joining any two points in the shape also lies entirely within the shape. I've only ever heard of shapes being 'convex' or 'not convex', and from this definition I can see why, but what if 'almost all' the lines joining 'almost all' pairs of points lie in the shape? It's not hard to imagine a shape tat is 'almost' or 'partially' convex in this sense.
So, with this in mind, here is my idea:
- Pick two random points in a shape.
- Draw the line that joins them.
- The proportion of these lines that lie within the shape is that shape's 'convexity'
All convex shapes (by the original definition) have convexity = 1. This definition can be applied to 2D or 3D shapes, as well as anything in higher dimensions.
Attached here, for instance, is the convexity of a torus of varying small-radius-to-big-radius proportion. I would love some feedback or places to read up on this kind of thing.
I've also got similar results for some variable 2D shapes - I can share these in the comments tomorrow.
I sometimes set myself little computing projects like this to just keep things ticking over, so if anyone has any suggestions on how this could be developed further I'm all ears. :)
https://preview.redd.it/ud62gb282wz51.png?width=1920&format=png&auto=webp&s=a63910a48dfc84727b929de0919916f14259ed6a
I saw this today and it made me smile. https://youtu.be/ijD2KSXWDyo
I know what a convex curve looks like on a graph. But what is the practical consequence of something being convex? For example, if all I know about a phenomenon is that it is convex, what can I tell about it?
Can you wear a convex flange even if flat abdomen? I have a flat stomach but my stoma retracts.
Can someone please tell me why there is a negative correlation between coupon rate and convexity of the bond? Hence, zero coupon bonds have the highest convexity. I've been trying to find logic here but just can't.
I already tried r/learnmath but nobody answered there, but is there anyone who knows good resources on learning what quasi-convexity and concavity really entails. I got a definition and that's about all but I can't really work with that. Everything is appreciated; a good youtube video or just good text. I would rather not get the title of a textbook, as I mostly don't have that.
If forecast YC change is X, then convexity strategy is Y to benefit from that YC change:
YC Flattening => Buy Convexity
YC Steepening => Sell Convexity
YC Curvature increasing => Buy Convexity
YC Curvature decreasing => Sell Convexity
Are these correct? I recall there was a summary/table of all the changes in the curriculum but I cannot find it.
Also, the curriculum does not make clear: is YC the yield curve of the bond portfolio? or the Risk Free benchmark yield curve?
P.S. YC Curvature is defined as Butterfly Spread = (mid-short) + (mid - long)
It is well known that the loss surface of neural networks (especially CNN and such) are non-convex. At the same time, a huge motivation of non-convex optimization is the training of neural networks. There are a host of non-convex condition that people have came up with (Polyak-Lojasiewicz, star-convex, quasi-convex, weakly convex, pseudo-convex...) , however, it usually turns out that their "non-convexity" only applies to some toy problem and not neural network in general.
This begs two questions:
- What type of non-convexity do the loss surface of neural networks have? Surely they must fall partially under one of the dozens of non-convex condition that people have proposed?
- What results from non-convex optimization applies to the training of neural networks?
I came across an etf SPYC who's principal holdings are 98% IVV (SP500 ETF), 1% of the other funds are dedicated to cheap out of the money puts to cover large draw downs, and the last 1% of the fund is used for cheap out of the money calls to cover large up ticks. I feel like my brain is full of recency bias given the COVID draw down and uptick where this fund would have performed extremely well comparatively.
The major risk I see is that the market stays relatively stagnant while holding this asset. If that were the case then you would essentially be scrubbing of 2% each year as the options expire. Thoughts?
Hi, I am learning mathematics for economics and really struggling to understand concepts like convexity, quasi-convexity, etc. I am following Sydsaeter and Hammond but it is not helping much. I believe this is a really important foundational topic for optimization? Would highly appreciate some resources on this!
Can someone conceptually explain the concept of convexity, and why callable bonds at low yields will have negative convexity versus callable and putable bonds at high yields?? I literally can’t wrap my head around this concept nor understand it.
Also am I right in the fact that the longer the maturity and lower the coupon rate, the higher the duration of the bond?? I also don’t understand why callable bonds exhibit more price volatility at lower yields. I understand the bond is more likely to be called when yields go lower, but why does that create more price volatility?
I understand I packed a ton into here lol, honestly any help on any of these questions will be huge. I’ve memorized the concepts but want to actually understand the why
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