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@maeverybrooke1:

maeverybrooke1
maeverybrooke1
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Region: US
Tuesday 03 November 2026 00:01:37 GMT
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Comments

juemgixodc7
Justin :
Good girl
2026-11-03 18:38:40
11
lostnotfound1230
lostnotfound1230 :
Gorgeous
2026-11-03 21:45:36
0
marverlous09
MarVerlous :
Lovely ☺️😋😍
2026-11-03 21:32:07
0
maa.gyk
Maa Gyk :
Good lord 😍😍
2026-11-03 18:23:54
8
wellfukthathappend
StupidMetalWiddler :
those look expensive
2026-11-03 20:30:43
0
nathanrobinson857
nathanrobinson857 :
looks expensive 🥰
2026-11-03 20:40:26
0
sergiy_in_nyc
Sergiy🔱 :
You're too perfect😘😘😘
2026-11-03 21:21:55
0
hoesdown4fo4
Punisher :
thanks for that
2026-11-03 21:26:15
0
illestwill_i_am
The Illest Will :
does it now🤔
2026-11-03 21:03:46
0
rockmetwice9
How$WillAM$ :
2026-11-03 19:33:11
0
jstubbkrack
JStubbKrack :
Very true 👏🏼👏🏼👏🏼☺️☺️☺️💪🏼💪🏼💪🏼
2026-11-03 18:45:46
0
prfctallamericanbtch
prfctallamericanbtch :
2026-11-03 18:40:01
0
emiles4219
EMiles :
oh damn 😳
2026-11-03 19:40:22
0
andrewmonroew
PhallusFlanigan :
Yayyyy
2026-11-03 18:57:22
0
phantom12000
phantom12000 :
2026-11-03 18:53:57
0
momentbetweentikntok
user461106287418 :
Are you sure this is supposed to keep bones from hardening?
2026-11-03 18:43:06
0
nativej5
native J :
that's what I'm talking about. U get my follow
2026-11-03 21:11:46
0
longjohnsilver637
longjohnsilver :
I’m moving to Japan to farm rices for the next couple weeks so I’m not going anywhere in this area but I’m sure I will get there in time to see the first time you guys come to my place I would like you guys are
2026-11-03 21:36:32
0
simonarey2
Simon :
when u going live again🙄
2026-11-03 06:02:04
0
pullmyfinger72
pullmyfinger72 :
2026-11-03 20:23:51
0
user10008355
user1000835 :
MOMMY
2026-11-03 15:37:56
0
erik70652
Erik :
How original
2026-11-03 18:53:16
0
therealhighfry
HighFry :
For women maybe…. Does the opposite for men… tends to make them stiffen up
2026-11-03 21:16:37
0
alex.a8219
Alex A :
sexy bounce
2026-11-03 18:20:31
0
sienjen22
Sienjen las piñas m. :
❤️❤️❤️
2026-11-03 01:55:20
0
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Other Videos

КОТІК 🐈 | #ОлександрРибак #автор #песни #котік #viral
КОТІК 🐈 | #ОлександрРибак #автор #песни #котік #viral


Okay, you’re gonna be my favorite game again. 😭 #playtogether #playtogethervng #playtogethergame
Okay, you’re gonna be my favorite game again. 😭 #playtogether #playtogethervng #playtogethergame
MAIN CHARACTER ENERGY �
MAIN CHARACTER ENERGY �


➡️Quantitative Finance from Scratch, part 40: GJR GARCH. The goal is simple: forecast how big tomorrow's moves will be. Volatility comes in clusters, and plain GARCH uses that by building tomorrow's forecast from today's move squared and today's forecast. Squaring wipes out the sign, though, so a 4 % drop and a 4 % rise raise tomorrow's forecast by exactly the same amount. In real stock markets a drop raises volatility much more than a rise of the same size. One reason is leverage: when the price falls, the company's debt weighs more relative to its stock, so the stock itself becomes riskier. Glosten, Jagannathan and Runkle captured this in 1993 with one extra weight, gamma, that only switches on after a down day. The model is now known as GJR GARCH: tomorrow's variance = omega + (alpha + gamma x I) x today's move squared + beta x today's variance, where I is 1 after a down day and 0 otherwise. The code (gjr_reel.py, 16 lines of Python with numpy) uses typical values omega 0.02, alpha 0.02, gamma 0.12 and beta 0.90, in percent squared per day, so the normal level is a volatility of 1 % a day. After a 4 % rise: 0.02 + 0.02 x 16 + 0.90 x 1 = 1.24, a forecast of 1.11 %. After a 4 % drop: 0.02 + 0.14 x 16 + 0.90 x 1 = 3.16, a forecast of 1.78 %. The extra volatility is 0.11 against 0.78 percentage points, about seven times as much (6.8). Plain GARCH with the values from part 39 gives 1.58 % in both cases, so right after a sell off it underestimates the risk. Source: Glosten, L. R., Jagannathan, R. and Runkle, D. E. (1993), On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks, Journal of Finance 48(5), 1779 to 1801. Honest notes: the parameter values are typical textbook sizes, not estimates from real data. All market data in the video is simulated. #quantfinance #finance #volatility #riskmanagement #timeseries Education only, not financial advice.
➡️Quantitative Finance from Scratch, part 40: GJR GARCH. The goal is simple: forecast how big tomorrow's moves will be. Volatility comes in clusters, and plain GARCH uses that by building tomorrow's forecast from today's move squared and today's forecast. Squaring wipes out the sign, though, so a 4 % drop and a 4 % rise raise tomorrow's forecast by exactly the same amount. In real stock markets a drop raises volatility much more than a rise of the same size. One reason is leverage: when the price falls, the company's debt weighs more relative to its stock, so the stock itself becomes riskier. Glosten, Jagannathan and Runkle captured this in 1993 with one extra weight, gamma, that only switches on after a down day. The model is now known as GJR GARCH: tomorrow's variance = omega + (alpha + gamma x I) x today's move squared + beta x today's variance, where I is 1 after a down day and 0 otherwise. The code (gjr_reel.py, 16 lines of Python with numpy) uses typical values omega 0.02, alpha 0.02, gamma 0.12 and beta 0.90, in percent squared per day, so the normal level is a volatility of 1 % a day. After a 4 % rise: 0.02 + 0.02 x 16 + 0.90 x 1 = 1.24, a forecast of 1.11 %. After a 4 % drop: 0.02 + 0.14 x 16 + 0.90 x 1 = 3.16, a forecast of 1.78 %. The extra volatility is 0.11 against 0.78 percentage points, about seven times as much (6.8). Plain GARCH with the values from part 39 gives 1.58 % in both cases, so right after a sell off it underestimates the risk. Source: Glosten, L. R., Jagannathan, R. and Runkle, D. E. (1993), On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks, Journal of Finance 48(5), 1779 to 1801. Honest notes: the parameter values are typical textbook sizes, not estimates from real data. All market data in the video is simulated. #quantfinance #finance #volatility #riskmanagement #timeseries Education only, not financial advice.

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