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➡️ Quantitative Finance from Scratch, part 39: GARCH. Markets show a clear pattern: calm weeks follow calm weeks, and high volatility days arrive in groups. This is called volatility clustering. Robert Engle introduced the idea in 1982 with the ARCH model (Econometrica), Tim Bollerslev extended it to GARCH in...

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QuantFinanceToGo

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➡️ Quantitative Finance from Scratch, part 35: how market makers make money. The first source is the spread. Like an exchange booth, a market maker buys a little below the fair price at the bid and sells a little above it at the ask, so every trade earns half the gap whatever the stock does next. After selling a call, it buys delta shares, so small moves no longer matter. The second source comes from the other Greeks: theta is the income, because the call it owes loses value every day, and gamma is the cost, because after every big move it must buy shares high and sell them low to keep the hedge. If the stock moves exactly as much as the implied volatility in the price, income and cost cancel. If it stays calmer, the market maker earns, and if it moves more, it loses. Vega is the risk to this income, because a jump in implied volatility makes the call it owes more expensive. If a customer sells it the call instead, everything flips. The code (marketmaker.py, 16 lines) sells our call from part 33 at the ask (fair $10.45, ask $10.55, bid $10.35) and hedges it every day on 2,000 simulated years with an implied volatility of 20 %. With a real volatility of 16 % it earns $1.59 per call on average and every path ends in profit; at 20 % it earns $0.10, about the spread; at 24 % it loses $1.41 and only 0.3 % of paths end in profit. Honest notes: a simplified model with one option, daily hedging, Black Scholes prices, constant volatility, no trading costs, no dividends and no other customers. Real market makers manage many options at once and also face fees, inventory limits and jumps. #quantfinance #options #marketmaking #greeks #finance
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➡️ Quantitative Finance from Scratch, part 35: how market makers make money. The first source is the spread. Like an exchange booth, a market maker buys a little below the fair price at the bid and sells a little above it at the ask, so every trade earns half the gap whatever the stock does next. After selling a call, it buys delta shares, so small moves no longer matter. The second source comes from the other Greeks: theta is the income, because the call it owes loses value every day, and gamma is the cost, because after every big move it must buy shares high and sell them low to keep the hedge. If the stock moves exactly as much as the implied volatility in the price, income and cost cancel. If it stays calmer, the market maker earns, and if it moves more, it loses. Vega is the risk to this income, because a jump in implied volatility makes the call it owes more expensive. If a customer sells it the call instead, everything flips. The code (marketmaker.py, 16 lines) sells our call from part 33 at the ask (fair $10.45, ask $10.55, bid $10.35) and hedges it every day on 2,000 simulated years with an implied volatility of 20 %. With a real volatility of 16 % it earns $1.59 per call on average and every path ends in profit; at 20 % it earns $0.10, about the spread; at 24 % it loses $1.41 and only 0.3 % of paths end in profit. Honest notes: a simplified model with one option, daily hedging, Black Scholes prices, constant volatility, no trading costs, no dividends and no other customers. Real market makers manage many options at once and also face fees, inventory limits and jumps. #quantfinance #options #marketmaking #greeks #finance

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Mike McGlone calls Bitcoin a horribly poor performing asset, with no gain on the S&P or gold since 2021 and at three times the S&P's volatility "Bitcoin versus the S&P 500 is at the same level as 2021, yet it trades about three times the volatility" "Bitcoin versus gold is at the same level since 2021, and annual volatility has dropped to its lowest level ever" "So I put on my FRM and CFA hat, and I look at this as a horribly poor performing asset. High volatility, high correlation, and underperforms beta, particularly on a volatility weighted basis"
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Mike McGlone calls Bitcoin a horribly poor performing asset, with no gain on the S&P or gold since 2021 and at three times the S&P's volatility "Bitcoin versus the S&P 500 is at the same level as 2021, yet it trades about three times the volatility" "Bitcoin versus gold is at the same level since 2021, and annual volatility has dropped to its lowest level ever" "So I put on my FRM and CFA hat, and I look at this as a horribly poor performing asset. High volatility, high correlation, and underperforms beta, particularly on a volatility weighted basis"

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82 minutes of an MIT lecture on Volatility Modeling > 6 months of any trading course or guides on YouTube You'll finally understand what conditional variance, GARCH and volatility clustering actually mean Bookmark & watch, no matter what
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82 minutes of an MIT lecture on Volatility Modeling > 6 months of any trading course or guides on YouTube You'll finally understand what conditional variance, GARCH and volatility clustering actually mean Bookmark & watch, no matter what

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Robert Engle was awarded the Nobel Prize for the ARCH time series model, which is probably the most used model for calculating volatility in today's markets. Now that AI is solving 80-year-old mathematical conjectures at an unprecedented speed, isn’t it time to rethink how we look at risk? This is a question being asked by Robert Engle, Marcos Lopez de Prado, and ADIA Lab (adia_lab) on Crunch. The ADIA Lab Structural Break Challenge will reward the best crunchers with a bounty of 100,000 USDC.
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Robert Engle was awarded the Nobel Prize for the ARCH time series model, which is probably the most used model for calculating volatility in today's markets. Now that AI is solving 80-year-old mathematical conjectures at an unprecedented speed, isn’t it time to rethink how we look at risk? This is a question being asked by Robert Engle, Marcos Lopez de Prado, and ADIA Lab (adia_lab) on Crunch. The ADIA Lab Structural Break Challenge will reward the best crunchers with a bounty of 100,000 USDC.

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➡️ Quantitative Finance from scratch part 12 closes season 1. Every part was one step in a single story about a bet with an edge, and every number below comes from that part's own run. Part 1, expected value: a $1 bet on red wins 18 times out of 37, so it loses 2.7 cents on average, and over a million bets the casino made $27,288. Part 2, the random walk: coin flips add up to something that looks like a stock chart, and the same edge becomes a drift of 2.7 cents per step. Part 3, the square root rule: after 100 random steps the typical distance is about 10, because randomness grows with the square root of time. Part 4, the bell curve: many small random steps build it, with 68 % of results within one standard deviation. Part 5, returns: percent moves multiply, so after -50 % you need +100 % to get back. Part 6, present value: at 5 % interest, $100 paid in ten years is worth $61.39 today. Part 7, volatility: two investments with the same average swing about 4 % and 32 % a year. Part 8, the Sharpe ratio (William F. Sharpe, 1966): extra return per unit of risk, 0.36 for the wild investment and 0.99 for the calm one. Part 9, volatility drag: +50 % and then -50 % leaves you 25 % down. Part 10, correlation: the less two assets move together, the less a 50/50 mix swings, from 19 % down to 10 %. Part 11, diversification: ten assets cut the risk from 20 % to 12.2 %, above a floor of 11 % that the shared market sets (Markowitz, "Portfolio Selection", Journal of Finance, 1952). The big picture: how big the edge is, how much it swings, and how much of that swing you can spread away. Season 2 brings portfolios, bets and evidence. This video is for education only, and nothing in it is financial advice. #quantfinance #finance #riskmanagement #portfolio #probability
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➡️ Quantitative Finance from scratch part 12 closes season 1. Every part was one step in a single story about a bet with an edge, and every number below comes from that part's own run. Part 1, expected value: a $1 bet on red wins 18 times out of 37, so it loses 2.7 cents on average, and over a million bets the casino made $27,288. Part 2, the random walk: coin flips add up to something that looks like a stock chart, and the same edge becomes a drift of 2.7 cents per step. Part 3, the square root rule: after 100 random steps the typical distance is about 10, because randomness grows with the square root of time. Part 4, the bell curve: many small random steps build it, with 68 % of results within one standard deviation. Part 5, returns: percent moves multiply, so after -50 % you need +100 % to get back. Part 6, present value: at 5 % interest, $100 paid in ten years is worth $61.39 today. Part 7, volatility: two investments with the same average swing about 4 % and 32 % a year. Part 8, the Sharpe ratio (William F. Sharpe, 1966): extra return per unit of risk, 0.36 for the wild investment and 0.99 for the calm one. Part 9, volatility drag: +50 % and then -50 % leaves you 25 % down. Part 10, correlation: the less two assets move together, the less a 50/50 mix swings, from 19 % down to 10 %. Part 11, diversification: ten assets cut the risk from 20 % to 12.2 %, above a floor of 11 % that the shared market sets (Markowitz, "Portfolio Selection", Journal of Finance, 1952). The big picture: how big the edge is, how much it swings, and how much of that swing you can spread away. Season 2 brings portfolios, bets and evidence. This video is for education only, and nothing in it is financial advice. #quantfinance #finance #riskmanagement #portfolio #probability

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If you've been trading for any amount of time, you're probably already aware of the concept of volatility and the impact it can have on the broader market. However, a concept that traders of all levels often miss is volatility supply. Volatility has a supply and demand curve, just like any other product, and it can be created or destroyed through a number of different means. Here's Cem Karsan 🥐 giving a great explanation of vol supply in response to a (very eloquent) YouTube comment asking about it! #StockMarket #Volatility #Investing #Finance #OptionsTrading
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If you've been trading for any amount of time, you're probably already aware of the concept of volatility and the impact it can have on the broader market. However, a concept that traders of all levels often miss is volatility supply. Volatility has a supply and demand curve, just like any other product, and it can be created or destroyed through a number of different means. Here's Cem Karsan 🥐 giving a great explanation of vol supply in response to a (very eloquent) YouTube comment asking about it! #StockMarket #Volatility #Investing #Finance #OptionsTrading

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Shell CEO Wael Sawan says the company is built to "unlock value during times of high volatility," as conflicts in Ukraine and the Middle East drive a surge in trading and refining
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Shell CEO Wael Sawan says the company is built to "unlock value during times of high volatility," as conflicts in Ukraine and the Middle East drive a surge in trading and refining

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➡️ Part 27 of Quantitative Finance From Scratch: the Fama-French three factor model. The market beta alone (parts 14 and 15) explains stock returns only in part, so Fama and French added two more factors, size (part 26) and value (part 25). They introduced the model in 1993 in the Journal of Financial Economics ("Common risk factors in the returns on stocks and bonds"). A factor is the return of one portfolio minus another: small minus big (SMB) and high minus low (HML, cheap minus expensive). In the regression, a stock's return above the risk-free rate equals alpha, plus a beta times each factor's return, plus an error term for the stock's own swings. Alpha is what the three factors cannot explain. In our simulation (nine portfolios, three simulated factors, 50 years of monthly returns, assumed numbers, true alpha zero), the market alone leaves the small value portfolio with an alpha of 7.9 % a year and explains only 46 % of its ups and downs. With all three factors, its alpha drops to -0.9 % a year, close to the built-in zero, the model explains 90 %, and the regression finds betas of 1.00, 1.00 and 0.96. The structure is built into the model, so it shows how the method works and makes no claim about real markets. In real data, the three factors explain much of the differences in average returns, though not all: in their 1996 tests, the continuation of short-term returns (momentum) stayed unexplained, and Fama and French later added two more factors. Eugene Fama shared the 2013 Nobel Memorial Prize in Economic Sciences with Lars Peter Hansen and Robert Shiller "for their empirical analysis of asset prices". Education only, not financial advice. #quantfinance #famafrench #factorinvesting #python #finance
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➡️ Part 27 of Quantitative Finance From Scratch: the Fama-French three factor model. The market beta alone (parts 14 and 15) explains stock returns only in part, so Fama and French added two more factors, size (part 26) and value (part 25). They introduced the model in 1993 in the Journal of Financial Economics ("Common risk factors in the returns on stocks and bonds"). A factor is the return of one portfolio minus another: small minus big (SMB) and high minus low (HML, cheap minus expensive). In the regression, a stock's return above the risk-free rate equals alpha, plus a beta times each factor's return, plus an error term for the stock's own swings. Alpha is what the three factors cannot explain. In our simulation (nine portfolios, three simulated factors, 50 years of monthly returns, assumed numbers, true alpha zero), the market alone leaves the small value portfolio with an alpha of 7.9 % a year and explains only 46 % of its ups and downs. With all three factors, its alpha drops to -0.9 % a year, close to the built-in zero, the model explains 90 %, and the regression finds betas of 1.00, 1.00 and 0.96. The structure is built into the model, so it shows how the method works and makes no claim about real markets. In real data, the three factors explain much of the differences in average returns, though not all: in their 1996 tests, the continuation of short-term returns (momentum) stayed unexplained, and Fama and French later added two more factors. Eugene Fama shared the 2013 Nobel Memorial Prize in Economic Sciences with Lars Peter Hansen and Robert Shiller "for their empirical analysis of asset prices". Education only, not financial advice. #quantfinance #famafrench #factorinvesting #python #finance

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➡️ Part 25 of Quantitative Finance From Scratch: the value factor. A value stock has a low price to book ratio; researchers flip it to book to market and compare the cheapest stocks with the most expensive ones. In our simulation (500 simulated stocks, 30 years, assumed numbers) the value portfolio earns 4.7 % a year with a t statistic of 4.29. It is a model with a small built-in premium, so it shows how the method works, not what real markets will pay. The chart in the hook is a schematic, not real data. Value and momentum have tended to move in opposite directions in real markets. In our simulation they are almost unrelated (correlation 0.04), and the 50/50 mix is still smoother than either alone. Education only, not financial advice. #quantfinance #valueinvesting #factorinvesting #python #finance
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➡️ Part 25 of Quantitative Finance From Scratch: the value factor. A value stock has a low price to book ratio; researchers flip it to book to market and compare the cheapest stocks with the most expensive ones. In our simulation (500 simulated stocks, 30 years, assumed numbers) the value portfolio earns 4.7 % a year with a t statistic of 4.29. It is a model with a small built-in premium, so it shows how the method works, not what real markets will pay. The chart in the hook is a schematic, not real data. Value and momentum have tended to move in opposite directions in real markets. In our simulation they are almost unrelated (correlation 0.04), and the 50/50 mix is still smoother than either alone. Education only, not financial advice. #quantfinance #valueinvesting #factorinvesting #python #finance

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With the help of Claude, I developed an innovative approach to analyzing market volatility and earned $9,670 in 21 days. The model views volatility not as a static indicator, but as a dynamic process of interaction between two forces: > Trend-reinforcing pressure: Pressure reinforcing the current trend. > Mean-reverting pressure: Forces stabilizing the market and returning the price to average values. The mathematical core of the model: V = P(+) - P(-) Key advantages: > Tracking the accumulation of pressure > Risk adaptability > Uniqueness This is not just a theory: it is a working tool that monetizes market complexity into concrete profit. In my article, I revealed the secret and gave access to 10 folders for Claude with the help of which this was created.

Bober_smart

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Listen to this... Cem Karsan 🥐 is warning that some type of volatility shock potentially hits at the end of the month. He is also warning that volatility begins expanding due to flows into the fall. Keep in mind, there was a volatility shock precisely 1 month before the Feb 2020 crash. Historically markets also like to become unstable in September.
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Listen to this... Cem Karsan 🥐 is warning that some type of volatility shock potentially hits at the end of the month. He is also warning that volatility begins expanding due to flows into the fall. Keep in mind, there was a volatility shock precisely 1 month before the Feb 2020 crash. Historically markets also like to become unstable in September.

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Bitcoin is volatile. Thankfully. I’ve been building a simple crowd behavior simulation to show how volatility naturally emerges with price discovery. In this sim, volatility is not something we add to the model. It is what the model produces when people disagree, learn at different speeds, and use price itself as information. 1/n

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One common reason why traders blow up is because of poor position sizing. In other words, how much do you bet on a trade. You can be right on direction 60% of the time and still lose everything if you size your positions poorly. One oversized trade can wipe out months of gains. This is why position sizing is a big part of risk management. Sandeep Rao - SEBI Reg. RA🖖 recently spoke to Tom Basso, one of the original Market Wizards, to discuss his approach to trading. I was listening to the interview and the one thing that stood out to me was how Tom's thinking on position sizing evolved over decades. He started simple: risk the same percentage of equity on every trade, inspired by Larry Hite's philosophy that every bet should be equal in terms of potential loss. But then came a silver trade with explosive volatility. Clients were calling, nervous about the wild swings. So he realized it wasn't just about the amount you could lose—it was also about the speed of movement. High volatility creates psychological stress that leads to poor decisions. So he added a second layer: volatility as a percentage of equity. Now he'd calculate both risk % and volatility %, then take the smaller of the two. Then came the third refinement: margin-to-equity ratios. Some markets have deceptively low risk and volatility but require high margin because of sudden jump risk. By incorporating all three factors, he never got caught overexposed. The result was a position sizing system that automatically scales down when markets get too volatile, protects against margin squeezes, and keeps portfolio risk in check. It's really interesting conversation. Link to the full interview is in the comments.

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McCullough: Bitcoin Doesn’t Care About Your Cost Basis 🔊 Hedgeye CEO Keith McCullough made it clear this week: when downside accelerates, the market isn’t debating your cost basis or favorite “support level.” In fact, “the situation is accelerating at an accelerating rate,” explains McCullough. That’s why we focus on the rate of change, especially the volatility of volatility. “The volatility of volatility is going to get you to the right answer faster than the crowd.” Get Keith's quantitative Risk Range™ Signals and daily analysis on Bitcoin, Ethereum, Solana, and more, with our $BTC Trend Tracker: And for risk management insights you won’t get anywhere else, subscribe to The Macro Show:

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13,872 Aufrufe • vor 9 Monaten

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Volatility is a sign of high energy, and #Bitcoin is the solution to everyone’s problems.

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The spread between #bitcoin and #ether’s annualized 30-day historical volatility has increased to the highest in at least a year, @kaikodata says. More on "Chart of the Day", presented by Crypto.com:

CoinDesk

25,213 Aufrufe • vor 2 Jahren

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0:43

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SentimenTrader

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A Nobel Prize-winning volatility model was turned into a trading filter with a reported 72% hit rate. The original framework came from economist Robert Engle. The new version answers one question before a trader risks any money: “Will tomorrow have enough price movement to trade?” On the example shown, GARCH predicted a true range near $306. The market realized roughly $311. The model takes true range, adjusts for its skew, removes the trailing average, and forecasts the remaining volatility. Then it compresses everything into a binary state: green chart = conditions are active normal chart = stay out That is closer to how professional risk systems think. They do not begin by searching for an entry. They first ask whether the current market regime is compatible with the strategy. A breakout strategy needs expansion. Without it, the setup can look perfect and still go nowhere. Most indicators tell you when to trade. This one may be more valuable because it tells you when the opportunity is not there.
2:34

A Nobel Prize-winning volatility model was turned into a trading filter with a reported 72% hit rate. The original framework came from economist Robert Engle. The new version answers one question before a trader risks any money: “Will tomorrow have enough price movement to trade?” On the example shown, GARCH predicted a true range near $306. The market realized roughly $311. The model takes true range, adjusts for its skew, removes the trailing average, and forecasts the remaining volatility. Then it compresses everything into a binary state: green chart = conditions are active normal chart = stay out That is closer to how professional risk systems think. They do not begin by searching for an entry. They first ask whether the current market regime is compatible with the strategy. A breakout strategy needs expansion. Without it, the setup can look perfect and still go nowhere. Most indicators tell you when to trade. This one may be more valuable because it tells you when the opportunity is not there.

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