Unemployed Guy Explains Option Trading in 5 Levels of Complexity

Source: Unemployed Guy Explains Option Trading in 5 Levels of Complexity, Benjamin, 17:02, uploaded 2022-05-02, Watch Later position 777.

Benjamin opens by describing himself as an unemployed financial professional on YouTube with no certification, training, or formal education. That qualification sets the tone for a five-level explanation of option trading. The joke also points towards a serious concern: apps such as Robinhood put options in front of retail traders through a game-like interface with little vetting. Benjamin leaves the judgement to the viewer, then starts with drawings aimed at a child of about fifth-grade age.

Calls, puts, and the appeal of leverage

The Investopedia definition says that an option gives its buyer the opportunity to buy a certain number of shares at a given price. Benjamin treats that definition as a starting point with little explanatory value. For a retail trader, a call is a directional bet that the share price will rise, whilst a put is a bet that it will fall.

The attraction lies in leverage. One option contract controls 100 shares of the underlying stock, so buying a call creates exposure to the returns and losses of 100 shares for a fraction of the cost of buying those shares. The same arrangement amplifies losses as well as gains. Benjamin calls this a complete oversimplification, since options carry several different risk exposures, yet it gives the viewer a usable first model.

He then moves from speculation to the use that gives options their place in finance. A holder of QQQ, an exchange-traded fund that tracks the Nasdaq, could buy a QQQ put as insurance against a fall. If QQQ declines, the shares lose value whilst the put gains value and offsets part of the loss. Benjamin tells viewers not to use this simplified example as a trade. The point is that an option can transfer risk to another party for a fee.

The distinction resembles the schoolroom model of the atom that places electrons in orbit around protons and neutrons. The model is wrong as a description of physics, yet it helps a student form a first picture. Benjamin uses the same kind of picture for options before adding the market mechanics that determine their price.

The option market and the search for mispricing

Options in a trading app come from an option market, so their prices emerge from buyers and sellers processing information and negotiating around their expectations. Benjamin lists terms such as delta, gamma, theta, the Black-Scholes model, and implied volatility as ways to describe option prices and how those prices change. His comic formula for a Robinhood portfolio subtracts infinity from an account balance. The joke carries a practical warning about how quickly leverage can destroy capital.

For the next level, Benjamin asks the viewer to assume that most market participants are rational and price-sensitive. If option prices are fair, strategies such as iron condors, straddles, covered calls, and cash-secured puts have no built-in source of long-term profit. Trading costs make the result worse. A covered call can still reduce the volatility of a stock position, yet the structure itself creates no edge when its inputs are fairly priced.

The useful question becomes whether the trader is paying a fair price for the probability offered by the market. Benjamin imagines Apple. A retail trader may buy calls because Apple will probably release another iPhone and its share price may rise. A trader working at the next level asks what probability the option market has already assigned to that outcome. If the market implies a 10 per cent chance that Apple will rise by 5 per cent over the next 90 days and the trader estimates a 20 per cent chance, the call may be cheap. If both estimates are 10 per cent, the trade gives away money through the option price, commission, and slippage.

Benjamin points to the probability-analysis tool in thinkorswim as a way to inspect those implied outcomes. The video gives the example as a model of reasoning, not as a current Apple forecast or a tested trading method. Its most useful change is grammatical: the trader moves from “the share price will rise” to “the market has priced a probability, and my estimate differs from it.”

Implied volatility and realised movement

The video uses GameStop to explain why “sell high implied volatility and buy low implied volatility” is too crude. A high implied volatility can describe an expensive option, although its absolute level tells us little on its own. Benjamin compares implied volatility with the volatility that the stock later realises. If a trader sells an option and hedges away its directional exposure, the result depends on the volatility implied by the option price and the volatility that follows.

Two points with similarly high implied volatility can therefore carry different prices. One can sit close to the stock’s later realised volatility, whilst another can imply far more movement than the stock produces. Benjamin says the gap between implied and realised volatility looked especially large in GameStop options during September 2020. That example illustrates a relative-value comparison. It does not establish that every high-volatility option is overpriced.

Volatility means the annualised standard deviation of log returns. Benjamin turns a 30-day GameStop implied volatility of 400 per cent into a daily estimate by dividing by the square root of roughly 256 trading days, which he rounds to 16. The result suggests an average daily move of 25 per cent during the option’s life. A one-year implied volatility of 212 per cent would suggest a daily move of about 13 per cent. Benjamin’s point is that option prices can imply extraordinary outcomes when demand pushes the price higher.

The Black-Scholes relationship explains why implied volatility is an estimate extracted from a price. The model uses an option price as one of its inputs, and implied volatility is the output that makes the formula match that price. When many traders buy calls, the price can rise because of demand even when a 400 per cent volatility forecast has little basis in a realistic view of the stock. GameStop becomes an exaggerated case of a smaller effect that appears throughout the market.

The distinction also changes how a trader interprets people who bought GameStop puts around its peak and still lost money. A directional view can be right about a fall and still lose when the option was purchased at a price that already assumed an extreme amount of movement. The direction of the stock answers one question. The price paid for the probability answers another.

Hedging demand and the price of risk

Benjamin then gives the market a physical business to insure. Imagine an ornamental-gourd farm producing 50,000 pounds of gourds. The farmer buys puts so that a fall in the gourd price is partly offset by a rise in the value of the puts. The farmer cares about protecting the business and may accept a small overpayment for that protection. Diseases, aphids, and the ordinary misery of farming already supply enough uncertainty.

That demand raises the price of the puts. The same mechanism appears in GameStop, where retail traders bought calls because they wanted to force a short squeeze. The motives differ, yet both groups can create an imbalance through demand from participants who care more about the outcome they need than about the option’s marginal price. A seller can earn a return by supplying that insurance when the option is overpriced.

Benjamin insists that this is the foundational idea behind option structures. An iron condor or butterfly works only when the options placed inside it have favourable prices. The name of the structure carries little weight by itself. Research papers and relative-value analysis can help identify cheap and expensive options, whilst implied volatility supplies the language for comparing them.

The linked book Trading Volatility develops this institutional version of the argument. Colin Bennett describes implied volatility as an estimate that tends to sit above future realised volatility, with the difference shaped by the equity risk premium, demand for downside protection, and the costs faced by market makers. The book also warns that historical profitability and backtests provide weak evidence for future returns when the market has absorbed a strategy. Benjamin’s explanation is shorter and funnier, yet the shared mechanism is clear: hedging demand can make risk expensive, and a trade depends on the price of that risk.

The missing fifth level

Benjamin admits that he does not know what level-five option knowledge contains. He draws a graph comparing time spent learning a skill with its usefulness. Video editing follows a more familiar rise in value, whilst trading demands thousands of hours with little tangible result in his sketch. The drawing changes the tone of the explanation because the subject turns back on the teacher. The deeper the model becomes, the harder it is to show that the skill produces a useful life.

His closing satire pushes that doubt into an account of meaning. Volatility models and complex option dynamics can teach lessons about finance and economics, yet Benjamin treats the pursuit of money as an empty end when it becomes detached from any other reason for living. The video ends with the deliberately bleak claim that he chose this path and turned out great. Level five therefore remains unresolved. The viewer receives a way to think about pricing, probability, hedging demand, and realised volatility, along with a warning about the distance between understanding a market and earning money from it.

Limits

The video is a comic introduction to options, not a financial study or trading recommendation. Benjamin’s figures for GameStop’s implied volatility, the examples involving Apple and ornamental gourds, and the claim that retail traders had little formal vetting remain attached to the source. The captions contain a few garbled words and omitted profanities. The video also promotes Predicting Alpha and mentions an unnamed paper about selling options around earnings; those promotional details do not supply evidence for the argument and are left out here.

The description links Hedging Pressure and Commodity Option Prices by Ing-Haw Cheng, Ke Tang, and Lei Yan. The SSRN page was blocked by Cloudflare during acquisition, so I have kept the paper as a source lead without treating its results as independently checked evidence. The accessible Trading Volatility PDF supports the distinction between implied and realised volatility and the role of hedging demand, whilst it also cautions against reading historical backtests as forecasts.

The examples explain why an option can be directionally right and still lose money. They do not show how to estimate probabilities, hedge a live position, account for margin, or decide whether a price is fair. Those tasks require evidence and risk controls that the video’s five levels intentionally leave out.

Further reading / references

• Trading Volatility: Trading Volatility, Correlation, Term Structure and Skew, Colin Bennett.
• Hedging Pressure and Commodity Option Prices, Ing-Haw Cheng, Ke Tang, and Lei Yan.