MetaQuotes:
Thank you very much for sharing this material! I’m reading through it and getting to grips with the trades, structures and code – it’s very interesting!!!
An article has been published entitled ‘Trading Options Without Options (Part 4): More Advanced Options Strategies’:
Author: Dmitriy Skub
Roman Shiredchenko #:
Thank you very much for sharing this material! I’m reading through it and getting to grips with the bidding process, the designs and the code – it’s really interesting!!!
Thank you very much for sharing this material! I’m reading through it and getting to grips with the bidding process, the designs and the code – it’s really interesting!!!
Have a read and enjoy. If you have any questions, do get in touch.
The next article will be (among other things) about trading synthetic underlying assets.
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Check out the new article: Trading Options Without Options (Part 4): More Complex Option Strategies.
The theory of options trading is based on the Black–Scholes formula, which, in turn, is based on the assumption that the movement of the underlying asset follows a random process with a normal distribution of price increments. This assumption is clearly refuted by empirical evidence (see Fig. 1), which means that the theory as a whole is incorrect.
For comparison, Fig. 1 shows distribution curves of price changes for a normal distribution (Normal distribution — dark blue line) and the empirical average distribution with “fat tails” (“Fat tail” distribution — dark green line) for an arbitrary underlying asset. There is a fundamental difference between them — mainly in the region of large price changes. The y-axis shows the frequency of occurrence of the corresponding price increments within the interval specified on the x-axis.
In addition, market data exhibits fractal properties (self-similarity). This means that the charts for a day, a week, or a month are structurally similar. This property clearly does not fit within the framework of a simple random walk.
Finally, there is a phenomenon known as historical volatility clustering. This phenomenon cannot be described by the distribution over a single time interval alone, but it is important for understanding the dynamics. Historical volatility is not constant: periods of high volatility tend to cluster together. The same is true of periods of low volatility. A strong move is usually followed by a series of other strong moves. This completely contradicts the hypothesis that asset price increments are independent.
Author: Dmitriy Skub