Reddit reviews Stochastic Calculus for Finance II: Continuous-Time Models (Springer Finance)
We found 3 Reddit comments about Stochastic Calculus for Finance II: Continuous-Time Models (Springer Finance). Here are the top ones, ranked by their Reddit score.
Springer
Some light reading to get you started on your way to be a quant:
Stochastic Calculus for Finance: The Binomial Asset Pricing Model
Stochastic Calculus for Finance: Continuous-Time Models
Stochastic Implied Volatility: A Factor-Based Model
Methods of Mathematical Finance (Stochastic Modelling and Applied Probability)
Pairs Trading: Quantitative Methods and Analysis
Alpha Trading: Profitable Strategies That Remove Directional Risk
The Encyclopedia of Trading Strategies
Evidence-Based Technical Analysis
>despite being 18, i have landed several jobs in the finance sector which give me valuable experience to become a quant ...
What's your IQ? When you look through Steven Shreve's, Stochastic Calculus for Finance II: Continuous-Time Models, do you understand it?
I think this question is a great example of why probability theory requires so much measure theory.
The probability of the finite sequence (H,H,H, ..., H) approaches zero as you increase the size of the sequence to infinity. The probability that the sequence does not occur is said to be "Almost Surely".
But this statement holds for any sequence you write down. Any specific sequence of heads and tails does not occur with probability "Almost surely".
Yet the space of possible outcomes is a probability space, so we require the sum of probabilities of all outcomes to be: Prob({space of all outcomes}) = 1.
This has implications on the construction of the probability space associated with these "experiments" of infinite coin tosses. This is very related to measure theory, where the set [0,1] has the same measure (length) as the set (0,1).
The book Shreve - Stochastic Calculus for Finance II: Continuous-Time Models happens to start off with precisely this example to explain the idea of a probability space, without going too deep into measure theory and all that.