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My personal favourites: pricing and hedging in incomplete markets, in particular credit derivatives stochastic prediction-discount and inter-tenor basis in the interest rate market; see "Two curves, one price" (Bianchetti) and "Interest Rates and The Credit Crunch: New Formulas and Market Models " (Mercurio)


The Early History of Option Contracts Haven't read this so can't vouch for whether they get into etymology. ABSTRACT: This chapter discusses the history of option contracts from ancient times until the appearance of Theorie der Prämiengeschäfte by Vincenz Bronzin in 1908. The history examines the use of contracts with option features prior to the ...


It's more stressful than the work of a tenured professor but less stressful than the work of a postdoc.


Lets assume I made two models for predicting future price of stocks, one trained in RNN and other in MLP (Multi Layer Perceptron) using 10 years (OHLC) of data from SPY with good accuracy. Which algorithm has more chances to give an accurate prediction? The choice of which model to use for training matters far, far less than the specific parameters ...


Three examples would be spreads, butterflies, and double-butterflies. They can all have negative prices. Reverse the sign of the quantity on all the legs and you're short the synthetic. For example, the Jan-Feb calendar spread would buy 1 Jan and sell 1 Feb contract. If you wanted to be short the spread, you would sell 1 Jan and buy 1 Feb contract.


I am not confident that I understand specifically what you are asking, but I hope this helps: What are theoretical approaches to model and answer this question? This question is rather broad. I will say that in comparing a random collection of purchases and sales of securities, with only the time between the transactions as varying among different ...

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