# Tag Info

1

I've worked in algorithmic trading for years. RL (or deep RL for that matter) is not used in this industry.

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You can find a lot of good papers by just typing keywords like "deep reinforcement learning finance" in the arXiv or Google Scholar or looking at top researchers websites which provides an overflow of applications and research directions to engage with. Anyway, here are a few off the top of my head: If you are looking for a more introductory level ...

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I know this is an old question, but I encountered this problem too, years ago, and the answer is that you can actually compute confidence intervals for VaR backtesting of overlapping returns. From what I understand, banks usually derive them with heavy Monte Carlo simulations (so they generate the overlapping returns a large amount of times and look at the ...

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Let us fix the asset universe with $N$ assets whose returns are multivariate normally distributed with covariance matrix $\Sigma$. You are already invested in $K<N$ assets (your portfolio) and you wish to add other assets from that universe to your portfolio to form a hedge(d) portfolio. Let us assume that the hedge should be self-financing. Let us ...

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I see this edited & bumped so here's a modern answer. You need historical data. Good data. Convert prices to implied vols (you need dividends, rates, and a good American option pricer with cash dividends) and a good clock (ticks faster when markets are open) Convert fixed strike surfaces to fixed moneyness(or delta) and tenor Regress changes in that '...

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I am actually more interested in it from the other perspective. If we have a price shock what is the likely IV change that will affect the options pricing? If you're using Python, I would recommend the Mibian library (http://code.mibian.net/). You can simulate a price shock by increasing the volatility parameter (which is HISTORICAL volatility in this case) ...

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