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Bram made a good point about looking at longer-term returns, but that weakens the quality of estimates. Here are two sources that address the issue directly - a formal approach here and one specifically in financial context here.


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Closing prices should be very highly correlated, I assume you care about close to close returns instead. Given the frequencycle of the data that you seem to be looking at (ie you don't seem to be looking at correlation of the futures return intraday), I assume this is for some sort of modelling/pricing over a longer horizon. What I believe most people do ...


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You can try finance.yahoo for example http://finance.yahoo.com/q/hp?s=GOOG+Historical+Prices it is free, for the other ones you mentioned you have to pay, usually a lot.


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Look at matplotlib.finance It downloads data from yahoo finance as well but it is much quicker than the package that you are mentioning. Regarding the reliability, I think that the data source is quite reliable.


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From the information I've gathered the volatility smile concept did not exist prior to 1987. Since then it can be seen in foreign exchange markets and various other investments. Equity derivatives show volatility pairs and the smile tends to seen quite easily here.


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Most likely you are missing something as any new order can't bypass the existing orders. The only possibility that comes to mind is if you have anti-internalization set and the broker is trying to hit his own quotes. Say broker A has two quoting systems running and they would otherwise interact. Anti-internalization would not allow this broker to trade ...


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There's a very simple explanation. The NYSE trading floor only deals in NYSE-listed stocks. NYSE have other venues (such as Arca) that allow you to trade listings from other exchanges. The following site has a very good summary of "tape" versus the volume traded at each venue. https://www.batstrading.com/market_summary/ Note that Tape B contains trades ...



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