# Tag Info

9

The volatility in the indices long ago was similar in magnitude to what it is today. The problem you are seeing in your plots is one of compounding and scaling. Think of it this way- back in the mid 70's the magnitude of NASDAQ pricing was around \$100. Today it is on the order of \$4000, a change of 40x. In linear terms, a 1% change in the index today ...

4

(P) prefix : As a service to the market and typically at the request of an issuer, Moody's will assign a provisional rating when it is highly likely that the rating will become final after all documents are received, or an obligation is issued into the market. A provisional rating is denoted by placing a (P) in front of the rating. Such ratings may also be ...

4

The data has definitely not disappeared, it's a problem with your vendor. There has been a corporate action on 2014-02-27 and hence the strike prices have been adapted accordingly. According to Bloomberg bsym your P69 (composite ID BBG004L7P7L6) became P68.63, and P70 (BBG004L7P8C4) became P69.63.

3

This is an interesting topic. I assumed that you are looking for a public data source. Here is the margin data as reported by NYSE organizations (nyxdata) that offers a downloadable file. Here is the page of FINRA for Margin Statistics. This is an HTML page, I did not find a link to download a data file. You can validate the two sources against each ...

3

As you've mentioned, it depends on the trading venue and the exact market data product that you're subscribed to. Unless otherwise stated, the data is usually updated at every occurrence of an event (explains the irregualr intervals), and often, the data is not disseminated immediately and multiple events may be batched in a single message informing you of ...

3

The stock was split into two share classes, the series that you might be looking for is under the ticker GOOGL.

3

From a note of P. Krugman (link): So no it is not. Why ? I would say 3 cause: First: Dynamics, saving rates are longterm figures. Offer and demand would be different for these products. Some time there is a lack of liquidity and a need of financement, so a huge demand in short term bonds. Second: bank margin, reserve policies, they have to earn some ...

3

In effect, you are wondering whether to price this option on risk-free probability distributions (B-S drift $r_f$), or real-world ones (B-S drift $\mu$, however calibrated) One cannot short the mutual fund, so the argument for using risk-free is weakened. But, there are various economic equilibrium arguments why using it may still be OK. If you use the ...

2

Here is what you can definitely use: Thomson Reuters Eikon

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To construct best bid/ask from ITCH you must build a book incrementally from the messages in the data. Every message, except for system oriented messages, and non-displayed Trades, represent an order or an action on an order. Process the data, build a book, and you will naturally be left with the best bid/ask at the top of each side.

2

PX_BID and PX_ASK are the static equivalents of BID and ASK, the latter two of which populate in "real time" (i.e. as they are dynamically updated). So the PX_BID and PX_ASK values are dependent upon when you pulled the data. Bloomberg's source depends on the asset in question and the exchange on which they are listed, but the data does come from the ...

2

There are actually a lot of options nowadays. Adjusting your data using historical realized inflation is certainly one way to go. And as @User1996 mentioned, the CPI for All Urban Consumers is the frequently quoted "headline" number. However, to the extent that asset prices reflect inflation expectations, it might be better to use forward-looking ...

2

The U.S. Consumer Price Index For All Urban Consumers (http://research.stlouisfed.org/fred2/series/CPIAUCSL) is the CPI you hear in the news, and is the standard inflation number.

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Most hedges funds only allow monthly subscriptions and redemptions; which means they will only publish official prices on a monthly basis. If someone does publish daily data view it with suspicion. Having said that HFRX publish numbers on a daily basis.

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As a short summary and adaption of the question: You better redefine $\hat{r}_i= \frac{S_{i-1}}{S_1}-1$ and $\hat{S}_i = (1+\hat{r}_i)S_0$. The above definition of $\hat{S}_i$ yields a sample of potential values for $S$ for the future day. This approach is usually applied in historical simulation. The aim here is to use information of the past about the ...

2

I found US data here. While this data doesn't include correlations, these can be calculated relatively easily.

2

One possible route would be to get the historical price data from Yahoo and use disparities between the Close returns and the Adjusted Close returns, given a certain threshold value, and where the disparity passes that threshold is where the splits have occurred. Find the Close returns where that condition was met, maybe round the number to the nearest ...

2

Unfortunately I don't think it's possible to compute returns purely based on yields... There are a few options: If you're on the buy side, you can easily get access to Barclay, Citi, or BofA's bond indices. These are very high quality datasets for studying historical bond returns. If you have Bloomberg, they've started providing bond indices as well. They ...

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All option pricing formulas except this one and this one use some sort of historical volatility . I can't see how you can use the Black Sholes framework and not use some sort of historical volatility uses an order book uses geometric shapes and volume

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If you want to estimate volatility from historical data, the only best linear unbiased estimator (BLUE) is $$\sigma=\sqrt{\frac{1}{T-1}\sum_{i=1}^T (r_i-E(r_i))^2}$$ Any other estimator will hence either be biased or not consistent. Another approach could be to estimate volatility via a GARCH model, which has shown good empirical results in the past. It is ...

2

As background, Floating point precision is a way of storing numbers such that the precision is relative to the largest digit. For instance, the number $0.00123$ stored in fixed precision needs 6 digits of precision (3 zeros and the 3 non-zero numbers). However, this same number stored as floating point precision $1.23 \cdot 10^{-3}$ needs only 3 ...

1

There's a couple of options other than Google or Yahoo that I am aware of. The NSE provides EOD data, as well as 5,2 and 1 minute data. If you're willing to pay for high quality data for your application, this is an excellent choice. http://www.nseindia.com/supra_global/content/dotex/data_products.htm Quandl provides comparatively clean, free EOD data ...

1

There is no such thing as "free" option data. This is free -->http://www.nasdaq.com/symbol/aapl/option-chain You could crawl that. But to get the actual ticks or intraday data, you will unfortunately have to pay. I strongly suggest you find a college business program that has option data ticks and reach out to them. Best of luck, JL

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Such an approach is done by the systemic investor blogger in his blog Time Series Matching with Dynamic Time Warping.

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You may get the S&P500 tickers and download the corresponding fields PEG, DivYield, Industry from Bloomberg into Excel. Then you can just sort the values in excel once by industry and then by PEG to directly see the ranks. You may use two sheets for PEG and DivYield.

1

For each major index, Bloomberg has functions that will give you the best performers for each relevant period; past week, past month, past quarter, past year, etc. I would take one of these "runs," and then re-set the start and end dates to the ones that you want.

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I don't know about free of charge, but if you're subscribed to WRDS or similar data sources, then here's how to get the historical S&P 500 constituents data

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They represent the current BID and ASK at the time you query them. If you look up those fields in the terminal FLDS<GO> you will see they are marked as reference data, that means they are not continually updated. They are refreshed each time you query them. They come from the NBBO quote at the time you query them.

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Yes definitely, the biggest challenge of using direct exchange feeds is the cost of maintenance. Here are a few issues to consider in your position: Cost of maintenance. This includes the time it takes to write a feed handler and keep it up-to-date against the exchange's feed API; the cost of colocation, and (often) higher licensing costs of receiving the ...

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Always use a semi-logarithmic scale when looking at prices. It makes percentage moves of equal heights on your graphs.

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