# Tag Info

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This post is Quant Stack Exchange's master list of data sources. Please append your links to other data sources to the list below. Economic Data See What are the most useful sources of economics data? on Cross Validated. World OECD.StatExtracts includes data and metadata for OECD countries and selected non-member economies. United Kingdom ...

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I'm only aware about 3 free data sources: EuroNext. Bonds and Equities are available. "Search by Criteria" -> select instrument -> "Data downloads". RBS Databank. Interest rates, FX rate, commodities and CPI GAIN Capital. It contains infomation about FX rates only

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I don't know how interested you are in the CME data, but I have been learning about options and volatility modeling. I have been working with delayed CME data. I have been able to extract the JSON queries and now have been able to run them in my .NET application to get data for every asset type. Exmaple of ES options data: Run the query below in Chrome ...

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-- (historical) stock prices -- What do you mean by that? Nominal, real, corrected due to monetary-base-change, corrections with Y-other-things? What is your goal? I have been able to download (historical) stock prices via yahoo and google. Alas looking historical data from Google/Yahoo's screeners can be highly misleading and making conclusion ...

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Academic access to Thomson Reuters Tick History: www.sirca.org.au The Thomson Reuters Tick History database provides millisecond-timestamped tick data going back to January 1996, covering 45 million OTC and exchange-traded instruments worldwide. The database currently updates at a rate of 1 million messages per second and is around 3 Petabytes ...

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This is the canonical Arrow-Pratt "portfolio" model. Couple of points on terminology: For a function $u$, we define the risk aversion function by $r_u(x):=-\frac{u''(x)}{u'(x)}$. In your utility function, $r_u(x) = \lambda$; hence, it is a constant absolute risk aversion utility and $\lambda$ is the "coefficient of risk aversion," not the "risk ...

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quandl is a new data source for all kind of econometric time series.

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I basically agree with @John, let me expand: We want to model $y$ using a simple linear model, the most basic setup is $$y = c + \mathbf{X}\beta$$ with $y$ the $N$ observations, $c$ a constant, $\mathbf{X}$ the $N \times M$ matrix of regressors and $\beta$ a $M$-dimensional vector of coefficients. This model has $M$ parameters, the elements of $\beta$. ...

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To get a consolidated feed of most of the data feets here use Quandl. This is free for limited amount of requests per day.

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The current jump in price is because one or more suppliers are dropping out. That shifts the supply curve to the left such that for the same demand for oil, the new price is now further up the steeper part of the curve (see the plots below). The demand for oil will react to that new price, but it takes time. Sorry that I don't have a more recent supply ...

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Somewhat more economic data can be found at e.g.: The World Bank The United Nations The OECD More financial: The IMF European Union / EFTA / EMU data: Eurostat European Central Bank (financial) Data from these sources is all freely available. You can also play with data from many of these sources using the Google Public Data Explorer.

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This occurs because the price elasticity of demand for oil is near zero, which is to say the demand curve is nearly vertical. This is partly a limited rate of production story (i.e., in the short term the rate of oil production is fixed), but it's mostly a limited substitutes story. Our cars run on gas, as do the trucks that move goods around the nation and ...

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This looks like a general equilibrium model in Economics. It should be described in most of microeconomics textbooks (e.g. this). Yes, you need a budget constraint here for$\ a$, otherwise your optimization problem makes no sense. Moreover, the household prefers consumption today to consumption tomorrow and, hence, you may want to enhance your model by ...

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That's the problem with China. The official data is nonsense, and the estimates of outsiders can change without warning. Here are some links: China Official Stats 1 China Official Stats 2 China Official Stats 3 More China Stats OECD CIA World Bank 1 World Bank 2 IMF CMF Changing Stats 1 Changing Stats 2 Changing Stats 3 - Copper Changing ...

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There is not a single 'interest-rate' to reduce, there are various interest rates in play. The central bank mandate is usually to control CPI or a similar measure of inflation (e.g. Bank of England's 2% inflation target for GBP). There are various tools for them to do this, including QE and setting the central bank rate. However, at the moment, the central ...

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What you are talking about is called regression using fractional polynomials and it has its merits. The canonical reference is this one: Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling by Royston and Altman (1994) From the abstract: The relationship between a response variable and one or more ...

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demand surely decreases? Maybe this is not true because the high demand for oil? Demand for oil, like grains, is very inelastic. People have a very hard time changing their energy consumption habits when price shocks happen. Some of this is unwillingness to change, but a lot is that there are very few alternatives. Most people in the US live too far ...

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First, your statement that your utility function goes to infinity is wrong. It's minus exponenta. You can think of it as a minimum of $e^{f(x)}$ which is bounded below by zero whatever $f(x)$ is. In other words, your utility function is bounded above by 0. Second, maximizing expected value, you need to calculate it before deploying maximization techniques. ...

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I did a fair amount of searching for a good source of historical data and I came across Norgate Investor Services. They provide the data in MetaStock format. I used the data for analysis in MATLAB via Metastockread. They have data for the US, Australia and Singapore.

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Volatility changes over time. Even if daily returns are normal, assuming the conditional volatility each day is known, the unconditional distribution of daily returns will have excess kurtosis. For example, if daily returns have a standard deviation of 1%, 90% of the time, and a standard deviation of 3%, 10% of the time, the presence of the high-volatility ...

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In the dot.com era the Internet was considered a-winner-takes-it-all market, new tech start-ups (like Netscape, Amazon.com and the famous Pets.com) was measured by how much the capital they where able to chew through, the logic being that the more they spend the more aggressive they were (at least in the investors' eyes), conquering this new market known as ...

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I would argue that indeed none of the so-called stylized facts you mentioned can be explained by classical economic theory. That there was a gross delta between the predictions of classical economic theory and empirical data was foremost found out by Benoit Mandelbrot as far back as 1963 in his seminal paper: The Variation of Certain Speculative Prices In ...

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The general effect of quantitative analysis of the markets is to enforce randomness. Suppose a strategic quant finds a predictable pattern where a stock always rises on Tuesdays. His institution will commence buying the stock every Monday, and selling on Tuesday. The trading itself pushes the stock price up on Monday and down on Tuesday (in general), so if ...

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Put it simply, the interest rate depends on the forces of demand and supply of money. When the Fed buy bond, it increases the money supply into the economy. To induce the people to borrow more money bank reduces their own interest rate, otherwise, people won't have any incentives to borrow more. The interest rate is reduce to such level again equilibrium is ...

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Futures and Forex: http://www.tradingblox.com/?page_id=218 Indicies, Forex, Futures: http://pitrading.com/free_market_data.htm Commodities, Forex, Stocks, Interest Rates, Mutual Funds, Hedge Funds and more: http://www.wikiposit.com

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www.historicaloptiondata.com for CBOE options data stretching back 10 years (EOD only). They also have an FTP service which allows you to download EOD option data on a daily basis after market close.

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I am looking for dates of FOMC meetings. Anybody knows where I could find a list of historical FOMC meeting dates going back to at least 1982? The best I could find is in tables of this article: http://www.rose-hulman.edu/~bremmer/professional/fed_target_paper.htm and of course here http://www.federalreserve.gov/monetarypolicy/fomchistorical2005.htm but ...

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Miscellaneous data, extending back hundreds of years in some cases, is available from Global Financial Data

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Here is the full math proof. Let g be the GMV portfolio and p be another asset. We have:  \begin{align*} Cov(x_g, x_p) &= E[{w_g}^T (x- \overline{x}) {(x- \overline{x})}^Tw_p]\\ &= {w_g}^TE[(x- \overline{x}) {(x- \overline{x})}^T]w_p\\ &= {w_g}^T\Sigma w_p \\ &= (\displaystyle\frac{{i}^T {\Sigma}^{-1}}{C})\Sigma w_p\\ &= ...

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There are several different ways you could formulate this problem in game theoretic terms. Hoping this is not too basic an answer for you : from what you write, the two canonical approaches would be to frame things in terms of Cournot oligopolies (firms simultaneously set quantities and prices result from the market clearing condition supply=demand) or ...

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