How Brokerages Provide Liquidity Through Order Routing and Market Making
Summary
The document asks how brokerage firms provide liquidity to customers. It contrasts two possible arrangements: brokers may submit customer limit orders directly to an exchange order book, or they may fill customer orders internally and manage the resulting exposure through a relationship with a liquidity provider or market maker. This framing introduces the distinction between agency execution and principal or internalized execution.
The text contains only the question and does not explain which model a broker uses, how orders are routed, or how any offsetting hedge is placed. In practice, liquidity provision can vary across brokers, markets, and order types, and a firm may combine internal execution with external routing or hedging. The document therefore serves as a topic prompt about brokerage execution and market structure, not a description of a particular firm's process or a complete account of liquidity provision.
Key ideas
- The document contrasts direct order-book submission with internal customer fills.
- It asks whether brokers offset accumulated customer exposure through market makers or liquidity providers.
- It does not describe a specific brokerage model or provide an answer.
- Execution arrangements may vary by broker, market, and order type.
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Full text
# Why does the presence of cointegration solve the problem of spurious correlation? # Why does the presence of cointegration solve the problem of spurious correlation? Many of us are familiar with the connection between spurious correlation and its relationship to cointegration. Granger explains in his seminal 1974 paper "Spurious Regressions in Econometrics" how hypothesis testing on two time series that are I(1) can lead to significance tests that are very biased according to experimental results (resulting in far more "significant" correlations than there should be). Later, Philips (1986) showed with functional limit theory exactly why these I(1) time series led to biased results: because in fact there is no convergent asyptotic behavior for the correlation coefficient for these series (among other relevant statistics). At this point, there seems to be a gap in my understanding. There seems to be dozens of articles online confidently proclaiming that you can "get around" the spurious correlation problem if the two time series in question are not only I(1), but also cointegrated. The explanation seems to be that since the error term is I(0), the usual assumptions of hypothesis testing for correlation are met. Of course, this makes intuitive sense, since the original assumption for the correlation significance test is that the residuals are iid normal. But saying the residuals are I(0) is not the same as saying they are iid normal. I am wondering if there is a reference that explains this relationship in more detail? Who was it that showed that regular hypothesis testing is valid when the two series are cointegrated? And is this an "if and only if", or are there other circumstances other than co-integration under which regular OLS correlation testing is still valid?
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