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2.07 Clearing Rules Cash Market
2.07 Clearing Rules Cash Market

... partner for one of these transactions. (3) Should an exchange member not be authorized to participate in clearing (non-clearing member), said exchange member's trades shall only be executed according to the detailed provisions of the Business Terms of CCP.A by the general clearing member with whom t ...
World Financial Markets, 1900-1925
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... I present a new dataset that describes the financial markets of the early twentieth century. Historical data have proven useful to better understand how financial markets operate. Estimation of the equity-premium (e.g. Goetzmann and Ibbotson (2006)), the efficiency of derivatives markets (Moore and J ...
Value-at-Risk (VaR)
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... of the correlations between asset returns. In the following section we give a highly-simplified example of the estimation of return distributions from market data. For example you can imagine that a long position in Deutschmarks and a short position in Dutch guldens is less risky than one leg only, ...
Margin requirements with intraday dynamics
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... order to compute the margin requirement that adequately protects against default at various probability levels and/or determine the probabilities associated with different margin requirements. Figlewski (1984) and Gay et al (1986) classically assume that futures price movements follow a Gaussian dis ...
The IOSCO Transparency Principle and Modelling the Bid
The IOSCO Transparency Principle and Modelling the Bid

... of the trading parties is obscured in this information, but all other information about yield, volume, settlement date and transaction type is provided. In addition, there are numerous press and data feeds that provide access to historical prices and volumes for the various bonds. This means that fo ...
B04 Chapter 04 Japanese Candlesticks 1.
B04 Chapter 04 Japanese Candlesticks 1.

... Out of a universe of dozens of candlestick patterns, it has been found that a small group of them provide more trade opportunities than most traders will be able to utilize. In this section, 12 patterns are dissected and studied, with the intention to offer you enough insight into a fascinating way ...
Final notice: JPMorgan Chase Bank NA
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... A firm legitimately managing the risk arising from its net client orders at the fix rate may make a profit or loss from its associated trading in the market. Such trading can, however, potentially influence the fix rate. For example, a firm buying a large volume of currency in the market just before ...
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ANALYSIS OF FACTORS AFFECTING THE DEVELOPMENT OF AN

... emphasized. Most economic managers recognize that a well organized capital market is crucial for mobilizing both domestic and international capital. In many developing countries, however, capital has been a major constraint in economic development. Dailami and Atkin (1990) describe the provision of ...
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Financial Market Infrastructure Ordinance

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Liquidity and the Law of One Price: The Case of the Futures/Cash

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Chapter IV: How Securities are Traded? etImUlbRtRtUv

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... We find that, during our sample period, FTDs affect about 95% of NYSE securities. For this NYSE sample, we conclude that an increase in FTDs equivalent to 0.1% of the number of outstanding shares leads to a 3% reduction in the magnitude of positive and absolute pricing errors; a 0.2% decline in intr ...
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Natural and manmade disasters can be powerful leaning experiences

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Slide 1

... US Treasury Bills). The S&P data are provided by Standard & Poor's Index Services Group. Russell data copyright © Russell Investment Group 1995–2012, all rights reserved. MSCI data copyright MSCI 2012, all rights reserved. Dow Jones data (formerly Dow Jones Wilshire) provided by Dow Jones Indexes. B ...
CAPM in Market Overreaction Conditions: Evidence in Indonesia
CAPM in Market Overreaction Conditions: Evidence in Indonesia

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Security Futures

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ch05 - U of L Class Index
ch05 - U of L Class Index

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Algorithmic trading

Algorithmic trading, also called algo trading and blackbox trading, encompasses trading systems that are heavily reliant on complex mathematical formulas and high-speed, computer programs to determine trading strategies. These strategies use electronic platforms to enter trading orders with an algorithm which executes pre-programmed trading instructions accounting for a variety of variables such as timing, price, and volume. Algorithmic trading is widely used by investment banks, pension funds, mutual funds, and other buy-side (investor-driven) institutional traders, to divide large trades into several smaller trades to manage market impact and risk.Algorithmic trading may be used in any investment strategy or trading strategy, including market making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically.Many types of algorithmic or automated trading activities can be described as high-frequency trading (HFT), which is a specialized form of algorithmic trading characterized by high turnover and high order-to-trade ratios. As a result, in February 2012, the Commodity Futures Trading Commission (CFTC) formed a special working group that included academics and industry experts to advise the CFTC on how best to define HFT. HFT strategies utilize computers that make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe. Algorithmic trading and HFT have resulted in a dramatic change of the market microstructure, particularly in the way liquidity is provided.Profitability projections by the TABB Group, a financial services industry research firm, for the US equities HFT industry were US$1.3 billion before expenses for 2014, significantly down on the maximum of US$21 billion that the 300 securities firms and hedge funds that then specialized in this type of trading took in profits in 2008, which the authors had then called ""relatively small"" and ""surprisingly modest"" when compared to the market's overall trading volume. In March 2014, Virtu Financial, a high-frequency trading firm, reported that during five years the firm as a whole was profitable on 1,277 out of 1,278 trading days, losing money just one day, empirically demonstrating the law of large numbers benefit of trading thousands to millions of tiny, low-risk and low-edge trades every trading day.A third of all European Union and United States stock trades in 2006 were driven by automatic programs, or algorithms. As of 2009, studies suggested HFT firms accounted for 60-73% of all US equity trading volume, with that number falling to approximately 50% in 2012. In 2006, at the London Stock Exchange, over 40% of all orders were entered by algorithmic traders, with 60% predicted for 2007. American markets and European markets generally have a higher proportion of algorithmic trades than other markets, and estimates for 2008 range as high as an 80% proportion in some markets. Foreign exchange markets also have active algorithmic trading (about 25% of orders in 2006). Futures markets are considered fairly easy to integrate into algorithmic trading, with about 20% of options volume expected to be computer-generated by 2010. Bond markets are moving toward more access to algorithmic traders.Algorithmic trading and HFT have been the subject of much public debate since the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission said in reports that an algorithmic trade entered by a mutual fund company triggered a wave of selling that led to the 2010 Flash Crash. The same reports found HFT strategies may have contributed to subsequent volatility by rapidly pulling liquidity from the market. As a result of these events, the Dow Jones Industrial Average suffered its second largest intraday point swing ever to that date, though prices quickly recovered. (See List of largest daily changes in the Dow Jones Industrial Average.) A July, 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while ""algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010."" However, other researchers have reached a different conclusion. One 2010 study found that HFT did not significantly alter trading inventory during the Flash Crash. Some algorithmic trading ahead of index fund rebalancing transfers profits from investors.
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