Forex Books - page 11

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Forecasting the Yield Curve with S-Plus

"Forecasting the Yield Curve with S-Plus" - Dario Cziráky, PhD

Methods capable of forecasting the entire yield curve based on a time series extension of the Nelson-Siegel model Nelson and Siegel (1987) were suggested in the literature and compared to the non-parametric alternatives Diebold and Li (2004). While relatively successful in forecasting the yield curve, the Nelson-Siegel model tends to have poor fit to highly nonlinear yield curves and at the long end of the term structure, although this can be improved by considering the Svensson (1994) model.

However, we find that Nelson-Siegel and Svensson models have poor forecasting performance around the points of non-parallel shifts, hence making them potentially problematic in interest rate risk management.

In this paper we show how to implement these models using non-linear least squares and how to obtain standard errors and confidence intervals for the parameters, which proves to be useful in assessing the goodness-of-fit at specific points in the term structure, such as at the events of non-parallel shifts.

Furthermore, we consider an alternative model based on principal components and smoothing splines, which gives improved forecasting performance, particularly for the highly non-linear changes in the term structure curvature.
 

What do we know about high-frequency trading?

"What do we know about high-frequency trading?" - Charles M. Jones

This paper reviews recent theoretical and empirical research on high-frequency trading (HFT). Economic theory identifies several ways that HFT could affect liquidity. The main positive is that HFT can intermediate trades at lower cost. However, HFT speed could disadvantage other investors, and the resulting adverse selection could reduce market quality.

Over the past decade, HFT has increased sharply, and liquidity has steadily improved. But correlation is not necessarily causation. Empirically, the challenge is to measure the incremental effect of HFT beyond other changes in equity markets. The best papers for this purpose isolate market structure changes that facilitate HFT. Virtually every time a market structure change results in more HFT, liquidity and market quality have improved because liquidity suppliers are better able to adjust their quotes in response to new information.

Does HFT make markets more fragile? In the May 6, 2010 Flash Crash, for example, HFT initially stabilized prices but were eventually overwhelmed, and in liquidating their positions, HFT exacerbated the downturn. This appears to be a generic feature of equity markets: similar events have occurred in manual markets, even with affirmative market-maker obligations. Well-crafted individual stock price limits and trading halts have been introduced since. Similarly, kill switches are a sensible response to the Knight trading episode.
 

Moore’s Law vs. Murphy’s Law - Algorithmic Trading and Its Discontents

And one more (for the weekend reading )

"Moore’s Law vs. Murphy’s Law - Algorithmic Trading and Its Discontents" - Andrei A. Kirilenko and Andrew W. Lo

Financial markets have undergone a remarkable transformation over the past two decades due to advances in technology. These advances include faster and cheaper computers, greater connectivity among market participants, and perhaps most important of all, more sophisticated trading algorithms. The benefits of such financial technology are evident: lower transactions costs, faster executions, and greater volume of trades. However, like any technology, trading technology has unintended consequences. In this paper, we review key innovations in trading technology starting with portfolio optimization in the 1950s and ending with high-frequency trading in the late 2000s, as well as opportunities, challenges, and economic incentives that accompanied these developments. We also discuss potential threats to financial stability created or facilitated by algorithmic trading and propose “Financial Regulation 2.0,” a set of design principles for bringing the current financial regulatory framework into the Digital Age.
 

News Trading and Speed

One more for the holidays (and weekend) :

"News Trading and Speed" - Thierry Foucaulty,Johan Hombertz,Ioanid Ros

Informed trading can take two forms: (i) trading on more accurate information or (ii) trading on public information faster than other investors. The latter is increasingly important due to technological advances. To disentangle the effects of accuracy and speed, we derive the optimal dynamic trading strategy of an informed investor when he reacts to news (i) at the same speed or (ii) faster than other market participants, holding information precision constant. With a speed advantage, the informed investor's order flow is much more volatile, accounts for a much bigger fraction of trading volume, and forecasts very short run price changes. We use the model to analyze the effects of high frequency traders on news (HFTNs) on liquidity, volatility, price discovery and provide empirical predictions about the determinants of their activity.
Files:
news_trading_and_speed.pdf  575 kb
 

"Noisy Prices and Inference Regarding Returns" - ELENA ASPAROUHOVA, HENDRIK BESSEMBINDER, and IVALINA KALCHEVA

"Temporary deviations of trade prices from fundamental values impart bias to estimates of mean returns to individual securities, to differences in mean returns across portfolios, and to parameters estimated in return regressions. We consider a number of corrections, and show them to be effective under reasonable assumptions. In an application to the Center for Research in Security Prices monthly returns, the corrections indicate significant biases in uncorrected return premium estimates associated with an array of firm characteristics. The bias can be large in economic terms, for example, equal to 50% or more of the corrected estimate for firm size and share price."

 

How to Maximize Java Application Performance in a Trading Environment

How to Maximize Java Application Performance in a Trading Environment - an interview with Scott Sellers

In the world of latency-sensitive trading and HFT, we often focus on how performance can be improved at the hardware layer, for example via hardware FPGA/GPU acceleration or high-speed networking components. But what about the software stack?
 

The Future of Computer Trading in Financial Markets

The Future of Computer Trading in Financial Markets - An International Perspective

This Project has two principal aims. First, looking out to 2022, it seeks to determine how computerbased trading in financial markets could evolve and, by developing a robust understanding of its effects, to identify potential risks and opportunities that this could present, notably in terms of financial stability1 but also in terms of other market outcomes such as volatility2, liquidity3, price efficiency and price discovery4. Secondly, drawing upon the best available scientific and other evidence, the Project aims to provide advice to policy makers, regulators and legislators on the options for addressing those risks and opportunities

Future of-computer trading in financial markets report.pdf

 

The Financial Modelers' Manifesto

"The Financial Modelers' Manifesto" by Emanuel Derman and Paul Wilmott

In finance we study how to manage funds – from simple securities like dollars and yen, stocks and bonds to complex ones like futures and options, subprime CDOs and credit default swaps. We build financial models to

estimate the fair value of securities, to estimate their risks and to show how those risks can be controlled. How can

a model tell you the value of a security? And how did these models fail so badly in the case of the subprime CDO

market?
 

Liquidity Cycles and Make/Take Fees in Electronic Markets

 

Does this problem have a solution?

"Does this problem have a solution?" - Rudi Bogni

Managing society requires the world to embrace randomness. Fat chance!
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