Forex Books - page 9

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"Are Stocks Really Less Volatile in the Long Run?" - LUBOS PASTOR and ROBERT F. STAMBAUGH

According to conventional wisdom, annualized volatility of stock returns is lower over long horizons than over short horizons, due to mean reversion induced by return predictability. In contrast, we find that stocks are substantially more volatile over long horizons from an investor’s perspective. This perspective recognizes that parameters are uncertain, even with two centuries of data, and that observable predictors imperfectly deliver the conditional expected return. Mean reversion contributes strongly to reducing long-horizon variance but is more than offset by various uncertainties faced by the investor. The same uncertainties reduce desired stock allocations of long-horizon investors contemplating target-date funds.
 

The Credit Ratings Game

"The Credit Ratings Game" - PATRICK BOLTON, XAVIER FREIXAS and JOEL SHAPIRO

The collapse of AAA-rated structured finance products in 2007 to 2008 has brought renewed attention to conflicts of interest in credit rating agencies (CRAs). We model competition among CRAs with three sources of conflicts: (1) CRAs conflict of understating risk to attract business, (2) issuers’ ability to purchase only the most favorable ratings, and (3) the trusting nature of some investor clienteles. These conflicts create two distortions. First, competition can reduce efficiency, as it facilitates ratings shopping. Second, ratings are more likely to be inflated during booms and when investors are more trusting. We also discuss efficiency-enhancing regulatory interventions.
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the_credit_ratings_game.pdf  314 kb
 

Not a book, but it will be a series of articles (according to announcement). Here is the announcement :

High Frequency Forecasting, (HFF).

Mon, 11 Feb 2013 14:14:07 GMT

(c) Christopher S Kirk

[A summary note intended as a prelim to a short series of relevant highlights]

Whilst the regulatory arguments for and against High Frequency Trading, (HFT) continue, a number of investment teams are already forging the next path forward and grasping the capability to advance above and beyond the provisions of HFT by several orders of magnitude.

This advance is more middleware than hardware, more spongeware than software...it is a group of dynamic machine learning algorithms that adapt at every change in data and live in a unresting world of constant flux.

Some years ago, the so-called Artificial Intelligence methodologies developed theorems which astounded the world and provided promise. Sadly, many of them never materialised into useful tools in the public domain yet their groundbreaking ideals provided the foundation for one of the most advanced sectors known, that of machine learning; the fundamentals and capacity which powerhouse the release of computers to be relatively free of human constraints and to learn and adapt to new horizons along the data-knowledge continuum.

These new-breed programs are free to learn, they do so at an unbelievable rate, not simply memorising events but examining (and acting upon) triggers that humans can only gasp at! A well-written system is flexible enough to develop itself and indeed within a modern algorithm, functions morph even whilst new data is being acquired in order to increase the rate of machine-awareness to real-time.

These new generation machine learning algorithms are in use today; some of the latest implementations have been developed to exploit multidisciplinary advances in electrical and mechanical engineering and in face, speech and hearing recognition and they operate as non-stop cycles of computational excellence in obscure space that are in a 'world of their own' whilst being connected actually and virtually to data-feeds.

As autonomous systems they exist in undefined states of constant learning and decision-making. These decisions when directed at the financial and capital markets can co-exist with high frequency execution systems and develop a controlled market. They do so by making forecasts not just milliseconds ahead (to seize arbitrage opportunities as in the current use of HFT systems), but to learn from trigger and event management and their resolution to evaluate risk, return, positioning and liquidity up to several seconds ahead. This foresight of vector prediction and consequential flow control enables not just a more immediate inter-asset transfer capability but also inter-market positioning to framework global money management.

On balance therefore, When HFT regulatory arguments are considered one might also maintain an awareness of new-generation, wholly dynamic computational systems which are as alive as humans yet unrestrained by temporal or spatial limitations; systems that are already providing new frameworks by developing a 'non-stop' world of High Frequency Forecasting.

It seems that it will be an interesting series of articles (highlights).

 

That will be interesting

 

Does Algorithmic Trading Improve Liquidity?

"Does Algorithmic Trading Improve Liquidity?" - TERRENCE HENDERSHOTT, CHARLES M. JONES, and ALBERT J. MENKVELD

Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change inmarket structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.
 

Does the euro area forward rate provide accurate forecasts of the short rate?

"Does the euro area forward rate provide accurate forecasts of the short rate?" - Ana Beatriz Galvaoa, Sonia Costab

The forward rate can deliver accurate forecasts of euro area short-term interest rates, depending on the time period. During periods of macroeconomic uncertainty, forecasts obtained from a model of yield and macro factors are more accurate than forward-based forecasts. We provide evidence that a time-varying forward premium explains the variation in the forecasting performance. We develop a method for computing forward premium confidence intervals to identify ex-ante periods during which forward-based forecasts are inaccurate.

Does the euro area forward rate provide accurate forecasts of the short rate.pdf

 
mladen:
Not a book, but it will be a series of articles (according to announcement). Here is the announcement : It seems that it will be an interesting series of articles (highlights).

Mladen,

Do you have the full text and the following articles?I am interested in how does the author explain the'' new generation machine learning algorithms'' in detail ?

 

nevar

It is still an announcement

I don't know when exactly those articles will be published but as soon as they are will post them here too (frankly that matter is interesting to me too )

nevar:
Mladen, Do you have the full text and the following articles?I am interested in how does the author explain the'' new generation machine learning algorithms'' in detail ?
 

"Man versus Machine, Parts one and two" - interview with Professor Dave Cliff

 

"The Market Price of Interest-rate Risk: Measuring and Modelling Fear and Greed in the Fixed-income Markets" - Riaz Ahmad

What should govern the pricing of fixed-income instruments? Forget everything you know about single-factor, two- or three-factor models, Vasicek, CIR, Hull & White, Ho & Lee, Heath, Jarrow & Morton, Brace, Gatarek & Musiela, etc., and suppose you were to address interest rate modelling from first principles. How would you go about the modelling?

The following shows how you might approach this task. Note that we aren’t going forget the tools of the trade in terms of stochastic calculus nor the financial principles relating risk and return. The model we end up with is still quite classical in nature, inhabiting the modelling world of stochastic differential equations.

We also aren’t going to worry about calibration. We know that is quite a shocking thing to say these days, but if it helps you read the rest of this paper, think of the ideas as inhabiting an ‘equilibrium world’ rather than a ‘no-arbitrage world.’ Some of the resulting ideas might find use in trading rather than pricing exotics.
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