Customer Reviews

Introduction to Econophysics: Correlations and Complexity in Finance

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I find the book rather poorly written in the aspect of providing links between statistical physics and its application in economics. As a physicist with a background in stochastic processes, I was looking for an introduction to their applications to economic analysis, complete with examples and discussion of the methods' limitations. The book was somewhat disappointing in this respect. Quite often, in many chapters, the necessary math is explained, then some aspects of how it is manefest in economical data are presented and then the chapter ends, leaving the reader wonder what the specific cases may be and if it is practical to use those methods at all. Above all, there is very little discussion as to what the results actually mean, in economical terms.
I believe the book may be helpful for reseachers active in this field but I would not recommend it as a first introduction to econophysics. For economists, the math may be rather difficult to go through as some of the fundamental concepts are not defined consistently. For physicists with no previous exposure to econophysics, I would prefer to see more economics.

I found several parts of this book useful while preparing lectures for an introductory econophysics course in Fall, 2001. The discussions of convolutions of distributions, Levy distributions and scaling are well-written and easy to follow. In the brief discussion of the St. Petersburg Paradox I missed a critical discussion of expected utility, which was invented by Bernoullli to 'resolve' that paradox. Spurred by von Neumann and Morgenstern, neo-classical economics relies on the idea of expected utility, which seems empirically to be wrong. The chapter on time correlations is also very readable (although Wiener processes are not 1/f^2 noise!). ARCH and GARCH methods are discussed, saving the student from the pain of reading badly-written papers by mathematically-minded economists, but the chapters on options are too brief with nothing new. The best introduction to options is still the original Black-Scholes paper (excepting their erroneous claim that CAPM and the delta-hedge strategy produce option pricing pdes that agree with each other). Also, it would have been nice to have seen a discussion of CAPM. The discussion of algorithmic complexity left me cold (see my earlier books and papers on nonlinear dynamics), and I would like to have seen a critical discussion of the EMH. These criticisms are ok, though, the gaps leave something for the rest of us to work on.

let us not forget it on the MARKET. Contrary to what has been suggested in a review on the econophysics forum, I find this book superior to its competitor by Bouchaud and Potters soon to appear in english at CUP. It is more concise, which is not necessarily an advantage, but in the light of what is available it certainly is one. I am mostly talking about the stuff on pdfs and the market models. What econophysicists have to say about actual financial instruments apart from the fact that they criticize the underlying probabilities used I can still not see, but well is physics not full of rediscoveries, so why not export these embarrassingly (and with arrogance please) outside of physics?

One last point concerns the style in which this and Bouchaud's book are written. I believe that econophysicists have yet to find the proper language in which to talk. Thus, most literature is written in a setting most appealing for statistical physicists, as it strongly hinges on that subject's background and contemporary culture. There are obvious reasons for this, but altogether this needs to be obliterated. Only then econophysics grow into a mature self-consistent branch of the natural sciences. This book is far from attaining such a goal and the comprehensive treatise on physics and economics remains to be written. It is unlikely that this will happen tomorrow given the immaturity of the scene, the actors and the play.

Altogether a book worth the read.

SINCE the last decade, physicists have been trying to cope with the issues traditionally approached by economics using their own tools and methodologies. This research has been dubbed 'econophysics'. One reason why this incursion should be welcomed is the failure of mainstream economics to recognise financial systems as complex systems. Take mainstream international finance, for instance. In the most respectable workhorse model--so-called 'new open economy macroeconomics model'--foreign exchange rates always reach some sort of stable equilibrium. To put it bluntly, this means that currencies do not exhibit complex behaviour.

However, financial markets do demonstrate several of the properties that characterise complex systems. What is more, they are highly complex, open systems in which many subunits interact nonlinearly in the presence of feedback and stable governing rules. Earlier attempts to find chaos in financial data, for instance, have been disappointing exactly because the phenomenon is likely to emerge in systems which are only moderately complex. Although it cannot be ruled out that financial markets follow chaotic dynamics, econophysics assumes that asset price dynamics are stochastic processes.

A fundamental commitment of the mainline model of international finance is to theory itself, and not to data. Modelling is devoted to equipping the discipline with an underlying rational behaviour at the individual level. Yet this is at odds with the fact that financial markets are prone to collective 'irrational exuberance'. Instead, econophysics attemps to build up stochastic models that encompass essential features observed in the financial data. Now that the time evolution of many financial markets is continually monitored, it is possible to test the accuracy and predictive power of the developed models using available data. One common objection to such a practice is that it is impossible to perform large-scale experiments in economics that could falsify any given theory. The authors note that this limitation is not specific to economics, but also affects such well developed areas of physics as astrophysics, atmospheric physics, and geophysics. By analogy with the activity in these more established areas, we are able to test and falsify any theories associated with the current available sets of financial data.

Complex systems can sometimes behave in remarkable simple ways. These are reflected in power law distributions and scaling. The authors illustrate these concepts and others, and apply them to the financial time series. The book is thus useful not only for physicists but also for economists and people in the financial world. Some familiarity with probability theory or statistical physics is required, though. Economists dissatisfied with the mainline approach of their discipline will find the book opportune. The others might end up welcoming econophysics as well. After all, economists implicitly see physics as nature's economics. What is then wrong with physicists thinking of economics as social physics?

The book is a quite nice introduction into the concept of application of physics to financial markets or to say better financial models. Both authors have published numerous papers in this same field, and this book I would say clearly summarizes some of their work as well as the work of other researchers. As I said the book is a good introduction to the subject, and it does not go into some great detail of each topic discussed, so if you are interested in something more detailed I would recommend not to read this book (but only if you already are familiar with the general topics of financial modeling). Authors presented current research results, whith a very nice and detailed bibliography, which I would recommend using if you are very interested in financial modeling, as there are some very good research papers and books cited.

As an introduction the book goes on to describe the basic points of today research process in this field, and some of the questions that yet have not been answered. There is very nice presentation of random walk and Levy processes, which seem to be quite an attraction in current research. Scaling and correlation is explained from the level of indices as whole down to a individual company stock prices. Some other topics which the authors discuss are:scaling, time correlation, correlation of financial time series, ARCH and GARCH processes, market turbulence (quite an interesting connection to physics), where they end the book with some option pricing theories.

The book is written in a very understandable language, where basic probability theory should be known to the reader. I would highly recommend this book to anyone interested in this field, but also to researchers, where I think they would find this book very useful introduction, as it describes some of the major work done in this field.

Under the Efficient Market Hypothesis, the market accurately reflects all knowledge available about all stocks at all times. Consequently, the movement of stock prices follows a Brownian motion -- specifically a geometric Brownian motion, meaning that the logarithm of the price at any given time is a Brownian-motion process.

It's long been known that this model doesn't really work: the "tails are too heavy," in the jargon, meaning that there are more large and small stock prices than the Brownian model would predict. The Taylor and Karlin book that we used in my first stochastic-processes class proposed that this is because the number of stock trades is itself a random variable -- specifically a Poisson process. At least at a first pass, this doesn't really provide a satisfactory explanation of the heavy tails. It seems like it lacks "microfoundations," in the economists' lingo: it doesn't explain the higher-level process (stock-price motion) in terms of the aggregated behaviors of economic actors.

It's not clear from Mantegna and Stanley's book that these microfoundations are really in the offing. They proceed through a wide variety of stock-price models, each of which explains more of the stock market's behavior. For starters, they ask whether individual stock-price changes can be best modeled by a random variable with a finite or an infinite variance. If the stock-price changes have a finite variance, then the trajectory of the stock price -- which is the sum of the price changes -- will follow the Central Limit Theorem, and the trajectory will be your standard, friendly Gaussian random variable.

If the variance is infinite, on the other hand -- meaning that stock prices can be much more volatile -- then the Central Limit Theorem breaks down: the trajectory converges instead to a Lévy stable process. So Mantegna and Stanley converge on a set of models called a "truncated Lévy flight," which assigns probability densities like a Lévy process within a symmetric interval around the origin, and no probability outside. By adjusting the width of that interval, we can bring the Lévy closer to a Gaussian.

So the truncated Lévy flight is a convenient modeling device, in that a suitable adjustment of parameters can explain one part of the distribution of stock prices. But does it actually provide understanding? Do we understand why this particular distribution looks more like the actual distribution of stock prices than does a Gaussian? Here we would have understanding if we could explain why variance is either finite or unbounded. Unless I misread Mantegna and Stanley, there's no good theoretical reason to expect either finite or infinite variance, and professional opinion is only just coalescing around finite variance.

The book contains other related concepts, among them ARCH and GARCH models that explain both heavy tails and a long-term dependence in stock volatility. But again, these models merely postulate a certain form of relationship, then conclude that various parameters are set in certain ways; they don't seem to confer understanding.

Mantegna and Stanley end with a couple quick related topics: how to identify stocks whose prices move together, and the Black-Scholes options-pricing model. Understanding synchronized stock-price movements is important in the construction of diversified portfolios -- "diversity" here being basically synonymous with "unsynchronized" (if you buy a share of Proctor and Gamble and a share of RJR, your portfolio is not diversified).

Throughout, they are as brisk as they could be, but no brisker; they give the reader a great breadth of understanding with decent depth, which is quite a trick in a book that's not much more than 100 pages long. This book would actually make a fantastic first course in stochastic processes: students wishing to move beyond the initial sketches of geometric Brownian motion can easily do so using the bibliography. If this book were a bit cheaper on the used market, I would snap it up in a heartbeat.

This is essentially a scientific review of econophysics published as a book. I am not an expert in this field and cannot judge, how up-to-date it is. But it is certainly a great introduction to econophysics that is suitable for physicists / physical chemists / possibly mathematicians. (H.E. Stanley is himself a physical chemist who has many publications on molecular diffusion and molecular dynamics.) Contrary to some assertions, this is *not* a re-hash of the papers published by the authors themselves: the list of reviewed literature seems to be fairly comprehensive, with a good coverage of old (pre-1940s) works as well as a reasonable coverage of more modern research. The list of references contains 158 items, the majority of which do not involve Mantegna or Stanley. The text is concise: 148 pages, including bibliography and index. It contains none of the popular "fluff" one usually has to wade through when reading books on this topic. The coverage is reasonably mathematical, but the mathematics should be accessible to anyone with a graduate-level knowledge of statistics or statistical physics and a physicist's knowledge of partial differential equations. The discussion covers plenty of concrete empirical data. I wish the book contained a few more specific examples, but it is fine as is. I highly recommend it.

This book is an excellent introductory text for the subject, and I recommend it. It gives a broad view of leading methodologies in the field. It's only shortcoming is that it is a little bit old and econophysics have seen a great deal of progess since the time it was written, and newer and somewhat different approaches have evolved. It is easy reading and I recommend it for begginers in the field of econophysics.

This book is an excellent introduction to financial analitics for Physicists and also for others. Though a little out dated, but what can you expect from such a fast changing subject?
This is not the first book I have read in this subject, but it is my favorite right now. I could have saved myself a lot of trouble if this would have been the first.

Nevertheless, it should be considered as an intial reference point and not as to expect it to contain all the details. After all it only has 148 pages.

The book is not bad considering the total lack of existence of intelligible literature in this supposedly vast field.

The content is really a collection of quickie crib-sheets on a sundry of topics with nominally common theme: Finance.

A lot of the actually useful stuff is the author's previously published papers on price-return distributions.

Aside from his own previously published work, he has a good tutorial on the GARCH scheme though with precious little follow up reading resources for delving in deeper (or even sideways).

This book is priced far too high given its content and depth.
Look for a used copy, and do not count on the author to answer questions by email.