Outline of the remainder of this book Chapter 2 This introduces the classical linear regression model (CLRM). The ordinary least squares (OLS) estimator is derived and its interpretation discussed. The conditions for OLS optimality are stated and explained. A hypothesis testing framework is developed and examined in the context of the linear model. Examples employed include Jensen’s classic study of mutual fund performance measurement and tests of the ‘overreaction hypothesis’ in the context of the UK stock market. Chapter 3 This continues and develops the material of chapter 2 by generalising the bivariate model to multiple regression -- i.e. models with many variables. The framework for testing multiple hypotheses is outlined , and measures of how well the model fits the data are described. Case studies include modelling rental values and an application of principal components analysis to interest rate modelling. Chapter 4 Chapter 4 examines the important but often neglected topic of diagnostic testing . The consequences of violations of the CLRM assumptions are described, along with plausible remedial steps. Model-building philosophies are discussed, with particular reference to the general-to-specific approach. Applications covered in this chapter include the determination of sovereign credit ratings. Chapter 5 This presents an introduction to time series models, including their motivation and a description of the characteristics of financial data that they can and cannot capture. The chapter commences with a presentation of the features of some standard models of stochastic ( white noise, moving average, autoregressive and mixed ARMA ) processes. The chapter continues by showing how the appropriate model can be chosen for a set of actual data, how the model is estimated and how model adequacy checks are performed. The generation of forecasts from such models is discussed, as are the criteria by which these forecasts can be evaluated. Examples include model-building for UK house prices, and tests of the exchange rate covered and uncovered interest parity hypotheses. Chapter 6 This extends the analysis from univariate to multivariate models . Multivariate models are motivated by way of explanation of the possible existence of bi-directional causality in financial relationships, and the simultaneous equations bias that results if this is ignored. Estimation techniques for simultaneous equations models are outlined. Vector autoregressive(VAR) models , which have become extremely popular in the empirical finance literature, are also covered. The interpretation of VARs is explained by way of joint tests of restrictions, causality tests, impulse responses and variance decompositions. Relevant examples discussed in this chapter are the simultaneous relationship between bid--ask spreads and trading volume in the context of options pricing, and the relationship between property returns and macroeconomic variables. Chapter 7 The first section of the chapter discusses unit root processes and presents tests for non-stationarity in time series . The concept of and tests for cointegration, and the formulation of error correction models , are then discussed in the context of both the single equation framework of Engle--Granger, and the multivariate framework of Johansen. Applications studied in chapter 7 include spot and futures markets, tests for cointegration between international bond markets and tests of the purchasing power parity hypothesis and of the expectations hypothesis of the term structure of interest rates .
This book concerns the use of concepts from statistical physics in the description of financial systems. Specifically, the authors illustrate the scaling concepts used in probability theory, in critical phenomena, and in fully developed turbulent fluids. These concepts are then applied to financial time series to gain new insights into the behavior of financial markets. The authors also present a new stochastic model that displays several of the statistical properties observed in empirical data. Usually in the study of economic systems it is possible to investigate the system at different scales. But it is often impossible to write down the 'microscopic' equation for all the economic entities interacting within a given system. Statistical physics concepts such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling permit an understanding of the global behavior of economic systems without first having to work out a detailed microscopic description of the same system. This book will be of interest both to physicists and to economists. Physicists will find the application of statistical physics concepts to economic systems interesting and challenging, as economic systems are among the most intriguing and fascinating complex systems that might be investigated. Economists and workers in the financial world will find useful the presentation of empirical analysis methods and wellformulated theoretical tools that might help describe systems composed of a huge number of interacting subsystems. This book is intended for students and researchers studying economics or physics at a graduate level and for professionals in the field of finance. Undergraduate students possessing some familarity with probability theory or statistical physics should also be able to learn from the book. DR ROSARIO N. MANTEGNA is interested in the empirical and theoretical modeling of complex systems. Since 1989, a major focus of his research has been studying financial systems using methods of statistical physics. In particular, he has originated the theoretical model of the truncated Levy flight and discovered that this process describes several of the statistical properties of the Standard and Poor's 500 stock index. He has also applied concepts of ultrametric spaces and cross-correlations to the modeling of financial markets. Dr Mantegna is a Professor of Physics at the University of Palermo. DR H. EUGENE STANLEY has served for 30 years on the physics faculties of MIT and Boston University. He is the author of the 1971 monograph Introduction to Phase Transitions and Critical Phenomena (Oxford University Press, 1971). This book brought to a. much wider audience the key ideas of scale invariance that have proved so useful in various fields of scientific endeavor. Recently, Dr Stanley and his collaborators have been exploring the degree to which scaling concepts give insight into economics and various problems of relevance to biology and medicine.
The Chinese Credit Bubble - Full Frontal Submitted by Tyler Durden on 11/05/2012 22:40 -0400 Whereas it is relatively easy to track the progression of the "developed world" deep into the twilight rabbit zone hole (in bizarro metaphore-land speak) of no total debt/GDP return as defined by Reinhart and Rogoff (where anything above 80% sovereign leverage is more or less the game over line for one country, let along the entire Western world) courtesy of day to day updates of total debt in the US (103% debt/GDP) and its comparably indebted peers, when it comes to world's growth dynamo - China - it is next to impossible to get a sense of just how big the debt hole is for a country whose economic data has been and continues to be one massive goalseeked, G.I.G.O. blackbox. At least that is the case at the sovereign level where the government can and does show whatever data it feels like as the country is excluded from traditional counterparty flow checks which serve as an at least modest buffer for data fabrication for the other globalized countries engaging in international trade. That, and the Ministry of Truth of course, which some have likened recently to an amateur version of the US' own BLS. However, while government and consumer debt can be whatever China wants it to be (and when it isn't, any discharged and non-performing debt is merely masked over with more debt: China doesn't have $3 trillion in foreign reserves for nothing) corporate debt, in keeping with Western-style reporting requirements, is far more difficult to obfuscate and falsify in recent years. It is here that we get the first glimpse of the true sheer extent of the Chinese credit bubble, which as the chart below shows, is already the largest in the entire world. None other than Goldman Sachs is concerned by this absolute number, which in recent years has exploded to all time highs: The rapid rise of corporate leverage to 130% of GDP in 2011 - one of the highest corporate leverage ratios in the world - is concerning . This high leverage is the result of substantial investment in the manufacturing sector since 2008, leading to over-capacity in many sectors such as solar energy, steel and ship building . It is therefore critical for the new leadership to pursue reforms that not only support the private sector, but also consumption more broadly, in order to utilize this capacity; the alternative would likely prove negative for sectors, banks, and ultimately, the economy. And here we go back to the one simplest fact of functional leverage that so few grasp: namely that debt, like money, is fungible. And debt, like money, will go to whatever sector has the capacity to carry it: be it corporate, household, financial, or, as a last resort, sovereign, in order to extract every possible ounce of future growth at the expense of current assets and cash flows, until neither viable collateral (as Europe has discovered), nor cash flows (as is becoming ever more apparent in the US) can sustain it. Sadly, it is still virtually impossible to get a comprehensive picture of total Chinese leverage as a function of GDP, the way we can, and have shown for the rest of the world. Recall from " Five Years Since The Great Financial Crisis: "No Growth, No Deleveraging " these two very telling charts: Total debt to GDP broken down by insolvent developed country: And total average and median rebased economy debt: What this shows is that all platitudes of the Richard Koos aside and Paul Krugmans, who demand ever more debt, the developed world is at its debt capacity. So what can we infer about the full big picture in China? We present Chart 2: the historical rise in Chinese corporate (somewhat auditable) and other other (completely imaginary) debt: If one takes the chart above showing the absolute level in Corporate debt, and assumes this is a valid proxy for total leverage growth across all other sectors, one can say, with a straight face, that if all Chinese debt on and off the books, including shadow leverage, were to be pooled, it would make America's grand consolidated debt (excluding the $100 trillion in entitlements) of 345% appears quite modest. 26946 reads Printer-friendly version Send to friend Similar Articles You Might Enjoy: Wikileaks Reveals Chinese Top Officials Say Not To Trust Country's Economic Data In Advance Of Tomorrow's "Future Of Housing Finance" Kabuki Theater; Or Why The GSE Zombies Will Suck The US Middle Class Dry Forever, Amen Are The BRICs Broken? Goldman And Roubini Disagree On China Guest Post: Central Planning's Christmas Problem Even Goldman Says China Is Cooking The Books
Last week, we published a chart-essay that illustrates the extreme inequality that has developed in the US economy over the past 30 years. The charts explain what the Wall Street protesters are angry about. They also explain why the protesters' message is resonating with the country at large. Here are the four key points: 1. Unemployment is at the highest level since the Great Depression (with the exception of a brief blip in the early 1980s). 2. At the same time, corporate profits are at an all-time high , both in absolute dollars and as a share of the economy. Image: St. Louis Fed Image: St. Louis Fed 3. Wages as a percent of the economy are at an all-time low. In other words, corporate profits are at an all-time high, in part, because corporations are paying less of their revenue to employees than they ever have. There are lots of reasons for this, many of which are not the fault of the corporations. (It's a global economy now, and 2-3 billion new low-cost employees in China, India, et al, have recently entered the global workforce. This is putting pressure on wages the world over.) Image: St. Louis Fed 4. Income and wealth inequality in the US economy is near an all-time high: The owners of the country's assets (capital) are winning, everyone else (labor) is losing. Three charts illustrate this: The top earners are capturing a higher share of the national income than they have anytime since the 1920s: CEO pay and corporate profits have skyrocketed in the past 20 years, "production worker" pay has risen 4%. After adjusting for inflation, average earnings haven't increased in 50 years. It's worth noting that the US has been in a similar situation before: At the end of the "Roaring '20s," just before the start of the Great Depression. (See some of the charts above). It took the country 15-20 years to pull out of that slump and fix the imbalances. But by the mid-1950s, employment, corporate profits, wages, and inequality had all returned to more normal levels. And the country enjoyed a couple of decades of relatively well-balanced prosperity. But now, everything's out of whack again.
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