Spatial finance:practical and theoretical contributions to financial analysis

We introduce and define a new concept, ‘Spatial Finance’, as the integration of geospatial data and analysis into financial theory and practice, and describe how developments in earth observation, particularly as the result of new satellite constellations, combined with new artificial intelligence methods and cloud computing, create a plethora of potential applications for Spatial Finance. We argue that Spatial Finance will become a core future competency for financial analysis, and this will have significant implications for information markets, risk modelling and management, valuation modelling, and the identification of investment opportunities. The paper reviews the characteristics of geospatial data and related technology developments, some current and future applications of Spatial Finance, and its potential impact on financial theory and practice

Financial Modelling: Where to go? with an illustration for portfolio management

The definition of Financial Modelling chosen by the EURO working group on financial modelling is ‘the development and implementation of tools supporting firms, investors, intermediaries, governments and others in their financial-economic decision making, including the validation of the premises behind these tools and the measurement of the effectivity of the use of these tools’. Clearly, in this definition, the decision and its solution is central. Unlike financial modelling in our definition, the theory of finance is not so much concerned with individual decisions, but rather with the effects of the decisions and actions of many individuals on the formation of prices in financial markets. It is therefore no wonder that the assumptions underlying financial theory, which at best describe ‘average individuals’ and ‘average decision situations’, are not suited to describe specific individual decision problems. In our view it is the role of financial modelling to support individual decision making, taking account of the peculiarities of the actual case, where possible taking benefit from the results of the financial theory. This philosophy towards financial modelling is illustrated by a framework for portfolio management

The methodology of finance

First paragraph: The methodology of finance is crucial to the field. The way in which financial markets and behaviour are analysed depends on the methodological approach taken to building knowledge. This methodological approach includes both the methods of enquiry and the principles by which some theories are judged to be better than others. But in finance there is more reflexivity than normal, in that finance theory and modelling directly inform and guide actual market behaviour. Therefore the methodology of finance theory carries over into the methodology of practice. For example, we have seen in the financial crisis that broke in 2007 that the methodology of finance, particularly the reliance on quantitative models, was a major contributor to the si

From tools to theories: The emergence of modern financial economics

It is shown that early research in modern financial economics had substantially been driven by the application of the research strategy of economics and the use of newly developed mathematical methods. For this purpose the professionalization of business education as a consequence of changes in the U.S. economy after Word War II is presented. The emergence of professional Journals in financial economics, similar to the academic culture including the trend of applying abstract mathematical reasoning and during the war developed methods like linear programming are highlighted. Also the meaning of Milton Friedman's 1953 essay The Methodology of Positive Economics for the dominance of abstract and prediction driven research in modern financial economics gets discussed. Finally, the emergence of Harry Markowitz's paper Portfolio Selection (1952) is used to substantiate the hypothesis. --history of finance,portfolio theory,business schools,modern financial economics,modelling,theories of modern financial economics,risk management,positivism,professionalization,methodology of finance

Systematic risk analysis: first steps towards a new definition of beta

We suggest a new model-free definition of the beta coefficient, which plays an important rôle in systematic risk management. This setting, which is based on the existence of trends for financial time series via nonstandard analysis (Fliess M., Join C.: A mathematical proof of the existence of trends in financial time series, Proc. Int. Conf. Systems Theory: Modelling, Analysis and Control, Fes, 2009, online: http://hal.inria.fr/inria-00352834/en/) leads to convincing computer experiments which are easily implementable.Quantitative finance; risk analysis; beta; alpha; trends; technical analysis; estimation techniques; forecasting; abrupt changes; nonstandard analysis.

The Nobel Memorial Prize for Robert F. Engle

Engle’s footsteps range widely. His major contributions include early work on band-spectral regression, development and unification of the theory of model specification tests (particularly Lagrange multiplier tests), clarification of the meaning of econometric exogeneity and its relationship to causality, and his later stunningly influential work on common trend modeling (cointegration) and volatility modelling (ARCH, short for Auto Regressive Conditional Heteroskedasticity). More generally, Engle’s cumulative work is a fine example of best-practice applied time-series econometrics: he identifies important dynamic economic phenomena, formulates precise and interesting questions about those phenomena, constructs sophisticated yet simple econometric models for measurement and testing, and consistently obtains results of widespread substantive interest in the scientific, policy, and financial communities.Econometric Theory, Finance

On the approximation of L\'evy driven Volterra processes and their integrals

Volterra processes appear in several applications ranging from turbulence to energy finance where they are used in the modelling of e.g. temperatures and wind and the related financial derivatives. Volterra processes are in general non-semimartingales and a theory of integration with respect to such processes is in fact not standard. In this work we suggest to construct an approximating sequence of L\'evy driven Volterra processes, by perturbation of the kernel function. In this way, one can obtain an approximating sequence of semimartingales. Then we consider fractional integration with respect to Volterra processes as integrators and we study the corresponding approximations of the fractional integrals. We illustrate the approach presenting the specific study of the Gamma-Volterra processes. Examples and illustrations via simulation are given.Comment: 39 pages, 3 figure

Financial Factors, Macroeconomic Information and the Expectations Theory of the Term Structure of Interest Rates

In this paper we concentrate on the hypothesis that the empirical rejections of the Expectations Theory(ET) of the term structure of interest rates can be caused by improper modelling of expectations. Our starting point is an interesting anomaly found by Campbell-Shiller(1987), when by taking a VAR approach they abandon limited information approach to test the ET, in which realized returns are taken as a proxy for expected returns. We use financial factors and macroeconomic information to construct a test of the theory based on simulating investors' effort to use the model in `real time' to forecast future monetary policy rates. Our findings suggest that the importance of fluctuations of risk premia in explaining the deviation from the ET is reduced when some forecasting model for short-term rates is adopted and a proper evaluation of uncertainty associated to policy rates forecast is consideredExpectations Theory, Macroeconomic information in Finance