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Aggregation – Definition

Aggregation Definition

Aggregation refers to an act of grouping items or things as a whole. This term can be used in various contexts, disciplines, and industries. In data analysis, aggregation refers to the compilation of information which is used to create datasets for analysis purposes.

In futures markets, aggregation refers to the combination of all futures positions held by an individual investor or a group of traders.

Aggregation can also be used in accounting, financial planning, real estate, and others. When used in Financial planning, aggregation is an accounting method in which financial data and reports from different entities are consolidated into one.

A Little More on What is Aggregation

Generally, aggregation refers to an act of grouping different items into a whole. Common words associated with aggregation are combination, compilation, and gathering.

Aggregation is popular among financial advisors, these professionals use this method to a group of the investments of positions help by a single investor in the financial market.

Individuals who specialize in aggregation are called aggregators. They are helpful to companies, investors and financial advisors.

Account Aggregation

Account aggregation is simply the harmonization or consolidation of different financial data belonging to an individual or an institution. Amount aggregation is common in portfolio management, it entails financial managers grouping all assets held by an advisor on behalf of a client.

In certain cases, aggregation entails the consolidation of managed and unmanaged accounts belonging to an investor or an organization. While managed amounts are those handled by a financial advisor on behalf of a client, unmanaged accounts are not under the control of an advisor such as retirement plans, employees savings accounts, pension funds and others.

Importance of Account Aggregation

The importance of account aggregation include the following;

  • Aggregation is an accurate method of getting all the positions or assets owned by an investor or a firm.
  • It offers a convenient way of identifying items in a group.
  • Aggregation facilitates a proper analysis, whether Financial Analysis or data analysis.
  • In financial planning, the liabilities and net worth of investors can be realized.
  • Aggregation also helps in assessing the level of risks and returns an investment.

Effects of Account Aggregation

Account aggregation is important to financial institutions, brokerage firms and financial planners.

Account aggregation helps in arranging data points of information into a group that can easily be used in account reporting and analysis.

However, a disruption in the database can create issues for information of clients that have been aggregated by these firms. For instance, if a bank’s website is hacked, customer’s information can be used by third parties for activities that are not in the interest of the customers. A break down in data flow can also cause customers themselves not to have access to their information collected by a firm, bank or institution.

Reference for “Aggregation”






Academics research on “Aggregation”

Aggregation-robustness and model uncertainty of regulatory risk measures, Embrechts, P., Wang, B., & Wang, R. (2015). Aggregation-robustness and model uncertainty of regulatory risk measures. Finance and Stochastics, 19(4), 763-790. Research related to aggregation, robustness and model uncertainty of regulatory risk measures, for instance, value-at-risk (VaR) and expected shortfall (ES), is of fundamental importance within quantitative risk management. In risk aggregation, marginal risks and their dependence structure are often modelled separately, leading to uncertainty arising at the level of a joint model. In this paper, we introduce a notion of qualitative robustness for risk measures, concerning the sensitivity of a risk measure to the uncertainty of dependence in risk aggregation. It turns out that coherent risk measures, such as ES, are more robust than VaR according to the new notion of robustness. We also give approximations and inequalities for aggregation and diversification of VaR under dependence uncertainty, and derive an asymptotic equivalence for worst-case VaR and ES under general conditions. We obtain that for a portfolio of a large number of risks, VaR generally has a larger uncertainty spread compared to ES. The results warn that unjustified diversification arguments for VaR used in risk management need to be taken with much care, and they potentially support the use of ES in risk aggregation. This in particular reflects on the discussions in the recent consultative documents by the Basel Committee on Banking Supervision.

Dependence structures for multivariate high-frequency data in finance, Breymann, W., Dias, A., & Embrechts, P. (2003). Dependence structures for multivariate high-frequency data in finance. Stylized facts for univariate high-frequency data in finance are well known. They include scaling behaviour, volatility clustering, heavy tails and seasonalities. The multivariate problem, however, has scarcely been addressed up to now. In this paper, bivariate series of high-frequency FX spot data for major FX markets are investigated. First, as an indispensable prerequisite for further analysis, the problem of simultaneous deseasonalization of high-frequency data is addressed. In the following sections we analyse in detail the dependence structure as a function of the timescale. Particular emphasis is put on the tail behaviour, which is investigated by means of copulas.

Temporal aggregation and the continuous‐time capital asset pricing model, Longstaff, F. A. (1989). Temporal aggregation and the continuous‐time capital asset pricing model. The Journal of Finance, 44(4), 871-887.


We examine how the empirical implications of the Capital Asset Pricing Model (CAPM) are affected by the length of the period over which returns are measured. We show that the continuous‐time CAPM becomes a multifactor model when the asset pricing relation is aggregated temporally. We use Hansen’s Generalized Method of Moments (GMM) approach to test the continuous‐time CAPM at an unconditional level using size portfolio returns. The results indicate that the continuous‐time CAPM cannot be rejected. In contrast, the discrete‐time CAPM is easily rejected by the tests. These results have a number of important implications for the interpretation of tests of the CAPM which have appeared in the literature.


Multivariate extremes and the aggregation of dependent risks: examples and counter-examples, Embrechts, P., Lambrigger, D. D., & Wüthrich, M. V. (2009). Multivariate extremes and the aggregation of dependent risks: examples and counter-examples. Extremes, 12(2), 107-127.


Properties of risk measures for extreme risks have become an important topic of research. In the present paper we discuss sub- and superadditivity of quantile based risk measures and show how multivariate extreme value theory yields the ideal modeling environment. Numerous examples and counter-examples highlight the applicability of the main results obtained.

Random aggregation with applications in high‐frequency finance, Tsay, R. S., & Yeh, J. H. (2011). Random aggregation with applications in high‐frequency finance. Journal of Forecasting, 30(1), 72-103.


In this paper we consider properties of random aggregation in time series analysis. For application, we focus on the problem of estimating the high‐frequency beta of an asset return when the returns are subject to the effects of market microstructure. Specifically, we study the correlation between intraday log returns of two assets. Our investigation starts with the effect of non‐synchronous trading on intraday log returns when the underlying return series follows a stationary time series model. This is a random aggregation problem in time series analysis. We also study the effect of non‐synchronous trading on the covariance of two asset returns. To overcome the impact of non‐synchronous trading, we use Markov chain Monte Carlo methods to recover the underlying log return series based on the observed intraday data. We then define a high‐frequency beta based on the recovered log return series and propose an efficient method to estimate the measure. We apply the proposed analysis to many mid‐ or small‐cap stocks using the Trade and Quote Data of the New York Stock Exchange, and discuss implications of the results obtained.

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