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A brief introduction to Big Data and Signal Processing - BigDataFinance

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A unenduring introduction to Big Data and Signal Processing - BigDataFinance Skip to content Menu Home People Projects Research Publications Beneficiaries & Partners Events BigDataFinancePrimingTraining Event on Textual Data in Finance Data Science in Finance High-Frequency Data Econometrics Winter School onRamifiednetworks in finance Blog Contact Blog A unenduring introduction to Big Data and Signal Processing 13.11.2017 The upturned nature of financial big datasets requires in depth wringer of their properties. These properties vary from past information and signal filtering to statistical inference and arbitrage identification. There are some unstipulated approaches that one should take into consideration when dealing with big chunks of data. To capitalize on the Big Data, information has to be extracted from all types of data. Data is either structured or unstructured; in particular, 30% is the structured and the rest 70% is unstructured or semi-structured. One major issue that should be taken into consideration with respect to large data sets is a veiling source separation problem, moreover known as the cocktail-party problem as discussed by Gresham and Oransky (2008). Imagine a room crowded with m people speaking simultaneously and in clusters, while several microphones are recording their voices. The question is how we are to view the individual raw signals in isolation so that remoter extraction of data can be accomplished. How are we, in other words, to obtain less noisy and, therefore, increasingly well-judged data? There are several models that can be employed in order to identify the data signal, as for instance the Wienner-Kolmogorov signal extraction filter as suggested by Pollock (2006). This filter minimizes the midpoint square error – the loftiness of a fitted line from data points – but cannot deal with mixed distributions. Another worldwide method in econometrics is Kalman filters (KF) as discussed by Jay, Duvaut and Drolles (2013). The main wholesomeness of this method is that it copes with missing data – which is the sparsity of information on model variables. The main disadvantage of the latter is that it depends on the covariance matrix. A robust indicator of a successful data extraction would be the existence of clusters, or “stable regions” as denoted by Tomasini and Jaekle (2009).Increasinglyspecifically, input parameters like median, mean, and standard deviation should have similar values in the in-sample (i.e. training part of the data) and out-of-sample (i.e. the future projection of the selected data) validation. Backtesting (in-sample and out-of-sample testing) is the simulation of a trading strategy based on data signals as well as historical data and, it is essential in estimating Value-at-Risk (VaR). Modelers should take into consideration the objective of their strategy and therefrom segregate a method. Backtesting methods vary from the most common, delta-normal method, which assumes that all resources returns follow a normal distribution equal to Jorion (2011); historical simulation, which applies current weights to historical windfall returns as presented by Sommacampagna (2003); or Monte Carlo simulation, based on random numbers, to increasingly complex, such as filtered historical simulation, which scales returns equal to their volatility as stated by Barone-Adesi and Giannopoulos (1996), and CTSARMA-GARCH model, which describes powerfully the skewness and fat tails of a distribution as discussed by Carchano, Kim, Sun, Rachev and Fabozzi (2015). Despite limitations such as overfitting or noisy data, to name a few, backtesting stands out as an essential component of Big Data wringer and certainly calls for remoter research.   References: Gresham, Steve D. and Arlen S. Oransky (2008). The New Managed Account Solutions Handbook: How to Build Your Financial Advisory Practice Using Managed Account Solutions. 1st edition. New Jersey: Wiley.   Pollock, D.S.G. (2006). “Econometric Methods of Signal Extraction. Computational Statistics & Data Analysis”. Vol. 50. Issue 9. 2268-2292.   Jay, Emmanuelle, Patrick Duvaut and Serges Darolles (2013). “Multi-Factor Models and Signal Processing Techniques: Application to Quantitative Finance”. 1st edition. London and New Jersey: Wiley-ISTE.   Tomasini, Emilio and Urban Jaekle (2009). Trading Systems: A New Approach to System Development and Portfolio Optimization. Hampshire: Harriman.   Jorion, Philippe (2011). Financial Risk Manager Handbook: FRM Part I / Part II, + Test Bank. 6th edition. New Jersey: Wiley.   Sommacampagna, Cristina (2003). “Estimating Value at Risk with the Kalman Filter”. IV Workshop in Finanza Quantitativa. Available at: http://www.finanzaonline.com/forum/attachments/econometria-e-modelli-di-tradingoperativo/ 836347d1201361672-isteresi-unopportunita-o-una-difficolta-cs1.pdf. [Accessed: 7th October 2015].   Barone-Adesi, Giovanni and Kostas Giannopoulos (1996). “A Simplified Approach to the Conditional Estimation of Value-at-Risk”. Futures and Options World. 68-72.   Carchano, Oscar, Kim Young Shin, Edward W. Sun, Svetlozar T. Rachev and Frank J. Fabozzi (2015). “A Quasi-Maximum Likelihood Estimation Strategy for Value-at-Risk Forecasting: Application to Equity Index Futures Markets”. In Handbook of Financial Econometrics and Statistics. Ed. Lee Cheng-Few, Lee John C. Springer.1325-1340.   Adamantios Ntakaris is based at Tampere University of Technology 2016-2019, and his research project is Divide and Conquer Deep Learning for Big Data in Finance (WP1) Volatility seasonality of Bitcoin prices.. “Learning properties of Bitcoin and other cryptocurrencies and c.. 21.06.2018 Read increasingly BigDataFinance protagonist at the 6th Li.. ESR Chiara Perillo from University of Zurich shared the stage with Nob.. 08.03.2018 Read increasingly Popular tags algorithms deject priming corporate ownership data econometric models ESR extracted knowledge finance financial slipperiness financial markets financial volatility job recruitment research risk management self-data velocity volatility volume Copyright BigDataFinance 2017 | All rights reserved | Login WordPress Download Manager - Best Download Management Plugin Close