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Finite sample analysis of predictive regressions with long-horizon returns

Kan, Raymond; Pan, Jiening - 2021 - This version: February 2021

In this paper, we provide an exact finite sample analysis of predictive regressions with overlapping long-horizon returns. This analysis allows us to evaluate the reliability of various asymptotic theories for predictive regressions in finite samples. In addition, our finite sample analysis...

Persistent link: https://www.econbiz.de/10012593767

Deep Signature models for Financial Equity Time Series prediction

Alonso, Miquel Noguer i; Agrawal, Himanshu; Srivastava, … - 2022

We explore in this paper the use of deep signature models to predict equity financial time series returns. First, we use signature transformations to model the underlying shape of the input equity returns; further assuming the underlying shape remains the same, we predict future values based on...

Persistent link: https://www.econbiz.de/10013289206

COPOD : Copula-Based Outlier Detection

Li, Zheng; Zhao, Yue; Botta, Nicola; Ionescu, Cezar; … - 2021

Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm...

Persistent link: https://www.econbiz.de/10013242963

A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series

Webel, Karsten - 2022

Infra-monthly time series have increasingly appeared on the radar of official statistics in recent years, mostly as a consequence of a general digital transformation process and the outbreak of the COVID-19 pandemic in 2020. Many of those series are seasonal and thus in need for seasonal...

Persistent link: https://www.econbiz.de/10013336397

Towards seasonal adjustment of infra-monthly time series with JDemetra+

Webel, Karsten; Smyk, Anna - 2023

Infra-monthly economic time series have become increasingly popular in official statistics in recent years. This evolution has been largely fostered by official statistics’ digital transformation during the last decade. The COVID-19 pandemic outbreak in 2020 has added fuel to the fire as many...

Persistent link: https://www.econbiz.de/10014336194

Revisions in concurrent seasonal adjustments of daily and weekly economic time series

Webel, Karsten - 2025

The COVID-19 outbreak in 2020 has fostered in many countries the development of new weekly economic indices for the timely tracking of pandemic-related turmoils and other forms of rapid economic changes. Such indices often utilise information from daily and weekly economic time series that...

Persistent link: https://www.econbiz.de/10015373330

A Meta-Learning approach to Model Uncertainty in Financial Time Series

Alonso, Miquel Noguer i; Batres-Estrada, Gilberto; … - 2021

Financial markets have experienced several negative sigma events in recent years; these eventsoccur with much more regularity than current risk models can predict. There is no guarantee thatthe training set's data generating process will be the same in the test set in finance. Mathematicalmodels...

Persistent link: https://www.econbiz.de/10013236220

Statistical Arbitrage and High-Frequency Data with an Application to Eurostoxx 50 Equities

Rudy, Jozef - 2013

data and compare its profit potential to the standard sampling frequency of daily closing prices. We use a simple trading … data sampling frequencies. The frequencies observed range from a 5-minute interval, to prices recorded at the close of each … (e.g. above 3 for an average pair sampled at the high-frequency interval and above 1 for a daily sampling frequency) …

Persistent link: https://www.econbiz.de/10013081228

Is Your Covariance Matrix Still Relevant? An Asset Allocation-Based Analysis of Dynamic Volatility Models

Colon, James - 2013

Ever since Harry Markowitz published his seminal paper on portfolio selection, investors have incorporated estimates of future volatilities and correlations into their asset allocation process. While portfolio construction methods continue to evolve, many investors continue to forecast...

Persistent link: https://www.econbiz.de/10013086014

The Shape of Performance Curve in Financial Time Series

Alonso, Miquel Noguer i; Srivastava, Sonam - 2021

in some specific domains.We discuss some of the recent discoveries in the mathematical theory of machine learning that … reduce the gap between theory and practice. We conduct experiments in the financial time series domain using deep neural … financial time series domain. This is consistent with the finance practitioner's theory that backtesting ( training data …

Persistent link: https://www.econbiz.de/10013310497