scholarly journals Model Free Inference on Multivariate Time Series with Conditional Correlations

Stats ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 484-509
Author(s):  
Dimitrios Thomakos ◽  
Johannes Klepsch ◽  
Dimitris N. Politis
Keyword(s):  
Portfolio Management ◽  
Likelihood Function ◽  
Multivariate Time Series ◽  
Joint Modeling ◽  
Conditional Correlation ◽  
Real World Data ◽  
Free Spirit ◽  
Model Free ◽  
Modeling And Forecasting ◽  
Conditional Correlations

New results on volatility modeling and forecasting are presented based on the NoVaS transformation approach. Our main contribution is that we extend the NoVaS methodology to modeling and forecasting conditional correlation, thus allowing NoVaS to work in a multivariate setting as well. We present exact results on the use of univariate transformations and on their combination for joint modeling of the conditional correlations: we show how the NoVaS transformed series can be combined and the likelihood function of the product can be expressed explicitly, thus allowing for optimization and correlation modeling. While this keeps the original “model-free” spirit of NoVaS it also makes the new multivariate NoVaS approach for correlations “semi-parametric”, which is why we introduce an alternative using cross validation. We also present a number of auxiliary results regarding the empirical implementation of NoVaS based on different criteria for distributional matching. We illustrate our findings using simulated and real-world data, and evaluate our methodology in the context of portfolio management.

scholarly journals Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values

2020 ◽  
Vol 34 (04) ◽  
pp. 5956-5963
Author(s):  
Xianfeng Tang ◽  
Huaxiu Yao ◽  
Yiwei Sun ◽  
Charu Aggarwal ◽  
Prasenjit Mitra ◽  
...  
Keyword(s):  
Time Series ◽  
Real World ◽  
Missing Values ◽  
Temporal Dynamics ◽  
Multivariate Time Series ◽  
Temporal Distribution ◽  
Joint Modeling ◽  
Adversarial Training ◽  
Global Patterns ◽  
Real World Datasets

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many efforts have been devoted to this problem, most of them solely rely on local dependencies for imputing missing values, which ignores global temporal dynamics. Local dependencies/patterns would become less useful when the missing ratio is high, or the data have consecutive missing values; while exploring global patterns can alleviate such problem. Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values. However, work in this direction is rather limited. Therefore, we study a novel problem of MTS forecasting with missing values by jointly exploring local and global temporal dynamics. We propose a new framework øurs, which leverages memory network to explore global patterns given estimations from local perspectives. We further introduce adversarial training to enhance the modeling of global temporal distribution. Experimental results on real-world datasets show the effectiveness of øurs for MTS forecasting with missing values and its robustness under various missing ratios.

scholarly journals Deep Reinforcement Learning Agent for S&P 500 Stock Selection

Axioms ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 130
Author(s):  
Tommi Huotari ◽  
Jyrki Savolainen ◽  
Mikael Collan
Keyword(s):  
Portfolio Management ◽  
Stock Selection ◽  
Real World Data ◽  
Data Set ◽  
Agent Based ◽  
Trading Systems ◽  
Learning Agent ◽  
Trading Agent ◽  
Risk Taker ◽  
Insight Into

This study investigated the performance of a trading agent based on a convolutional neural network model in portfolio management. The results showed that with real-world data the agent could produce relevant trading results, while the agent’s behavior corresponded to that of a high-risk taker. The data used were wide in comparison with earlier reported research and was based on the full set of the S&P 500 stock data for twenty-one years supplemented with selected financial ratios. The results presented are new in terms of the size of the data set used and with regards to the model used. The results provide direction and offer insight into how deep learning methods may be used in constructing automatic trading systems.

Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting

2020 ◽  
Vol 30 (08) ◽  
pp. 2050039 ◽  
Author(s):  
Foued Saâdaoui ◽  
Othman Ben Messaoud
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Multivariate Time Series ◽  
Seasonal Effects ◽  
Real World Data ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Mode Decomposition ◽  
Nonlinear Trends

Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction that we propose, in this paper, a new multiscaled Feedforward Neural Network (FNN), with the aim of forecasting multivariate time series. This new model, called Empirical Mode Decomposition (EMD)-based Neural ARDL, is inspired from the well-known Autoregressive Distributed Lag (ARDL) model being our proposal founded upon the concepts of nonlinearity, EMD-multiresolution and neural networks. These features give the model the ability to effectively capture many nonlinear patterns like the ones often present in econophysical time series, such as nonlinear trends, seasonal effects, long-range dependency, etc. The proposed algorithm can be summarized into the following four basic tasks: (i) EMD breaking-down multivariate time series into different resolution levels, (ii) feeding EMD components from the same levels into a number of feedforward neural ARDL models, (iii) from one level to the next, extrapolating the component corresponding to the response variable (scalar output) a number of steps ahead, and finally, (iv) recombining level-by-level forecasts into a single output. An optimal learning scheme is rigorously designed for efficiently training the new proposed architecture. The approach is finally tested and compared to a number of powerful benchmark models, where experiments are conducted on real-world data.

scholarly journals Risk Spillover during the COVID-19 Global Pandemic and Portfolio Management

2021 ◽  
Vol 14 (5) ◽  
pp. 222
Author(s):  
Mohamed Yousfi ◽  
Abderrazak Dhaoui ◽  
Houssam Bouzgarrou
Keyword(s):  
Portfolio Management ◽  
Stock Markets ◽  
Multivariate Garch ◽  
Conditional Correlation ◽  
Financial Variables ◽  
Global Pandemic ◽  
Daily Data ◽  
Hedge Ratios ◽  
Portfolio Managers ◽  
Multivariate Garch Models

This paper aims to examine the volatility spillover, diversification benefits, and hedge ratios between U.S. stock markets and different financial variables and commodities during the pre-COVID-19 and COVID-19 crisis, using daily data and multivariate GARCH models. Our results indicate that the risk spillover has reached the highest level during the COVID-19 period, compared to the pre-COVID period, which means that the COVID-19 pandemic enforced the risk spillover between U.S. stock markets and the remains assets. We confirm the economic benefit of diversification in both tranquil and crisis periods (e.g., a negative dynamic conditional correlation between the VIX and SP500). Moreover, the hedging analysis exhibits that the Dow Jones Islamic has the highest hedging effectiveness either before or during the recent COVID19 crisis, offering better resistance to uncertainty caused by unpredictable turmoil such as the COVID19 outbreak. Our finding may have some implications for portfolio managers and investors to reduce their exposure to the risk in their portfolio construction.

scholarly journals Human-Machine Collaborative Optimization via Apprenticeship Scheduling

2018 ◽  
Vol 63 ◽  
pp. 1-49 ◽  
Author(s):  
Matthew Gombolay ◽  
Reed Jensen ◽  
Jessica Stigile ◽  
Toni Golen ◽  
Neel Shah ◽  
...  
Keyword(s):  
Decision Making ◽  
Domain Knowledge ◽  
Resource Constraints ◽  
Optimization Technique ◽  
Collaborative Optimization ◽  
Data Sets ◽  
Scheduling Problems ◽  
Real World Data ◽  
Domain Experts ◽  
Model Free

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the "single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes. We propose a new approach for capturing this decision-making process through counterfactual reasoning in pairwise comparisons. Our approach is model-free and does not require iterating through the state space. We demonstrate that this approach accurately learns multifaceted heuristics on a synthetic and real world data sets. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of schedule optimization. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates optimal solutions up to 9.5 times faster than a state-of-the-art optimization algorithm.

scholarly journals Optimal model-free prediction from multivariate time series

2015 ◽  
Vol 91 (5) ◽  
Author(s):  
Jakob Runge ◽  
Reik V. Donner ◽  
Jürgen Kurths
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Optimal Model ◽  
Model Free

scholarly journals Is the Group Structure Important in Grouped Functional Time Series?

2021 ◽  
pp. 1-22
Author(s):  
Yang Yang ◽  
Han Lin Shang
Keyword(s):  
Time Series ◽  
Group Structure ◽  
Forecast Accuracy ◽  
Joint Modeling ◽  
Multiple Series ◽  
Functional Time Series ◽  
Modeling And Forecasting ◽  
Time Series Method ◽  
Group Structures ◽  
Practical Question

We study the importance of group structure in grouped functional time series. Due to the non-uniqueness of group structure, we investigate different disaggregation structures in grouped functional time series. We address a practical question on whether or not the group structure can affect forecast accuracy. Using a dynamic multivariate functional time series method, we consider joint modeling and forecasting multiple series. Illustrated by Japanese sub-national age-specific mortality rates from 1975 to 2016, we investigate one- to 15-step-ahead point and interval forecast accuracies for the two group structures.

scholarly journals Addiction as Learned Behavior Patterns

2019 ◽  
Vol 8 (8) ◽  
pp. 1086 ◽  
Author(s):  
Andreas Heinz ◽  
Anne Beck ◽  
Melissa Gül Halil ◽  
Maximilian Pilhatsch ◽  
Michael N. Smolka ◽  
...  
Keyword(s):  
Computational Models ◽  
Instrumental Learning ◽  
Drug Intake ◽  
Learning Mechanisms ◽  
Real World Data ◽  
Drug Seeking ◽  
Model Free ◽  
Acute And Chronic Stress ◽  
Vulnerable Patients ◽  
The Impact

Individuals with substance use disorders (SUDs) have to cope with drug-related cues and contexts which can affect instrumental drug seeking, as shown with Pavlovian-to-instrumental transfer (PIT) tasks among humans and animals. Our review addresses two potential mechanisms that may contribute to habitual or even compulsive drug seeking and taking. One mechanism is represented by Pavlovian and PIT effects on drug intake. The other is a shift from goal-directed to habitual drug intake, which can be accessed via model-based versus model-free decision-making in respective learning tasks. We discuss the impact of these learning mechanisms on drug consumption. First, we describe how Pavlovian and instrumental learning mechanisms interact in drug addiction. Secondly, we address the effects of acute and chronic stress exposure on behavioral and neural PIT effects in alcohol use disorder (AUD). Thirdly, we discuss how these learning mechanisms and their respective neurobiological correlates can contribute to losing versus regaining control over drug intake. Utilizing mobile technology (mobile applications on smartphones including games that measure learning mechanisms, activity bracelets), computational models, and real-world data may help to better identify patients with a high relapse risk and to offer targeted behavioral and pharmacotherapeutic interventions for vulnerable patients.

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