Econometrics

16 pages, 353 KiB

Open AccessArticle

Detecting Common Bubbles in Multivariate Mixed Causal–Noncausal Models

by Gianluca Cubadda, Alain Hecq and Elisa Voisin

Econometrics 2023, 11(1), 9; https://doi.org/10.3390/econometrics11010009 - 09 Mar 2023

Cited by 1 | Viewed by 1773

This paper proposes concepts and methods to investigate whether the bubble patterns observed in individual time series are common among them. Having established the conditions under which common bubbles are present within the class of mixed causal–noncausal vector autoregressive models, we suggest statistical [...] Read more.

This paper proposes concepts and methods to investigate whether the bubble patterns observed in individual time series are common among them. Having established the conditions under which common bubbles are present within the class of mixed causal–noncausal vector autoregressive models, we suggest statistical tools to detect the common locally explosive dynamics in a Student t-distribution maximum likelihood framework. The performances of both likelihood ratio tests and information criteria were investigated in a Monte Carlo study. Finally, we evaluated the practical value of our approach via an empirical application on three commodity prices. Full article

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33 pages, 992 KiB

Open AccessArticle

Semi-Metric Portfolio Optimization: A New Algorithm Reducing Simultaneous Asset Shocks

by Nick James, Max Menzies and Jennifer Chan

Econometrics 2023, 11(1), 8; https://doi.org/10.3390/econometrics11010008 - 07 Mar 2023

Cited by 6 | Viewed by 2579

Abstract

This paper proposes a new method for financial portfolio optimization based on reducing simultaneous asset shocks across a collection of assets. This may be understood as an alternative approach to risk reduction in a portfolio based on a new mathematical quantity. First, we [...] Read more.

This paper proposes a new method for financial portfolio optimization based on reducing simultaneous asset shocks across a collection of assets. This may be understood as an alternative approach to risk reduction in a portfolio based on a new mathematical quantity. First, we apply recently introduced semi-metrics between finite sets to determine the distance between time series’ structural breaks. Then, we build on the classical portfolio optimization theory of Markowitz and use this distance between asset structural breaks for our penalty function, rather than portfolio variance. Our experiments are promising: on synthetic data, we show that our proposed method does indeed diversify among time series with highly similar structural breaks and enjoys advantages over existing metrics between sets. On real data, experiments illustrate that our proposed optimization method performs well relative to nine other commonly used options, producing the second-highest returns, the lowest volatility, and second-lowest drawdown. The main implication for this method in portfolio management is reducing simultaneous asset shocks and potentially sharp associated drawdowns during periods of highly similar structural breaks, such as a market crisis. Our method adds to a considerable literature of portfolio optimization techniques in econometrics and could complement these via portfolio averaging. Full article

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30 pages, 504 KiB

Open AccessArticle

Causal Vector Autoregression Enhanced with Covariance and Order Selection

by Marianna Bolla, Dongze Ye, Haoyu Wang, Renyuan Ma, Valentin Frappier, William Thompson, Catherine Donner, Máté Baranyi and Fatma Abdelkhalek

Econometrics 2023, 11(1), 7; https://doi.org/10.3390/econometrics11010007 - 24 Feb 2023

Viewed by 2072

Abstract

A causal vector autoregressive (CVAR) model is introduced for weakly stationary multivariate processes, combining a recursive directed graphical model for the contemporaneous components and a vector autoregressive model longitudinally. Block Cholesky decomposition with varying block sizes is used to solve the model equations [...] Read more.

A causal vector autoregressive (CVAR) model is introduced for weakly stationary multivariate processes, combining a recursive directed graphical model for the contemporaneous components and a vector autoregressive model longitudinally. Block Cholesky decomposition with varying block sizes is used to solve the model equations and estimate the path coefficients along a directed acyclic graph (DAG). If the DAG is decomposable, i.e., the zeros form a reducible zero pattern (RZP) in its adjacency matrix, then covariance selection is applied that assigns zeros to the corresponding path coefficients. Real-life applications are also considered, where for the optimal order

p \geq 1

of the fitted CVAR

(p)

model, order selection is performed with various information criteria. Full article

(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)

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20 pages, 428 KiB

Open AccessArticle

Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles

by Hui-Ching Chuang and Jau-er Chen

Econometrics 2023, 11(1), 6; https://doi.org/10.3390/econometrics11010006 - 14 Feb 2023

Viewed by 2290

Abstract

In this study, we explore the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR). The UQR provides better interpretative and thus policy-relevant information on the predictive effect of the target variable than the conditional quantile regression. To [...] Read more.

In this study, we explore the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR). The UQR provides better interpretative and thus policy-relevant information on the predictive effect of the target variable than the conditional quantile regression. To deal with a broad set of macroeconomic and industry variables, we use the lasso-based double selection to estimate the predictive effects of industry distress and select relevant variables. Our sample consists of 5334 debt and loan instruments in Moody’s Default and Recovery Database from 1990 to 2017. The results show that industry distress decreases recovery rates from 15.80% to 2.94% for the 15th to 55th percentile range and slightly increases the recovery rates in the lower and the upper tails. The UQR provide quantitative measurements to the loss given default during a downturn that the Basel Capital Accord requires. Full article

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37 pages, 1354 KiB

Open AccessArticle

Building Multivariate Time-Varying Smooth Transition Correlation GARCH Models, with an Application to the Four Largest Australian Banks

by Anthony D. Hall, Annastiina Silvennoinen and Timo Teräsvirta

Econometrics 2023, 11(1), 5; https://doi.org/10.3390/econometrics11010005 - 06 Feb 2023

Cited by 1 | Viewed by 2114

Abstract

This paper proposes a methodology for building Multivariate Time-Varying STCC–GARCH models. The novel contributions in this area are the specification tests related to the correlation component, the extension of the general model to allow for additional correlation regimes, and a detailed exposition of [...] Read more.

This paper proposes a methodology for building Multivariate Time-Varying STCC–GARCH models. The novel contributions in this area are the specification tests related to the correlation component, the extension of the general model to allow for additional correlation regimes, and a detailed exposition of the systematic, improved modelling cycle required for such nonlinear models. There is an R-package that includes the steps in the modelling cycle. Simulations demonstrate the robustness of the recommended model building approach. The modelling cycle is illustrated using daily return series for Australia’s four largest banks. Full article

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13 pages, 428 KiB

Open AccessFeature PaperArticle

Comparing the Conditional Logit Estimates and True Parameters under Preference Heterogeneity: A Simulated Discrete Choice Experiment

by Maksat Jumamyradov, Benjamin M. Craig, Murat Munkin and William Greene

Econometrics 2023, 11(1), 4; https://doi.org/10.3390/econometrics11010004 - 25 Jan 2023

Cited by 2 | Viewed by 2333

Abstract

Health preference research (HPR) is the subfield of health economics dedicated to understanding the value of health and health-related objects using observational or experimental methods. In a discrete choice experiment (DCE), the utility of objects in a choice set may differ systematically between [...] Read more.

Health preference research (HPR) is the subfield of health economics dedicated to understanding the value of health and health-related objects using observational or experimental methods. In a discrete choice experiment (DCE), the utility of objects in a choice set may differ systematically between persons due to interpersonal heterogeneity (e.g., brand-name medication, generic medication, no medication). To allow for interpersonal heterogeneity, choice probabilities may be described using logit functions with fixed individual-specific parameters. However, in practice, a study team may ignore heterogeneity in health preferences and estimate a conditional logit (CL) model. In this simulation study, we examine the effects of omitted variance and correlations (i.e., omitted heterogeneity) in logit parameters on the estimation of the coefficients, willingness to pay (WTP), and choice predictions. The simulated DCE results show that CL estimates may have been biased depending on the structure of the heterogeneity that we used in the data generation process. We also found that these biases in the coefficients led to a substantial difference in the true and estimated WTP (i.e., up to 20%). We further found that CL and true choice probabilities were similar to each other (i.e., difference was less than 0.08) regardless of the underlying structure. The results imply that, under preference heterogeneity, CL estimates may differ from their true means, and these differences can have substantive effects on the WTP estimates. More specifically, CL WTP estimates may be underestimated due to interpersonal heterogeneity, and a failure to recognize this bias in HPR indirectly underestimates the value of treatment, substantially reducing quality of care. These findings have important implications in health economics because CL remains widely used in practice. Full article

(This article belongs to the Special Issue Health Econometrics)

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2 pages, 158 KiB

Open AccessEditorial

Acknowledgment to the Reviewers of Econometrics in 2022

by Econometrics Editorial Office

Econometrics 2023, 11(1), 3; https://doi.org/10.3390/econometrics11010003 - 19 Jan 2023

Viewed by 1077

Abstract

High-quality academic publishing is built on rigorous peer review [...] Full article

29 pages, 6426 KiB

Open AccessArticle

Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers

by Graziano Moramarco

Econometrics 2023, 11(1), 2; https://doi.org/10.3390/econometrics11010002 - 28 Dec 2022

Cited by 2 | Viewed by 4723

Abstract

We propose an approach for jointly measuring global macroeconomic uncertainty and bilateral spillovers of uncertainty between countries using a global vector autoregressive (GVAR) model. Over the period 2000Q1–2020Q4, our global index is able to summarize a variety of uncertainty measures, such as financial-market [...] Read more.

We propose an approach for jointly measuring global macroeconomic uncertainty and bilateral spillovers of uncertainty between countries using a global vector autoregressive (GVAR) model. Over the period 2000Q1–2020Q4, our global index is able to summarize a variety of uncertainty measures, such as financial-market volatility, economic-policy uncertainty, survey-forecast-based measures and econometric measures of macroeconomic uncertainty, showing major peaks during both the global financial crisis and the COVID-19 pandemic. Global spillover effects are quantified through a novel GVAR-based decomposition of country-level uncertainty into the contributions from all countries in the global model. We show that this approach produces estimates of uncertainty spillovers which are strongly related to the structure of the global economy. Full article

(This article belongs to the Special Issue Special Issue on Time Series Econometrics)

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18 pages, 905 KiB

Open AccessArticle

Maximum Likelihood Inference for Asymmetric Stochastic Volatility Models

by Omar Abbara and Mauricio Zevallos

Econometrics 2023, 11(1), 1; https://doi.org/10.3390/econometrics11010001 - 23 Dec 2022

Cited by 1 | Viewed by 1459

Abstract

In this paper, we propose a new method for estimating and forecasting asymmetric stochastic volatility models. The proposal is based on dynamic linear models with Markov switching written as state space models. Then, the likelihood is calculated through Kalman filter outputs and the [...] Read more.

In this paper, we propose a new method for estimating and forecasting asymmetric stochastic volatility models. The proposal is based on dynamic linear models with Markov switching written as state space models. Then, the likelihood is calculated through Kalman filter outputs and the estimates are obtained by the maximum likelihood method. Monte Carlo experiments are performed to assess the quality of estimation. In addition, a backtesting exercise with the real-life time series illustrates that the proposed method is a quick and accurate alternative for forecasting value-at-risk. Full article

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Econometrics, Volume 11, Issue 1 (March 2023) – 9 articles

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