How does stock market volatility react to oil shocks?

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1. Apparenté Pas pertinent

   Understanding Volatility Spillover Relationship Among G7 Nations And India During Covid-19

   Avik Das, Dr. Devanjali Nandi Das · 2022 · International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 6, pp.c261-c269, June 2022 · Résumé IA disponible

   Résumé scientifique

   Purpose: In the context of a COVID pandemic in 2020-21, this paper attempts to capture the interconnectedness and volatility transmission dynamics. The nature of change in volatility spillover effects and time-varying conditional correlation among the G7 countries and India is investigated. Methodology: To assess the volatility spillover effects, the bivariate BEKK and t- DCC (1,1) GARCH (1,1) models have been used. Our research shows how the dynamics of volatility spillover between India and the G7 countries shift before and during COVID-19. Findings: The findings reveal that the extent of volatility spillover has altered during COVID compared to the pre-COVID environment. During this pande…

2. Apparenté Pas pertinent

   Modeling Macroeconomic Variations After COVID-19

   Serena Ng · 2021 · Économétrie · Résumé IA disponible

   Résumé scientifique

   The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many pre-covid forecasting models inadequate. It also creates a new problem for estimation of economic factors and dynamic causal effects because the variations around the outbreak can be interpreted as outliers, as shifts to the distribution of existing shocks, or as addition of new shocks. I take the latter view and use covid indicators as controls to 'de-covid' the data prior to estimation. I find that economic uncertainty remains high at the end of 2020 even though real economic activity has recovered and covid uncertainty has receded. Dynamic…

3. Apparenté Pas pertinent

   Deep Learning Macroeconomics

   Rafael R. S. Guimaraes · 2022 · Économétrie · Résumé IA disponible

   Résumé scientifique

   Limited datasets and complex nonlinear relationships are among the challenges that may emerge when applying econometrics to macroeconomic problems. This research proposes deep learning as an approach to transfer learning in the former case and to map relationships between variables in the latter case. Although macroeconomists already apply transfer learning when assuming a given a priori distribution in a Bayesian context, estimating a structural VAR with signal restriction and calibrating parameters based on results observed in other models, to name a few examples, advance in a more systematic transfer learning strategy in applied macroeconomics is the innovation we are introducing. We expl…

4. Apparenté Pas pertinent

   A Descriptive Method of Firm Size Transition Dynamics Using Markov Chain

   Boyang You, Kerry Papps · 2022 · Économétrie · Résumé IA disponible

   Résumé scientifique

   Social employment, which is mostly carried by firms of different types, determines the prosperity and stability of a country. As time passing, the fluctuations of firm employment can reflect the process of creating or destroying jobs. Therefore, it is instructive to investigate the firm employment (size) dynamics. Drawing on the firm-level panel data extracted from the Chinese Industrial Enterprises Database 1998-2013, this paper proposes a Markov-chain-based descriptive approach to clearly demonstrate the firm size transfer dynamics between different size categories. With this method, any firm size transition path in a short time period can be intuitively demonstrated. Furthermore, by utili…

5. Apparenté Pas pertinent

   Robust decision-making under risk and ambiguity

   Maximilian Blesch, Philipp Eisenhauer · 2021 · Économétrie · Résumé IA disponible

   Résumé scientifique

   Economists often estimate economic models on data and use the point estimates as a stand-in for the truth when studying the model's implications for optimal decision-making. This practice ignores model ambiguity, exposes the decision problem to misspecification, and ultimately leads to post-decision disappointment. Using statistical decision theory, we develop a framework to explore, evaluate, and optimize robust decision rules that explicitly account for estimation uncertainty. We show how to operationalize our analysis by studying robust decisions in a stochastic dynamic investment model in which a decision-maker directly accounts for uncertainty in the model's transition dynamics.

6. Apparenté Pas pertinent

   On Bayesian Filtering for Markov Regime Switching Models

   Nigar Hashimzade, Oleg Kirsanov, Tatiana Kirsanova et al. · 2024 · Économétrie · Résumé IA disponible

   Résumé scientifique

   This paper presents a framework for empirical analysis of dynamic macroeconomic models using Bayesian filtering, with a specific focus on the state-space formulation of Dynamic Stochastic General Equilibrium (DSGE) models with multiple regimes. We outline the theoretical foundations of model estimation, provide the details of two families of powerful multiple-regime filters, IMM and GPB, and construct corresponding multiple-regime smoothers. A simulation exercise, based on a prototypical New Keynesian DSGE model, is used to demonstrate the computational robustness of the proposed filters and smoothers and evaluate their accuracy and speed for a selection of filters from each family. We show …

7. Apparenté Pas pertinent

   Equity in Startups

   Hervé Lebret · 2017 · Économétrie · Pas encore résumé

   Résumé scientifique

   Startups have become in less than 50 years a major component of innovation and economic growth. An important feature of the startup phenomenon has been the wealth created through equity in startups to all stakeholders. These include the startup founders, the investors, and also the employees through the stock-option mechanism and universities through licenses of intellectual property. In the employee group, the allocation to important managers like the chief executive, vice-presidents and other officers, and independent board members is also analyzed. This report analyzes how equity was allocated in more than 400 startups, most of which had filed for an initial public offering. The author ha…

8. Apparenté Pas pertinent

   Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job Displacement

   Brantly Callaway · 2020 · Économétrie · Résumé IA disponible

   Résumé scientifique

   This paper develops new techniques to bound distributional treatment effect parameters that depend on the joint distribution of potential outcomes -- an object not identified by standard identifying assumptions such as selection on observables or even when treatment is randomly assigned. I show that panel data and an additional assumption on the dependence between untreated potential outcomes for the treated group over time (i) provide more identifying power for distributional treatment effect parameters than existing bounds and (ii) provide a more plausible set of conditions than existing methods that obtain point identification. I apply these bounds to study heterogeneity in the effect of …