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on Econometric Time Series |
| By: | Luca Vincenzo Ballestra; Enzo D'Innocenzo; Christian Tezza |
| Abstract: | Christoffersen, Jacobs, Ornthanalai, and Wang (2008) (CJOW) proposed an improved Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for valuing European options, where the return volatility is comprised of two distinct components. Empirical studies indicate that the model developed by CJOW outperforms widely-used single-component GARCH models and provides a superior fit to options data than models that combine conditional heteroskedasticity with Poisson-normal jumps. However, a significant limitation of this model is that it allows the variance process to become negative. Oh and Park [2023] partially addressed this issue by developing a related model, yet the positivity of the volatility components is not guaranteed, both theoretically and empirically. In this paper we introduce a new GARCH model that improves upon the models by CJOW and Oh and Park [2023], ensuring the positivity of the return volatility. In comparison to the two earlier GARCH approaches, our novel methodology shows comparable in-sample performance on returns data and superior performance on S&P500 options data. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.14513 |
| By: | Farley Ishaak; Peng Liu; Egbert Hardeman; Hilde Remoy |
| Abstract: | Recent literature has shown how dynamic factor models (DFM) can be used successfully to predict real estate price returns. In this paper, we take it a step further. In a two-step approach we estimate (1) a dynamic factor model over multiple markets to extract a few common trends, and (2) estimate a per-market Autoregressive Distributed Lag (ARDL) model including the dynamic factors, in a LASSO framework. In total we estimate 7 different variants (for example by also utilizing macroeconomic explanatory variables) of this model for rents and prices for a selection of Polish cities. Compared to a vanilla ARDL model, our LASSO-DFM augmented ARDL, reduces the prediction error by more than 60% on average. What is more, the prediction errors are relatively "stable." With this we mean that the size of the error is comparable over time and over markets, without any large outliers. This holds true even for forecasts over very long horizons. |
| Keywords: | Autoregressive Distributed Lag; LASSO; Poland |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-207 |
| By: | Yannick Hoga |
| Abstract: | Forecasting risk (as measured by quantiles) and systemic risk (as measured by Adrian and Brunnermeiers's (2016) CoVaR) is important in economics and finance. However, past research has shown that predictive relationships may be unstable over time. Therefore, this paper develops structural break tests in predictive quantile and CoVaR regressions. These tests can detect changes in the forecasting power of covariates, and are based on the principle of self-normalization. We show that our tests are valid irrespective of whether the predictors are stationary or near-stationary, rendering the tests suitable for a range of practical applications. Simulations illustrate the good finite-sample properties of our tests. Two empirical applications concerning equity premium and systemic risk forecasting models show the usefulness of the tests. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.05861 |
| By: | Anubha Goel; Puneet Pasricha; Juho Kanniainen |
| Abstract: | This study is the first to explore the application of a time-series foundation model for VaR estimation. Foundation models, pre-trained on vast and varied datasets, can be used in a zero-shot setting with relatively minimal data or further improved through finetuning. We compare the performance of Google's model, called TimesFM, against conventional parametric and non-parametric models, including GARCH, Generalized Autoregressive Score (GAS), and empirical quantile estimates, using daily returns from the S\&P 100 index and its constituents over 19 years. Our backtesting results indicate that, in terms of the actual-over-expected ratio, the fine-tuned TimesFM model consistently outperforms traditional methods. Regarding the quantile score loss function, it achieves performance comparable to the best econometric approach, the GAS model. Overall, the foundation model is either the best or among the top performers in forecasting VaR across the 0.01, 0.025, 0.05, and 0.1 VaR levels. We also found that fine-tuning significantly improves the results, and the model should not be used in zero-shot settings. Overall, foundation models can provide completely alternative approaches to traditional econometric methods, yet there are challenges to be tackled. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.11773 |
| By: | Pulikandala Nithish Kumar; Nneka Umeorah; Alex Alochukwu |
| Abstract: | Volatility forecasting is essential for risk management and decision-making in financial markets. Traditional models like Generalized Autoregressive Conditional Heteroskedasticity (GARCH) effectively capture volatility clustering but often fail to model complex, non-linear interdependencies between multiple indices. This paper proposes a novel approach using Graph Neural Networks (GNNs) to represent global financial markets as dynamic graphs. The Temporal Graph Attention Network (Temporal GAT) combines Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) to capture the temporal and structural dynamics of volatility spillovers. By utilizing correlation-based and volatility spillover indices, the Temporal GAT constructs directed graphs that enhance the accuracy of volatility predictions. Empirical results from a 15-year study of eight major global indices show that the Temporal GAT outperforms traditional GARCH models and other machine learning methods, particularly in short- to mid-term forecasts. The sensitivity and scenario-based analysis over a range of parameters and hyperparameters further demonstrate the significance of the proposed technique. Hence, this work highlights the potential of GNNs in modeling complex market behaviors, providing valuable insights for financial analysts and investors. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.16858 |
| By: | Ter Steege, Lucas |
| Abstract: | We study the application of approximate mean field variational inference algorithms to Bayesian panel VAR models in which an exchangeable prior is placed on the dynamic parameters and the residuals follow either a Gaussian or a Student-t distribution. This reduces the estimation time of possibly several hours using conventional MCMC methods to less than a minute using variational inference algorithms. Next to considerable speed improvements, our results show that the approximations accurately capture the dynamic effects of macroeconomic shocks as well as overall parameter uncertainty. The application with Student-t residuals shows that it is computationally easy to include the COVID-19 observations in Bayesian panel VARs, thus offering a fast way to estimate such models. JEL Classification: C18, C32, C33 |
| Keywords: | panel-VAR, student-t distribution, variational Bayes |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20242991 |
| By: | Sohyeon Kwon; Yongjae Lee |
| Abstract: | In the financial sector, a sophisticated financial time series simulator is essential for evaluating financial products and investment strategies. Traditional back-testing methods have mainly relied on historical data-driven approaches or mathematical model-driven approaches, such as various stochastic processes. However, in the current era of AI, data-driven approaches, where models learn the intrinsic characteristics of data directly, have emerged as promising techniques. Generative Adversarial Networks (GANs) have surfaced as promising generative models, capturing data distributions through adversarial learning. Financial time series, characterized 'stylized facts' such as random walks, mean-reverting patterns, unexpected jumps, and time-varying volatility, present significant challenges for deep neural networks to learn their intrinsic characteristics. This study examines the ability of GANs to learn diverse and complex temporal patterns (i.e., stylized facts) of both univariate and multivariate financial time series. Our extensive experiments revealed that GANs can capture various stylized facts of financial time series, but their performance varies significantly depending on the choice of generator architecture. This suggests that naively applying GANs might not effectively capture the intricate characteristics inherent in financial time series, highlighting the importance of carefully considering and validating the modeling choices. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.09850 |