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on Econometric Time Series |
| By: | Dennis Koch Vahidin Jeleskovic; Zahid I. Younas |
| Abstract: | This paper introduces a unique and valuable research design aimed at analyzing Bitcoin price volatility. To achieve this, a range of models from the Markov Switching-GARCH and Stochastic Autoregressive Volatility (SARV) model classes are considered and their out-of-sample forecasting performance is thoroughly examined. The paper provides insights into the rationale behind the recommendation for a two-stage estimation approach, emphasizing the separate estimation of coefficients in the mean and variance equations. The results presented in this paper indicate that Stochastic Volatility models, particularly SARV models, outperform MS-GARCH models in forecasting Bitcoin price volatility. Moreover, the study suggests that in certain situations, persistent simple GARCH models may even outperform Markov-Switching GARCH models in predicting the variance of Bitcoin log returns. These findings offer valuable guidance for risk management experts, highlighting the potential advantages of SARV models in managing and forecasting Bitcoin price volatility. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.03393&r=ets |
| By: | Chenlei Leng; Degui Li; Hamlin Shang; Yingcun Xia |
| Abstract: | We propose a flexible dual functional factor model for modelling high-dimensional functional time series. In this model, a high-dimensional fully functional factor parametrisation is imposed on the observed functional processes, whereas a low-dimensional version (via series approximation) is assumed for the latent functional factors. We extend the classic principal component analysis technique for the estimation of a low-rank structure to the estimation of a large covariance matrix of random functions that satisfies a notion of (approximate) functional "low-rank plus sparse" structure; and generalise the matrix shrinkage method to functional shrinkage in order to estimate the sparse structure of functional idiosyncratic components. Under appropriate regularity conditions, we derive the large sample theory of the developed estimators, including the consistency of the estimated factors and functional factor loadings and the convergence rates of the estimated matrices of covariance functions measured by various (functional) matrix norms. Consistent selection of the number of factors and a data-driven rule to choose the shrinkage parameter are discussed. Simulation and empirical studies are provided to demonstrate the finite-sample performance of the developed model and estimation methodology. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05784&r=ets |
| By: | Parley R Yang; Alexander Y Shestopaloff |
| Abstract: | Vector Error Correction Model (VECM) is a classic method to analyse cointegration relationships amongst multivariate non-stationary time series. In this paper, we focus on high dimensional setting and seek for sample-size-efficient methodology to determine the level of cointegration. Our investigation centres at a Bayesian approach to analyse the cointegration matrix, henceforth determining the cointegration rank. We design two algorithms and implement them on simulated examples, yielding promising results particularly when dealing with high number of variables and relatively low number of observations. Furthermore, we extend this methodology to empirically investigate the constituents of the S&P 500 index, where low-volatility portfolios can be found during both in-sample training and out-of-sample testing periods. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.17061&r=ets |
| By: | Jad Beyhum |
| Abstract: | We study a new model where the potential outcomes, corresponding to the values of a (possibly continuous) treatment, are linked through common factors. The factors can be estimated using a panel of regressors. We propose a procedure to estimate time-specific and unit-specific average marginal effects in this context. Our approach can be used either with high-dimensional time series or with large panels. It allows for treatment effects heterogenous across time and units and is straightforward to implement since it only relies on principal components analysis and elementary computations. We derive the asymptotic distribution of our estimator of the average marginal effect and highlight its solid finite sample performance through a simulation exercise. The approach can also be used to estimate average counterfactuals or adapted to an instrumental variables setting and we discuss these extensions. Finally, we illustrate our novel methodology through an empirical application on income inequality. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.03293&r=ets |
| By: | Da Huo, Da |
| Abstract: | This thesis presents a novel rolling GLS-based model to improve the precision of time-varying parameter estimates in dynamic linear models. Through rigorous simulations, the rolling GLS model exhibits enhanced accuracy in scenarios with smaller sample sizes and maintains its efficacy when the normality assumption is relaxed, distinguishing it from traditional models like Kalman Filters. Furthermore, the thesis expands on the model to tackle more complex stochastic structures and validates its effectiveness through practical applications to real-world financial data, like inflation risk premium estimations. The research culminates in offering a robust tool for financial econometrics, enhancing the reliability of financial analyses and predictions. |
| Keywords: | Time Series Analysis, Dynamic Linear Model, Stochastic Parameters, Least Squares |
| JEL: | C13 C22 C32 C58 G11 G12 |
| Date: | 2024–01–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119731&r=ets |
| By: | Katerina Petrova |
| Abstract: | This paper studies large sample classical and Bayesian inference in a prototypical linear DSGE model and demonstrates that inference on the structural parameters based on a Gaussian likelihood is unaffected by departures from Gaussianity of the structural shocks. This surprising result is due to a cancellation in the asymptotic variance resulting into a generalized information equality for the block corresponding to the structural parameters. The underlying reason for the cancellation is the certainty equivalence property of the linear rational expectation model. The main implication of this result is that classical and Bayesian Gaussian inference achieve a semi-parametric efficiency bound and there is no need for a “sandwich-form” correction of the asymptotic variance of the structural parameters. Consequently, MLE-based confidence intervals and Bayesian credible sets of the deep parameters based on a Gaussian likelihood have correct asymptotic coverage even when the structural shocks are non-Gaussian. On the other hand, inference on the reduced-form parameters characterizing the volatility of the shocks is invalid whenever the structural shocks have a non-Gaussian density and the paper proposes a simple Metropolis-within-Gibbs algorithm that achieves correct large sample inference for the volatility parameters. |
| Keywords: | DSGE models; generalized information equality; sandwich form covariance |
| JEL: | C11 C12 C22 |
| Date: | 2024–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:97624&r=ets |
| By: | Allayioti, Anastasia; Venditti, Fabrizio |
| Abstract: | Commodity prices co-move, but the strength of this co-movement changes over time due to structural factors, like changing energy intensity in production and consumption as well as changing composition of underlying shocks. This paper explores whether econometric models that exploit this co-movement and account for parameter instability provide more accurate point and density forecasts of ten major commodity indices viz-a-viz constant coefficient models. Improvements in point forecast accuracy are small, with predictability varying substantially across forecast horizons and commodity indices, but they are large and significant in terms of density forecasting. An economic evaluation reveals that allowing for parameter time variation and commonalities leads to higher portfolios returns, and to higher utility values for investors. JEL Classification: C32, C52, C53, C55, E37 |
| Keywords: | commodities, commonalities, density forecasting, economic evaluation, instabilities |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20242901&r=ets |
| By: | Shun Liu; Kexin Wu; Chufeng Jiang; Bin Huang; Danqing Ma |
| Abstract: | In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.00534&r=ets |
| By: | Brahmana, Rayenda Khresna |
| Abstract: | The emergence of cryptocurrencies as digital investments drives scholars to explore their predictive prices. Intriguingly, most research focuses on its price and returns prediction using various models, leaving out the importance of persistent risk for portfolio management. This is not to mention that most research focuses only on Bitcoin, neglecting other altcoins and stablecoins. Therefore, this study comprehensively examines the cryptocurrency investment’s persistent risk from the forecasting point of view. We focus on comparing the best forecasting methods because they are vital for volatility-targeting and risk-parity in portfolio strategy. Four time-series model performances will be compared to select a suitable volatility prediction model: Machine Learning-Based GARCH, Machine Learning-Based SVR-GARCH, Neural Network, and Deep Learning. Using six different cryptocurrencies proxies: Bitcoin, Ethereum, Ripple, USD Coin, Tether, and Binance Coin, we found that ML-Based SVR-GARCH outperformed the peers in volatility forecasting. However, the prediction accuracy differences among all models are not significant. Finally, our paper provides new insights into machine learning methods’ applications in cryptocurrency market volatility prediction, which is helpful for academics, policy-makers, and investors in forming portfolio strategies. |
| Keywords: | Volatility Forecasting; Cryptocurrencies; Bitcoin; SVR-GARCH; Neural Network; Deep Learning |
| JEL: | C53 G17 G32 |
| Date: | 2022–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119598&r=ets |
| By: | Cristina Chinazzo; Vahidin Jeleskovic |
| Abstract: | This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measures of volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it's crucial to emphasize the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme moneyness or maturity. This analysis provides valuable insights into Bitcoin's volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.02049&r=ets |