nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒01‒31
seven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Optimal Out-of-Sample Forecast Evaluation under Stationarity By Filip Stanek
  2. Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models By Andras Fulop; Jeremy Heng; Junye Li
  3. Modelling the volatility of Bitcoin returns using Nonparametric GARCH models By Mestiri, Sami
  4. Modelling of Daily Price Volatility of South Africa Property Stock Market Using GARCH Analysis By Tosin B. Fateye; Oluwaseun D. Ajay; Cyril A. Ajay
  5. Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market) By Vladimir Pyrlik; Pavel Elizarov; Aleksandra Leonova
  6. Machine Learning Based Semiparametric Time Series Conditional Variance: Estimation and Forecasting By Justin Dang; Aman Ullah
  7. Spread Option Pricing in a Copula Affine GARCH(p,q) Model By Edoardo Berton; Lorenzo Mercuri

  1. By: Filip Stanek
    Abstract: It is common practice to split time-series into in-sample and pseudo out-of-sample segments and to estimate the out-of-sample loss of a given statistical model by evaluating forecasting performance over the pseudo out-of-sample segment. We propose an alternative estimator of the out-of-sample loss which, contrary to conventional wisdom, utilizes both measured in-sample and out-of-sample performance via a carefully constructed system of affine weights. We prove that, provided that the time-series is stationary, the proposed estimator is the best linear unbiased estimator of the out-of-sample loss and outperforms the conventional estimator in terms of sampling variance. Applying the optimal estimator to Diebold-Mariano type tests of predictive ability leads to a substantial power gain without worsening finite sample level distortions. An extensive evaluation on real world time-series from the M4 forecasting competition confirms the superiority of the proposed estimator and also demonstrates a substantial robustness to the violation of the underlying assumption of stationarity.
    Keywords: loss estimation; forecast evaluation; cross-validation; model selection;
    JEL: C22 C52 C53
    Date: 2021–11
  2. By: Andras Fulop; Jeremy Heng; Junye Li
    Abstract: Most solved dynamic structural macrofinance models are non-linear and/or non-Gaussian state-space models with high-dimensional and complex structures. We propose an annealed controlled sequential Monte Carlo method that delivers numerically stable and low variance estimators of the likelihood function. The method relies on an annealing procedure to gradually introduce information from observations and constructs globally optimal proposal distributions by solving associated optimal control problems that yield zero variance likelihood estimators. To perform parameter inference, we develop a new adaptive SMC$^2$ algorithm that employs likelihood estimators from annealed controlled sequential Monte Carlo. We provide a theoretical stability analysis that elucidates the advantages of our methodology and asymptotic results concerning the consistency and convergence rates of our SMC$^2$ estimators. We illustrate the strengths of our proposed methodology by estimating two popular macrofinance models: a non-linear new Keynesian dynamic stochastic general equilibrium model and a non-linear non-Gaussian consumption-based long-run risk model.
    Date: 2022–01
  3. By: Mestiri, Sami
    Abstract: Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. The use of parametric GARCH models to characterise the volatility of Bitcoin returns is widely observed in the empirical literature. In this paper, we consider an alternative approach involving non-parametric method to model and forecast Bitcoin return volatility. We show that the out-of-sample volatility forecast of the non-parametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. The improvement in forecasting accuracy of Bitcoin return volatility based on the non-parametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models.
    Keywords: Bitcoin; volatility; GARCH; Nonparametric; Forecasting.
    JEL: C14 C53 C58
    Date: 2021–12–13
  4. By: Tosin B. Fateye; Oluwaseun D. Ajay; Cyril A. Ajay
    Abstract: Purpose: The study examined the volatility of the daily market price of listed property stocks on the Johannesburg Stock Exchange (JSE) for a 10year period (2008-2017). The primary aim of the study is to investigate the volatility pattern of the daily market price; in an attempt to document and model the nature of volatility characterised by the daily price of the listed property stock market for informed investment decision making.Design/Methodology/Approach: The study used daily prices from January 2, 2008, to December 29, 2017 of twelve (12) quoted property companies out of the twenty-seven (27) listed on Johannesburg Stock Exchange (SA REIT Association, 2020). The property stocks were selected based on the quoted property companies that have sufficient published data on daily prices for the period under review. The data were obtained from the JSE published statistical bulletin. The study computed the average daily price of the selected (12) property stocks and was used as a proxy for the daily market price for the property stock market in the analysis. The study deployed mean, standard deviation, maximum and minimum analytical tools for descriptive statistics, Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS); Jarque-Bera, Breusch-Godfrey LM and Heteroskedasticity tests for unit root, normal distribution, autocorrelation, and ARCH effect tests respectively. The diversification benefits and modelling of SA-REIT market price volatility were analysed using correlation matrix and generalised autoregressive conditional heteroskedasticity (GARCH 1, 1)Findings: Analysis of residual estimate of the series documents the evidence of volatility characterised by prolonged high and low clustering patterns for the period under review. The GARCH model reported that the previous day's information of both the daily market price (ARCH term) and the volatility (GARCH term) have a positive and significant (p
    Keywords: GARCH; Model; Property stock; Stock market; Volatility
    JEL: R3
    Date: 2021–09–01
  5. By: Vladimir Pyrlik; Pavel Elizarov; Aleksandra Leonova
    Abstract: We assess the performance of selected machine learning algorithms (lasso, random forest, gradient boosting, and long short-term memory) in forecasting the daily realized volatility of returns of selected top stocks in the Russian stock market in comparison with a heterogeneous autoregressive realized volatility benchmark in 2018-2020. We seek to improve the predictive power of the models by including various economic indicators that carry information about future volatility. We find that lasso delivers a good combination of easy implementation and forecast precision. The other algorithms require fine-tuning and frequent re-training, otherwise they are likely to fail to outperform the benchmark often enough. Only the basic lagged log-RV values are significant explanatory variables in terms of the benchmark in-sample quality. Many economic indicators of mixed frequencies improve the predictive power of lasso though, including calendar and overnight effects, financial spillovers from local and global markets, and various macroeconomics indicators.
    Keywords: heterogeneous autoregressive model; machine learning; lasso; gradient boosting; random forest; long short-term memory; realized volatility; Russian stock market; mixed-frequency data;
    Date: 2021–11
  6. By: Justin Dang (UCR); Aman Ullah (Department of Economics, University of California Riverside)
    Abstract: This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel based machine learning algorithm, known as kernel regularized least squares estimator, is used to estimate the nonparametric component. We discuss how to estimate the semiparametric estimator using real data and how to use this estimator to make forecasts for the conditional variance.Simulations are conducted to show the dominance of the proposed estimator in terms of mean squared error. An empirical application using S&P 500 daily returns is analyzed, and the semiparametric estimator effectively forecasts future volatility.
    Keywords: Conditional variance; Nonparametric estimator; Semiparametric models; Forecasting; Machine Learning
    JEL: C01 C14 C51
    Date: 2021–01
  7. By: Edoardo Berton; Lorenzo Mercuri
    Abstract: In this study, we construct a bivariate market model combining the copula function with the affine GARCH(p,q) process used to describe the marginal dynamics of the log price. We then provide a numerical procedure for pricing European spread option contracts. To assess the accuracy of our approach we present a comparison with the Monte Carlo simulation method.
    Date: 2021–12

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