Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series

• URL: http://arxiv.org/abs/2304.03069v2
• Date: Wed, 12 Apr 2023 14:12:45 GMT
• Title: Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series
• Authors: Jarek Duda
• Abstract summary: We will focus on recently proposed philosophy of moving estimator. $F_t=sum_taut (1-eta)t-tau ln(rho_theta (x_tau))$ moving log-likelihood, evolving in time. Student's t-distribution, popular especially in economical applications, here applied to log-returns of DJIA companies.
• Score: 0.8702432681310399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The real life time series are usually nonstationary, bringing a difficult question of model adaptation. Classical approaches like ARMA-ARCH assume arbitrary type of dependence. To avoid such bias, we will focus on recently proposed agnostic philosophy of moving estimator: in time $t$ finding parameters optimizing e.g. $F_t=\sum_{\tau<t} (1-\eta)^{t-\tau} \ln(\rho_\theta (x_\tau))$ moving log-likelihood, evolving in time. It allows for example to estimate parameters using inexpensive exponential moving averages (EMA), like absolute central moments $E[|x-\mu|^p]$ evolving for one or multiple powers $p\in\mathbb{R}^+$ using $m_{p,t+1} = m_{p,t} + \eta (|x_t-\mu_t|^p-m_{p,t})$. Application of such general adaptive methods of moments will be presented on Student's t-distribution, popular especially in economical applications, here applied to log-returns of DJIA companies. While standard ARMA-ARCH approaches provide evolution of $\mu$ and $\sigma$, here we also get evolution of $\nu$ describing $\rho(x)\sim |x|^{-\nu-1}$ tail shape, probability of extreme events - which might turn out catastrophic, destabilizing the market.

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