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on Econometric Time Series |
| By: | Kanchana Nadarajah; Gael M Martin; Donald S Poskitt |
| Abstract: | We use the jackknife to bias correct the log-periodogram regression (LPR) estimator of the fractional parameter in a stationary fractionally integrated model. The weights for the jackknife estimator are chosen in such a way that bias reduction is achieved without the usual increase in asymptotic variance, with the estimator viewed as `optimal' in this sense. The theoretical results are valid under both the non-overlapping and moving-block sub-sampling schemes that can be used in the jackknife technique, and do not require the assumption of Gaussianity for the data generating process. A Monte Carlo study explores the Önite sample performance of different versions of the optimal jackknife estimator under a variety of fractional data generating processes. The simulations reveal that when the weights are constructed using the true parameter values, a version of the optimal jackknife estimator almost always out-performs alternative bias-corrected estimators. A feasible version of the jackknife estimator, in which the weights are constructed using consistent estimators of the unknown parameters, whilst not dominant overall, is still the least biased estimator in some cases. |
| Keywords: | Long memory; bias adjustment, cumulants, discrete Fourier transform, periodograms, log-periodogram regression. |
| JEL: | C18 C22 C52 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2019-7&r=all |
| By: | Arnab Chakrabarti; Rituparna Sen |
| Abstract: | Copula is a powerful tool to model multivariate data. Due to its several merits Copula modelling has become one of the most widely used methods to model financial data. We discuss the problem of modelling intraday financial data through Copula. The problem originates due to the nonsynchronous nature of intraday financial data whereas to estimate the Copula, we need synchronous observations. We show that this problem may lead to serious underestimation of the Copula parameter. We propose a modification to obtain a consistent estimator in case of Elliptical Copula or to reduce the bias significantly in case of general copulas. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1904.10182&r=all |
| By: | Muhamad Irzal (Ministry of Trade of Republic of Indonesia); Kiki Verico (Institute for Economic and Social Research, Faculty of Economics and Business, Universitas Indonesia) |
| Abstract: | This paper attempts to analyze economic integration of China and Southeast Asian countries. This paper adopts several methods: One, stationarity for correlation, Error Correction Model (ECM) for short-run relation and Cointegration for long-run relations. Two, Structural Vector Autoregression (SVAR) analysis to identify the cause and impact. As stock market index follows real sector performance this paper utilizes: One, elasticity analysis of economic growth between China and these countries as a proxy for real sector economic relations between them and two, descriptive statistical analysis on real effective exchange rate as well as Current Account Balance as a proxy of external economic performance between them. In correlation analysis, this paper found that one, stationarity of each country is difference at level; two, short-run economic relations (ECM) between China and these Southeast Asian countries and three, they have long-run economic relations. In causality, this paper found that China affects all of these Southeast Asian countries and no causality between Singapore and Philippines. In term of real sector analysis this paper found that, one, economic growth in China signi?cantly affects all of these countries’ economic growth. Two, external economic performance of these countries are difference with special ?nding on Indonesia’s current account. |
| Keywords: | Time-Series Model — stock market integration — foreign exchange — elasticity of economic growth — current account balance — China and Southeast Asia Economy of Indonesia, Malaysia, Singapore, Thailand, & Philippines |
| JEL: | C32 F36 F31 F43 F32 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:lpe:wpaper:201934&r=all |
| By: | Bonino-Gayoso, Nicolás; García-Hiernaux, Alfredo |
| Abstract: | This paper tackles the mixed-frequency modeling problem from a new perspective. Instead of drawing upon the common distributed lag polynomial model, we use a transfer function representation to develop a new type of models, named TF-MIDAS. We derive the theoretical TF-MIDAS implied by the high-frequency VARMA family models and as a function of the aggregation scheme (flow and stock). This exact correspondence leads to potential gains in terms of nowcasting and forecasting performance against the current alternatives. A Monte Carlo simulation exercise confirms that TF-MIDAS beats UMIDAS models in terms of out-of-sample nowcasting performance for several data generating high-frequency processes. |
| Keywords: | Mixed-Frequency models, TF-MIDAS, U-MIDAS, Nowcasting, Forecasting |
| JEL: | C18 C51 C53 |
| Date: | 2019–03–30 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:93366&r=all |
| By: | Azeez, Rasheed Oluwaseyi |
| Abstract: | Studies have been done on oil price volatility spillover effects on the prices of food in both pre-crisis and post-crisis periods. However, what has been sparingly studied is oil price volatility spillover effects on urban prices of food and rural prices of food. The disparity in the rural-urban spending in Nigeria is an area that can further be explored by evaluating the effects of oil price volatility spillover on prices of food in these areas. This study therefore adopts GARCH (1, 1)-TY model to evaluate the impulse response function and variance decomposition of these effects on prices of food in pre-crisis and post-crisis periods. Findings show that in full sample and post-crisis periods both aggregate price of food (APF) and urban average price of food (APFU) positively respond to oil price shocks while rural average price of food (APFR) responds negatively to oil price shocks. However, the response of the urban average price of food proves to be more significant in the post-crisis periods as it appears relatively most affected in this period by a greater percentage of oil price shocks. |
| Keywords: | GARCH (1, 1), TY, APF, APFR, APFU, Oil price volatility spillover, Impulse Response Function, Variance Decomposition |
| JEL: | Q18 Q3 Q31 Q43 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:93188&r=all |
| By: | Peter Carr; Andrey Itkin |
| Abstract: | In this paper we apply Markovian approximation of the fractional Brownian motion (BM), known as the Dobric-Ojeda (DO) process, to the fractional stochastic volatility model where the instantaneous variance is modelled by a lognormal process with drift and fractional diffusion. Since the DO process is a semi-martingale, it can be represented as an \Ito diffusion. It turns out that in this framework the process for the spot price $S_t$ is a geometric BM with stochastic instantaneous volatility $\sigma_t$, the process for $\sigma_t$ is also a geometric BM with stochastic speed of mean reversion and time-dependent colatility of volatility, and the supplementary process $\calV_t$ is the Ornstein-Uhlenbeck process with time-dependent coefficients, and is also a function of the Hurst exponent. We also introduce an adjusted DO process which provides a uniformly good approximation of the fractional BM for all Hurst exponents $H \in [0,1]$ but requires a complex measure. Finally, the characteristic function (CF) of $\log S_t$ in our model can be found in closed form by using asymptotic expansion. Therefore, pricing options and variance swaps (by using a forward CF) can be done via FFT, which is much easier than in rough volatility models. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1904.09240&r=all |