FIGARCH Model Research Articles

Forecasting volatility in developing countries’ nominal exchange returns

This paper identifies the best models for forecasting the volatility of daily exchange returns of developing countries. An emerging consensus in the recent literature focusing on industrialised counties has noted the superior performance of the FIGARCH model in the case of industrialised countries, a result that is reaffirmed here. However, we show that when dealing with developing countries’ data the IGARCH model results in substantial gains in terms of the in-sample results and out-of-sample forecasting performance.

Oil and Methanol Price volatility

Crude oil as one of the main sources of energy is also the main source of income for members of OPEC. So, the volatility of crude oil price is one of the main economic variables in the world and analysis of the effect of its changes on key economic factors has been always considered as significant. The reason might be the high sensitivity of oil price to political, economic and cultural issues worldwide and consequently its volatility on the one hand, and the high influence of the volatile prices on macroeconomic variables. On the other hand, for different reasons such as oil price volatilities and income from oil export, economic planners and policy makers in Iran have been mainly focused on the promotion of non-oil exports especially during the last few decades. Therefore, methanol as one of the most commonly used petrochemical products has a high potential for production and export of non-oil products in Iran. For this reason, in the present study there was an attempt to examine the relationship between the prices of Iran’s crude oil and methanol using FIGARCH model and based on the weekly time series data related to the research variables. The results of the study showed that the long memory parameter is equal to 0.32 which is meaning the shocks caused by volatility of methanol market and crude oil price to the methanol price were lasting and meaningful and were revealed in the long term.

Sectoral stock return sensitivity to oil price changes: a double-threshold FIGARCH model

We investigate the association between the stock return distributions of 10 major U.S. sectors and oil returns within a double-threshold FIGARCH model. This model nests GARCH, IGARCH and Fama–French specifications as its special cases and allows a test of their validity. This model also has the advantage of capturing not only the short-run dynamics (as in the standard GARCH model), but also the long-run persistence pattern of oil shock effects that may decay at a slower hyperbolic pace. We find that: (i) data reject the more restricted GARCH, IGARCH and Fama–French models in favor of FIGARCH, (ii) oil return is a significant determinant of every sector's return and/or return volatility, (iii) oil effects are asymmetric for oil returns above and below the thresholds, (iv) asymmetry is stronger when oil return volatility is greater, (v) volatilities of sectoral returns exhibit threshold-based regime shifts, and (vi) oil shock effects follow a hyperbolic, rather than an exponential decay pattern, establishing long-term persistence of shocks. Policy implications are drawn.

Support Vector Machine GARCH and Neural Network GARCH Models in Modeling Conditional Volatility: An Application to Turkish Financial Markets

The Turkish version of this paper can be found at: http://ssrn.com/abstract=2222071 The study aims to investigate linear GARCH, fractionally integrated FI-GARCH and Asymmetric Power APGARCH models and their nonlinear counterparts based on Support Vector Regression (SVR) and Neural Network (NN) models. GARCH family models are extended to NN-GARCH architecture of Donaldson and Kamstra (1997) to various NN-GARCH family models (Bildirici and Ersin, 2009) such as NN-APGARCH model. The study aims to introduce a class of extended NN-GARCH and SVR-GARCH family of models with nonlinear augmentations in modeling both the conditional mean and variance. The SVR-GARCH, SVR-APGARCH and SVR-FIAPGARCH and their Multi-Layer Perceptron architecture based counterparts, MLP-GARCH, MLP-APGARCH and MLP-FIAPGARCH are evaluated. An application to daily returns in Istanbul ISE100 stock index is provided. Results suggest that volatility clustering, asymmetry and nonlinearity characteristics are modeled more efficiently with the models possessing neural network architectures.

Asymmetric long memory volatility in the PIIGS economies

PurposeThe main purpose of this paper is to examine the asymmetry and long memory properties in the volatility of the stock indices of the PIIGS economies (Portugal, Ireland, Italy, Greece and Spain).Design/methodology/approachThe paper utilizes the wavelets approach (based on Haar, Daubechies‐4, Daubechies‐12 and Daubechies‐20 wavelets) and the GARCH class of models (namely, ARFIMA (p,d′,q)‐GARCH (1,1), IGARCH (1,1), FIGARCH (1,d,0), FIGARCH (1,d,1), EGARCH (1,1) and FIEGARCH (1,d,1)) to accomplish the desired goals.FindingsThe findings provide evidence in support of the presence of long range dependence in the various proxies of volatility of the PIIGS economies. The results from the wavelet approach also support the Taylor effect in the volatility proxies. The results show that ARFIMA (p,d′,q)‐FIGARCH (1,d,0) model specification is better able to capture the long memory property of conditional volatility than the conventional GARCH and IGARCH models. In addition, the ARFIMA (p,d′,q)‐FIEGARCH (1,d,1) model is better able to capture the asymmetric long memory feature in the conditional volatility.Originality/valueThis paper has both methodological and empirical originality. On the methodological side, the study applies the wavelet technique on the major proxies of volatility (squared returns, absolute returns, logarithm squared returns and the range) because the wavelet‐based estimator exhibits superior properties in modeling the behavior of the volatility of stock returns. On the empirical side, the paper finds asymmetry and long range dependence in the conditional volatility of the stock returns in PIIGS economies using the GARCH family of models.

MODELLING LONG MEMORY VOLATILITY IN AGRICULTURAL COMMODITY FUTURES RETURNS

This paper estimates a long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGARCH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1, d, 1) and FIEGARCH (1, d, 1) models are found to outperform their GARCH (1, 1) and EGARCH (1, 1) counterparts.

Modeling and forecasting the volatility of petroleum futures prices

We investigate volatility models and their forecasting abilities for three types of petroleum futures contracts traded on the New York Mercantile Exchange (West Texas Intermediate crude oil, heating oil #2, and unleaded gasoline) and suggest some stylized facts about the volatility of these futures markets, particularly in regard to volatility persistence (or long-memory properties). In this context, we examine the persistence of market returns and volatility simultaneously using the following ARFIMA–GARCH-class models: ARIMA–GARCH, ARFIMA–GARCH, ARFIMA–IGARCH, and ARFIMA–FIGARCH. Although the ARFIMA–FIGARCH model better captures long-memory properties of returns and volatility, the out-of-sample analysis indicates no unique model for all three types of petroleum futures contracts, suggesting that investors should be careful when measuring and forecasting the volatility (risk) of petroleum futures markets.

Interrelationships among the Taiwanese, Japanese and Korean TFT-LCD panel industry stock market indexes: An application of the trivariate FIEC–FIGARCH model

The purpose of this study is to analyze the interrelationships among the Taiwanese, Japanese and Korean TFT-LCD panel industry stock market indexes by applying a trivariate FIEC-FIGARCH model. The empirical results confirm that the FIEC-FIGARCH model can be used to capture long memory behavior and allow us to conclude that mean and volatility spillover, and long memory effects are found in these three markets. Furthermore, we found that deviations in the long-run equilibrium for Japanese TFT-LCD panel industry adjust back very slowly in comparison to the other two countries; and that, in terms of conditional covariance, dynamic interrelationships exist among the TFT-LCD panel industry stock market indices of these three countries.

Long Memory Property In Return and Volatility: Evidence from the Indian Stock Markets

The paper examines the existence of long memory in the Indian stock market using ARFIMA, FIGARCH models. The data set consists of daily return of BSE and NSE stock indices and long memory tests are carried out both for the returns and volatilities of these series. The results of ARFIMA model suggests the absence of long memory in return series of the Indian stock market. The results of FIGARCH model indicate strong evidence of long memory in conditional variance of the stock indices. The long memory property of the BSE market is revealed to be stronger than NSE.

Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period value-at-risk (VaR) and expected shortfall (ES) across 20 stock indices worldwide. The dataset is composed of daily data covering the period from 1989 to 2009. The research addresses the question of whether or not accounting for long memory in the conditional variance specification improves the accuracy of the VaR and ES forecasts produced, particularly for longer time horizons. Accounting for fractional integration in the conditional variance model does not appear to improve the accuracy of the VaR forecasts for the 1-day-ahead, 10-day-ahead and 20-day-ahead forecasting horizons relative to the short memory GARCH specification. Additionally, the results suggest that underestimation of the true VaR figure becomes less prevalent as the forecasting horizon increases. Furthermore, the GARCH model has a lower quadratic loss between actual returns and ES forecasts, for the majority of the indices considered for the 10-day and 20-day forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve the VaR and ES forecasting accuracy, even for longer forecasting horizons. Finally, the rolling-sampled estimated FIGARCH parameters change less smoothly over time compared to the GARCH models. Hence, the parameters' time-variant characteristic cannot be entirely due to the news information arrival process of the market; a portion must be due to the FIGARCH modelling process itself.

Long memory and structural breaks in modeling the return and volatility dynamics of precious metals

Abstract We investigate the potential of structural changes and long memory (LM) properties in returns and volatility of the four major precious metal commodities traded on the COMEX markets (gold, silver, platinum and palladium). Broadly speaking, a random variable is said to exhibit long memory behavior if its autocorrelation function is not integrable, while structural changes can induce sudden and significant shifts in the time-series behavior of that variable. The results from implementing several parametric and semiparametric methods indicate strong evidence of long range dependence in the daily conditional return and volatility processes for the precious metals. Moreover, for most of the precious metals considered, this dual long memory is found to be adequately captured by an ARFIMA–FIGARCH model, which also provides better out-of-sample forecast accuracy than several popular volatility models. Finally, evidence shows that conditional volatility of precious metals is better explained by long memory than by structural breaks.

Modelling heavy tails and double long memory in North African stock market returns

Modelling heavy tails and double long memory in stock returns is very important for financial asset pricing, asset allocation and risk management. In this paper, we demonstrate that an α-stable distribution is better fitted to the North African stock return data in TUNINDEX (Tunisia), MASI (Morocco) and EGX30 (Egypt) than the normal distribution. The empirical results show that the asymmetric leptokurtic features presented in these markets can be captured by an α-stable distribution. Moreover, estimation of the tail index allows us to determine the long-memory behaviour of stock returns. Additionally, this study examines the long-memory property in mean returns and volatility of these markets. The results indicate that long-memory dynamics in the returns and volatility might be modelled by the joint ARFIMA–FIGARCH model. The results of the joint ARFIMA–FIGARCH model show strong evidence of long memory in both returns and volatility. The long memory in returns implies that stock prices follow a predictable behaviour, which is inconsistent with the efficient market hypothesis. The evidence of long memory in volatility, however, shows that uncertainty or risk is an important determinant of the behaviour of daily stock data in North African stock markets. The implication of the present work is that the assumption of non-normality provides better specifications regarding the long-memory property.

Return volatilities of the Korea treasury bond in financial markets

We investigate the long memory property in the volatility of the Korean futures market. For our model, we analyze the high-frequency data of the KTB503 by applying the FIGARCH model. From the result of our analysis, we conclude that the volatility of the KTB503 exhibits the feature of the long memory. In particular, this long memory feature is compared to those used in other studies.

Uma avaliação da volatilidade dos preços da soja no mercado internacional com dados de alta frequência

Neste trabalho foram avaliados os ajustes de cinco modelos para previsão da variância, utilizando-se uma série de preços de soja, uma commodity negociada na bolsa de mercadorias de Chicago (CBOT), com dados de alta frequência. Os modelos utilizados foram do tipo GARCH, FIGARCH e ARFIMA. Foi possível observar características desta série de preços de uma commodity negociada globalmente que se apresentaram inteiramente diferentes daquelas de ativos financeiros anteriormente estudados, possivelmente em virtude da característica contínua dos preços observados, induzida pela sua negociação global independente de pregões com início e fim. Foi possível concluir que a série de dados de alta frequência encerra informações adicionais às séries de dados diários, também no caso estudado de preços da soja, e que o tradicional modelo GARCH(1,1) tem um bom desempenho também no caso dos dados de alta frequência, assim como aqueles da família ARFIMA. Recomenda-se mais investigação para o caso dos modelos FIGARCH, procurando um melhor ajuste.

Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models

This paper extends previous studies by investigating the relevance of structural breaks and long memory in modeling and forecasting the conditional volatility of oil spot and futures prices using a variety of GARCH-type models. Our results can be summarized as follows. First, we provide evidence of parameter instability in five out of nine GARCH-based conditional volatility processes for energy prices. Second, long memory is effectively present in all the series considered and a FIGARCH model seems to better fit the data, but the degree of volatility persistence diminishes significantly after adjusting for structural breaks. Finally, the out-of-sample analysis shows that volatility models accommodating instability and long memory characteristics of the data provide the best volatility forecasts for most cases.

Real Versus Spurious Long-Memory Volatility In Foreign Exchange Data: Evidence From The Rand Against The G4 Currencies

This paper investigates the extent to which volatility estimates and forecasts may be biased because of the existence of, and failure to account for structural change in the evolution of the exchange rate volatility series. The results of this analysis show that evidence of volatility persistence and long-memory in the Rand against the G4 currencies is overestimated when structural breaks are not considered. A modification of the standard GARCH model to allow for time-variation in the unconditional variance is shown to generate improved volatility forecasting performance over long(er)1 horizons for some currency pairs. Finally, the performance of the standard GARCH, modified GARCH and FIGARCH models are evaluated in the context of value-at- risk (VaR) estimation given the Basle regulatory framework. The results show that both the modified GARCH model and long-memory models generally deliver more accurate VaR measures relative to the standard GARCH model. In terms of VaR estimation our findings may provide guidance on more effective prudential standards for operational risk measurement and, as result, may help ensure adequate capitalisation and reduce the probability of financial distress. Our results highlight the importance of using out-of-sample forecasting techniques and the stipulated probability level for the identification of methods that minimise the occurrence of VaR exceptions. We find that models that account for structural change and long memory attributes generally outperform the basic GARCH model in estimating VaR across the probability levels we considered.

Structural Breaks and Long Memory Property in Korean Won Exchange Rates: Adaptive FIGARCH Model

Han, Young Wook.June, 2011.Structural breaks and long memory property in Korean Won exchange rates,Article,[Seoul]Korea Institute for International Economic Policy,28

Does Corporate Governance Matter for Market Efficiency? Evidence from Turkish markets

This article examines informational efficiency in the Corporate Governance Index comprising of firms having corporate governance ratings in Turkish equity markets. By using ARFIMA FIGARCH model, it is empirically showed that Corporate Governance Index does not display long-memory effect, thus is in weak-form efficiency. The article has originality in that it examines Corporate Governance Index in Turkey in terms of market efficiency by employing ARFIMA-FIGARCH model that consider coexistence of long-term memory in return and volatility. Key words: Corporate governance, market efficiency, long-memory models, Turkish equity markets   JEL codes: G32, G14, C14

Forecasting exchange rate volatility using high-frequency data: Is the euro different?

We assess the performances of alternative procedures for forecasting the daily volatility of the euro’s bilateral exchange rates using 15 min data. We use realized volatility and traditional time series volatility models. Our results indicate that using high-frequency data and considering their long memory dimension enhances the performance of volatility forecasts significantly. We find that the intraday FIGARCH model and the ARFIMA model outperform other traditional models for all exchange rate series.

Modeling volatility with time-varying FIGARCH models

This paper puts the light on a new class of time-varying FIGARCH or TV-FIGARCH processes to model the volatility. This new model has the feature to account for the long memory and the structural change in the conditional variance process. The structural change is modeled by a logistic function allowing the intercept to vary over time. We also implement a modeling strategy for our TV-FIGARCH specification whose performance is examined by a Monte Carlo study. An empirical application to the crude oil price and the S&P 500 index is carried out to illustrate the usefulness of our techniques. The main result of this paper is that the long memory behavior of the absolute returns is not only explained by the existence of the long memory in the volatility but also by deterministic changes in the unconditional variance.