Forecasting inflation, industrial output and exchange rates : a template study for India

This paper reports results from a forecasting study for inflation, industrial output and exchange rates for India. We cannot reject the null hypothesis for linearity for all series used except for the growth rate of the foreign exchange series and our analysis is based on linear models, ARIMA and bivariate transfer functions and restricted VAR. Forecasting performance is evaluated using the models’ root mean-squared error differences and Theil’s inequality coefficients from recursive origin static, fixed origin dynamic and rolling origin dynamic forecasts. For models based on weekly data, based on RMSEs, we find that the bivariate models improve upon the forecasts of the ARIMA model while for models based on monthly data the ARIMA model has almost always better performance. In choosing between the two bivariate models on the basis of RMSEs, our overall results tend to support the use of a restricted VAR, as this model had the best forecasting performance more frequently than the transfer function model.

Dynamic interdependence and volatility transmission of Asian stock markets: evidence from the Asian crisis

This paper examines dynamic interdependence, volatility transmission, and market integration across selected stock markets during the Asian financial crisis periods 1997 and 1998. Using a vector autoregressive–exponential generalized autoregressive conditional heteroskedasticity (VAR-EGARCH) model, it is found that reciprocal volatility transmission existed between Hong Kong and Korea, and unidirectional volatility transmission from Korea to Thailand. This suggests that Hong Kong played a significant role in volatility transmission to the other Asian markets. The data also indicate market integration in that each market reacted to both local news and news originating in the other markets, particularly adverse news.

Intraday periodicity and long memory volatility processes in the Korean bond futures market

This article examines long memory volatility processes in the 5 minute Korean Treasury bond futures data using two different techniques: the GPH test and the EML test. Two proxies of volatility returns are used: absolute returns and square returns. We found intraday periodicity and autocorrelation persistence in the volatility for the 5 minute returns. The empirical results from both techniques indicate that there is little evidence of long memory in the level of returns, but there is significant evidence of long memory in the absolute returns and the square returns. This implies that volatility is best characterised by long memory processes. Additionally, we found that the absolute returns are the most appropriate indicator to represent the long memory volatility processes.

Forecasting industry-level CPI and PPI inflation : does exchange rate pass-through matter?

We show that incorporating the effects of exchange rate pass-through into a model can help in obtaining superior forecasts of domestic, industry-level inflation. Our analysis is based on a multivariate system of domestic inflation, import prices and exchange rates that incorporates restrictions from economic theory. These are restrictions on the transmission channels of the exchange rate pass-through to domestic prices, and are presented as testable hypotheses that lead to model reduction. We provide the results of various tests, including causality and prior restrictions, which support the underlying economic arguments and the model we use. The forecasting results for our model suggest that it has a superior performance overall, jointly producing more accurate forecasts of domestic inflation.

Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH

The performance of different information criteria - namely Akaike, corrected Akaike (AICC), Schwarz-Bayesian (SBC), and Hannan-Quinn - is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.

Modelling regulatory change V's volume of trading effects in HSIF and HSI volatility

In an earlier paper, we adopted a bi-variate BEKK–GARCH framework and employed a systematic approach to examine structural breaks in the Hang Seng Index and Index Futures market volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market surrounding the Asian markets crisis. In this paper, we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant. There is some evidence that the Sequential Arrival of Information Model (SIM) provides a platform to explain these market induced effects when volume of trade is accounted for.

Multifractality and long-range dependence of asset returns: the scaling behavior of the Markov-switching multifractal model with lognormal volatility components

In this paper, we consider daily financial data from various sources (stock market indices, foreign exchange rates and bonds) and analyze their multiscaling properties by estimating the parameters of a Markov-switching multifractal (MSM) model with Lognormal volatility components. In order to see how well estimated models capture the temporal dependency of the empirical data, we estimate and compare (generalized) Hurst exponents for both empirical data and simulated MSM models. In general, the Lognormal MSM models generate "apparent" long memory in good agreement with empirical scaling provided that one uses sufficiently many volatility components. In comparison with a Binomial MSM specification [11], results are almost identical. This suggests that a parsimonious discrete specification is flexible enough and the gain from adopting the continuous Lognormal distribution is very limited.

Forecasting Fiji's exports and imports, 2003-2020

Purpose – The purpose of this paper is to forecast Fiji's exports and imports for the period 2003-2020.

Design/methodology/approach – To achieve the goal of this paper, the autoregressive moving average with explanatory variables (ARMAX) model was applied. To this end, the paper drew on the published export demand model and the import demand model of Narayan and Narayan for Fiji.

Findings – The paper's main findings are: Fiji's imports will outperform exports over the 2003-2020 period; and current account deficits will escalate to be around F$934.4 million on average over the 2003-2020 period.

Originality/value – Exports and imports are crucial for macroeconomic policymaking. It measures the degree of openness of a country and it signals the trade balance and current account balances. This has implications for inflation and exchange rate. By forecasting Fiji's exports and imports, the paper provides policy makers with a set of information that will be useful for devising macroeconomic policies.

The forecast quality of CBOE implied volatility indexes

We examine the forecast quality of Chicago Board Options Exchange (CBOE) implied volatility indexes based on the Nasdaq 100 and Standard and Poor's 100 and 500 stock indexes. We find that the forecast quality of CBOE implied volatilities for the S&P 100 (VXO) and S&P 500 (VIX) has improved since 1995. Implied volatilities for the Nasdaq 100 (VXN) appear to provide even higher quality forecasts of future volatility. We further find that attenuation biases induced by the econometric problem of errors in variables appear to have largely disappeared from CBOE volatility index data since 1995.

Estimating expected excess returns using historical and option-implied volatility

We test the relation between expected and realized excess returns for the S&P 500 index from January 1994 through December 2003 using the proportional reward-to-risk measure to estimate expected returns. When risk is measured by historical volatility, we find no relation between expected and realized excess returns. In contrast, when risk is measured by option-implied volatility, we find a positive and significant relation between expected and realized excess returns in the 1994–1998 subperiod. In the 1999–2003 subperiod, the option-implied volatility risk measure yields a positive, but statistically insignificant, risk-return relation. We attribute this performance difference to the fact that, in the 1994–1998 subperiod, return volatility was lower and the average return was much higher than in the 1999–2003 subperiod, thereby increasing the signal-to-noise ratio in the latter subperiod.

Realized volatility and correlation in grain futures markets: testing for spillover effects

Fluctuations in commodity prices are a major concern to many market participants. This paper uses realized volatility methods to calculate daily volatility and correlation estimates for three grain futures prices (corn, soybean, and wheat). The realized volatility estimates exhibit properties consistent with the stylized facts observed in earlier studies. According to daily realized correlations and regression coefficients, the spot returns from the three grain futures are positively related. The realized estimates are then used to evaluate the degree of volatility transmission across grain futures prices. The impulse response analysis is conducted by fitting the vector autoregressive model to realized volatility and correlation estimates, using the bootstrap method for statistical inference. The results indicate that rich dynamic interactions exist among the volatilities and correlations across the grain futures markets.

Modelling Fiji-US exchange rate volatility

In this article, we examine the Fiji-US exchange rate volatility using daily data for the period 2000 to 2006. Our modelling framework is based on the EGARCH model. We find robust evidence of conditional shocks having a positive effect on exchange rate volatility, shocks having asymmetric effects on exchange rate volatility and shocks having a transitory effect on exchange rate volatility.