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We investigate whether smooth robust methods for forecasting can help mitigate pronounced and persistent failure across multiple forecast horizons. We demonstrate that naive predictors are interpretable as local estimators of the long-run relationship with the advantage of adapting quickly after a break, but at a cost of additional forecast error variance. Smoothing over naive estimates helps retain these advantages while reducing the costs, especially for longer forecast horizons. We derive the performance of these predictors after a location shift, and confirm the results using simulations. We apply smooth methods to forecasts of UK productivity and US 10-year Treasury yields and show that they can dramatically reduce persistent forecast failure exhibited by forecasts from macroeconomic models and professional forecasters.

This study employs a new time series representation of persistence in conditional mean and variance to test for the existence of the long memory property in the currency futures market. Empirical results indicate that there exists a fractional exponent in the differencing process for foreign currency futures prices. The series of returns for these currencies displays long-term positive dependence. A hedging strategy for long memory in volatility is also discussed in this article to help the investors hedge for the exchange rate risk by using currency futures.

The purpose of this paper is to provide empirical evidence on the long-memory behaviour of South African real estate investment trusts (SAREITs).

The study employs a battery of advanced techniques to examine the behaviour of returns of 29 SAREIT equities listed on the Johannesburg Stock Exchange. The authors analysed daily closing prices covering different periods up to 21 May 2016. The results provide support for long memory in majority of SAREIT returns.

The finding of negative fractional integration parameters provides evidence of anti-persistence in SAREIT returns.

It is recommended that the regulatory authorities adopt technologies that allow a more effective, faster means to disseminate information, and improve the electronic trading mechanism that facilitates quicker price adjustment to news entering the market.

The paper determines the fractional differencing (long-memory) parameter for SAREITs and adds value to the existing body of knowledge.

The purpose of this paper is to examine, whether or not, the residuals of the market model (MM) are conditionally heteroscedastic; to examine, whether or not, there exists an intervalling effect in conditional heteroscedasticity in the residuals of the MM; to propose a simple data‐driven conditional capital asset pricing model (CAPM); and to examine the effect of conditional heteroscedasticity on the estimation of systematic risk.

Systematic risk coefficients (betas) are estimated at first using data of various frequencies from the Athens stock exchange without taking into account conditional heteroscedasticity. The same procedure is repeated, but this time taking into consideration conditional heteroscedasticity, which is found to exist. The results of the two approaches are compared.

Empirical evidence is provided for the existence of: conditional heteroscedasticity in MM residuals; a pronounced intervalling effect on autoregressive conditional heteroscedasticity (ARCH) in MM residuals; and generalized autoregressive conditional heteroscedasticity in mean type of conditional heteroscedasticity for the majority of cases where ARCH was present in MM residuals. These findings are conducive to a conditional CAPM, which takes into account the effect of conditional variance on expected returns, rather than the standard CAPM.

Better estimates of financial risk.

The intervalling effect in ARCH in the residuals of the MM is examined for the first time.

This study aims to analyse the conditional volatility of the Vietnam Index (Ho Chi Minh City) and the Hanoi Exchange Index (Hanoi) with a specific focus on their application to risk management tools such as Expected Shortfall (ES).

First, the author tests both indices for long memory in their returns and squared returns. Second, the author applies several generalised autoregressive conditional heteroskedasticity (GARCH) models to account for asymmetry and long memory effects in conditional volatility. Finally, the author back tests the GARCH models’ forecasts for Value-at-Risk (VaR) and ES.

The author does not find long memory in returns, but does find long memory in the squared returns. The results suggest differences in both indices for the asymmetric impact of negative and positive news on volatility and the persistence of shocks (long memory). Long memory models perform best when estimating risk measures for both series.

Short-time horizons to estimate the variance should be avoided. A combination of long memory GARCH models with skewed Student’s t-distribution is recommended to forecast VaR and ES.

Up to now, no analysis has examined asymmetry and long memory effects jointly. Moreover, studies on Vietnamese stock market volatility do not take ES into consideration. This study attempts to overcome this gap. The author contributes by offering more insight into the Vietnamese stock market properties and shows the necessity of considering ES in risk management. The findings of this study are important to domestic and foreign practitioners, particularly for risk management, as well as banks and researchers investigating international markets.

Since the seminal works by Granger and Joyeux (1980) and Hosking (1981), estimations of long-memory time series models have been receiving considerable attention and a number of parameter estimation procedures have been proposed. This paper gives an overview of this plethora of methodologies with special focus on likelihood-based techniques. Broadly speaking, likelihood-based techniques can be classified into the following categories: the exact maximum likelihood (ML) estimation (Sowell, 1992; Dahlhaus, 1989), ML estimates based on autoregressive approximations (Granger & Joyeux, 1980; Li & McLeod, 1986), Whittle estimates (Fox & Taqqu, 1986; Giraitis & Surgailis, 1990), Whittle estimates with autoregressive truncation (Beran, 1994a), approximate estimates based on the Durbin–Levinson algorithm (Haslett & Raftery, 1989), state-space-based maximum likelihood estimates for ARFIMA models (Chan & Palma, 1998), and estimation of stochastic volatility models (Ghysels, Harvey, & Renault, 1996; Breidt, Crato, & de Lima, 1998; Chan & Petris, 2000) among others. Given the diversified applications of these techniques in different areas, this review aims at providing a succinct survey of these methodologies as well as an overview of important related problems such as the ML estimation with missing data (Palma & Chan, 1997), influence of subsets of observations on estimates and the estimation of seasonal long-memory models (Palma & Chan, 2005). Performances and asymptotic properties of these techniques are compared and examined. Inter-connections and finite sample performances among these procedures are studied. Finally, applications to financial time series of these methodologies are discussed.

This chapter demonstrates that fixed-effects and first-differences models often understate the effect of interest because of the variation used to identify the model. In particular, the within-unit time-series variation often reflects transitory fluctuations that have little effect on behavioral outcomes. The data in effect suffer from measurement error, as a portion of the variation in the independent variable has no effect on the dependent variable. Two empirical examples are presented: one on the relationship between AFDC and fertility and the other on the relationship between local economic conditions and AFDC expenditures.

The importance of banking and insurance, as an important part of the financial system, has been well accepted in the growth literature. Acting as financial intermediaries they perform important functions that may contribute in economic growth. Addressing this issue, the purpose of this paper is to empirically examine the relationship between banking, insurance and economic growth in India in the post-liberalized era when the private sector was allowed to operate banking and insurance business.

In order to find the long-run and short-run relationship between banking, insurance and economic growth, the study uses the VAR-vector error correction model (VECM) along with Granger causality test to explore any causal relationship.

The results indicate that there is the long-term relationship between banking, insurance and economic growth and the causality results show a bi-directional relationship between insurance activity and economic growth; however, banking is not granger cause of insurance or economic growth rather it is economic growth that cause banking development.

The only limitation to the study is the non-availability of monthly figures of GDP. The study therefore, as suggested by RBI, uses monthly data set of Index of Industrial Production to measure economic growth.

The findings of the study give policy directions to the policymakers to make strategies that are conducive toward boosting development in insurance in order to achieve the targeted economic growth.

This work is the first attempt to study the conjoint relationship between banking, insurance and economic growth on the Indian economy after the reforms were initiated in the financial sector.

This paper aims to examine association of money demand with key macroeconomic variables in Pakistan. The paper also investigates the asymmetric effect of real effective exchange rate (REER) on money demand.

The study employs both linear autoregressive distributed lag (ARDL) and non-linear autoregressive distributed lag (NARDL) model. Annual data from 1970 to 2018 is used which is subjected to non-linearity through partial sum concept. Empirical analysis is conducted to prove if money demand is influenced by currency appreciation or depreciation, for long and short run.

Cointegration test indicates existence of a long-run relationship between money demand and its determinants. Results from NARDL model suggest negative relation between money demand and inflation in long and short run. Real income shows positive but a very minimal and insignificant effect on money demand in long and short run. Impact of call money rates is statistically significant and negative on M1 and M2. Wald tests and differing coefficient sign confirm presence of asymmetric relation of REER in long run with M2, whereas in short run we observe a linear, symmetrical relation of REER with M1 and M2. Stability diagnostic tests (CUSUM and CUSUMSQ) verify stability of M2 demand model in Pakistan.

Results signify that role of money demand is imperative as a monetary policy tool and it can be utilized to achieve objective of price stability. Additionally, exchange rate movements should be critically examined by monetary authorities to avoid inflationary pressures resulting from an increase in demand for broad monetary aggregate.

The paper contributes to scarce monetary literature on asymmetrical effects of exchange rate in Pakistan. Impact of variables has been studied through linear approach, but this paper is unique since it attempts to explore non-linear relationships.

This paper investigates whether a J-curve can be detected in the time series data on China’s bilateral trade with the G-7 countries. It utilizes cointegration and causality tests to ascertain both the long-run relatedness, and the short-run dynamics, between the real exchange rate, national income, and the trade balance. There is some evidence that a real depreciation eventually improves the trade balance with some countries. But there is no indication of a negative short-run response which characterizes the J-curve.