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The purpose of this paper is to introduce a model to measure foreign exchange (FX) rate volatility accurately. The FX rate volatility forecasting is a crucial endeavour in financial markets and has gained the attention of researchers and practitioners over the last several decades. The implied volatility (IV) measure is widely believed to be the best measure of exchange rate volatility. Despite its widespread usage, the IV approach suffers from an obvious chicken‐egg problem: obtaining an unbiased IV requires the options to be priced correctly and calculating option prices accurately requires an unbiased IV.

The authors contribute to the literature by developing a new model for FX rate volatility – the “moneyness volatility (MV)”. This approach is based on measuring the variability of forward‐looking “moneyness” rather than use of options price. To assess volatility forecasting performance of MV against IV, the in‐sample and out‐of‐sample tests are involved using the F‐test, Granger‐Newbold test and Diebold‐Mariano framework.

The MV model outperforms the IV in FX rate volatility forecasting ability in both in‐sample and out‐of‐sample tests. The F‐test, Granger‐Newbold test and Diebold‐Mariano test results consistently reveal that MV outperforms IV in estimating as well as forecasting exchange rate volatility for six major currency options. Furthermore, in Mincer‐Zarnowitz regressions, MV outperforms IV and time‐series models in predicting future volatility.

The authors’ pioneering approach in modeling exchange rate volatility has far‐reaching implications for academicians, professional traders, and financial risk analysts and managers.

The purpose of this paper is to test the accuracy of the explicit time variable (ETV) and the strictly cross-sectional (SCS) hedonic models when constructing house price indices in developing markets using Ghana as a case study.

The quantitative research methodology is adopted where the accuracy of the two hedonic models used in the construction of house price indices is examined using the mean squared error (MSE) and out-of-sample technique. Yearly indices are constructed for each of the models using 60 per cent of the sample data and 40 per cent is used to forecast house prices for each observations based on which the MSEs are calculated.

The two models produce similar house price trend but the SCS model is more volatile. The ETV model produces the lower MSE, suggesting that it is better to pool data together and includes time dummies (ETV) to estimate indices rather than running separate regressions (SCS) to estimate the index. Using the Morgan–Granger–Newbold test, it is found that indeed the difference between the forecast errors of the two models are statistically significant on a 1 per cent level confirming the accuracy of the ETV model over the SCS model.

This paper has produced convincing results recommending the use of the ETV hedonic model to construct house price indices which is of use to practitioners and academics.

The introduction of financial products like the property derivatives and home equity insurances to the financial market calls for accurate and robust property price indices and the hedonic method is mostly used to construct these indices. While there have been a lot of test conducted as to which variant of the hedonic method to use in developed markets, little is known about the developing markets. This paper contributes to fill these gaps.

We provide straightforward new nonparametric methods for testing conditional independence using local polynomial quantile regression, allowing weakly dependent data. Inspired by Hausman's (1978) specification testing ideas, our methods essentially compare two collections of estimators that converge to the same limits under correct specification (conditional independence) and that diverge under the alternative. To establish the properties of our estimators, we generalize the existing nonparametric quantile literature not only by allowing for dependent heterogeneous data but also by establishing a weak consistency rate for the local Bahadur representation that is uniform in both the conditioning variables and the quantile index. We also show that, despite our nonparametric approach, our tests can detect local alternatives to conditional independence that decay to zero at the parametric rate. Our approach gives the first nonparametric tests for time-series conditional independence that can detect local alternatives at the parametric rate. Monte Carlo simulations suggest that our tests perform well in finite samples. We apply our test to test for a key identifying assumption in the literature on nonparametric, nonseparable models by studying the returns to schooling.

This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress.

A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models.

The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models.

The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

This study examines the volatility of inflation in Indonesia before and during COVID-19, focusing on people’s purchasing power. The high inflation variability makes future price expectations uncertain, creating risks in the long run and uncertainty in wealth redistribution. The ARIMA model was used from January 2005 to June 2020. The results show that the ARMA (0.1) model is suitable for testing inflation volatility in Indonesia. Forecasting results show that inflation for the next six months will still be under pressure due to COVID-19.

There is a growing interest in fuzzy time series (FTS) forecasting, and several improvements are presented in the last few decades. Among these improvements, the development of causal models (i.e., multiple factor FTS) has sparked a particular literature dealing with the causal inference and its integration in the FTS framework. However, causality among variables is usually introduced as a subjective assumption rather than empirical evidence. As a result of arbitrary causal modeling, the existing multiple factor FTS models are developed with implicit forecasting failure. Since post-sample control (unknown future, as in the business practice) is usually ignored, the spurious accuracy gain through increasing factors is not identified by scholars. This paper discloses the use of causality in the FTS method, and investigates the spurious causal inference problem in the literature with a justification approach. It invalidates the contribution of dozens of previously published papers while justifying its claim with illustrative examples and a comprehensive set of experiments with random data, as well as real business data from maritime transportation (Baltic Dry Index).

This chapter re-examines the Fama–French (FF) five-factor asset pricing model proposed by Fama and French (2015), since this model has a failure to capture the lower average returns on small stocks and its performance could not fully satisfy the original definitions of those considered factors. From the viewpoint of the econometrics analysis, we consider the inferior performance could be potentially caused by the spurious effect in the five-factor model, which could mislead the statistical inference and yield biased empirical results. We thus employ the CO-AR estimation by Wang and Hafner (2018) to prove the usefulness of the FF five-factor model. Empirical results demonstrate with the CO-AR estimation, the five-factor model indeed properly captures the lower average returns on small stocks and illustrate the sustainability of efficiency of the market, which is in contrast to the findings of Fama and French (2015). However, we propose a new perspective on the seminal five-factor model.

A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-à-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently popularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionally integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management.

This chapter investigates the predictability of the European monetary policy through the eyes of the professional forecasters from a large investment bank. The analysis is based on forward-looking Actual and Perceived Taylor Rules for the European Central Bank which are estimated in real-time using a newly constructed database for the period April 2000–November 2009. The former policy rule is based on the actual refi rate set by the Governing Council, while the latter is estimated for the bank’s economists using their main point forecast for the upcoming refi rate decision as a dependent variable. The empirical evidence shows that the pattern of the refi rate is broadly well predicted by the professional forecasters even though the latter have foreseen more accurately the increases rather than the policy rate cuts. Second, the results point to an increasing responsiveness of the ECB to macroeconomic fundamentals along the forecast horizon. Third, the rolling window regressions suggest that the estimated coefficients have changed after the bankruptcy of Lehman Brothers in October 2008; the ECB has responded less strongly to macroeconomic fundamentals and the degree of policy inertia has decreased. A sensitivity analysis shows that the baseline results are robust to applying a recursive window methodology and some of the findings are qualitatively unaltered from using Consensus Economics forecasts in the regressions.