We present an approach to forecast customer orders of ready-to-launch new products that are similar to past products. The approach fits product life cycle (PLC) curves to historical customer order data, clusters the curves of similar products, and uses the representative curve of the new...

Macroeconomic analysis in Lebanon presents a distinct challenge. For example, long delays in the publication of GDP data mean that our analysis often relies on proxy variables, and resembles an extended version of the 'nowcasting' challenge familiar to many central banks. Addressing this...

The game of cricket got a new dimension, when the Indian Premier League (IPL), a competition of twenty over-a-side featuring eight teams named after various Indian cities/states started in 2008. The teams were franchisee driven and the players were selected via competitive bidding from a pool of...

A modification of the self-perturbed Kalman filter of Park and Jun (1992) is proposed for the on-line estimation of models subject to parameter in stability. The perturbation term in the updating equation of the state covariance matrix is weighted by the measurement error variance, thus avoiding...

This study employs big data and text data mining techniques to forecast financial market volatility. We incorporate financial information from online news sources into time series volatility models. We categorize a topic for each news article using time stamps and analyze the chronological...

This research introduces an innovative GDP nowcasting strategy tailored for developing countries, specifically addressing challenges related to limited data timeliness. The study centers on Bolivia, where the official monthly indicator of economic growth is released with a substantial delay of...

The large number of financial crises in emerging markets over the past ten years has left many observers, both from academia and financial institutions, puzzled by an apparent lack of homogenous causal relations between endogenous economic variables and the bursting of large financial shocks....

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques, based on machine learning, can readily be employed when treating volatility as a univariate, daily time-series. However, econometric studies have shown that...

Out-of-sample tests are subject to look-ahead bias when a forecaster constructs a model using an intuition derived from empirical patterns in the test sample. Even if model parameters are estimated without the test sample, information from it affects a forecaster's model choice. Since such...

We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted...