Review of Nature-Inspired Forecast Combination Techniques

Effective and efficient planning in various areas can be significantly supported by forecasting a variable like an economy growth rate or product demand numbers for a future point in time. More than one forecast for the same variable is often available, leading to the question whether one should choose one of the single models or combine several of them to obtain a forecast with improved accuracy. In the almost 40 years of research in the area of forecast combination, an impressive amount of work has been done. This paper reviews forecast combination techniques that are nonlinear and have in some way been inspired by nature

A comparison of fuzzy regression methods for the estimation of the implied volatility smile function

The information content of option prices on the underlying asset has a special importance in finance. In particular, with the use of option implied trees, market participants may price other derivatives, estimate and forecast volatility (see e.g. the volatility index VIX), or higher moments of the underlying asset distribution. A crucial input of option implied trees is the estimation of the smile (implied volatility as a function of the strike price), which boils down to fitting a function to a limited number of existing knots. However, standard techniques require a one-to-one mapping between volatility and strike price, which is not met in the reality of financial markets, where, to a given strike price, two different implied volatilities are usually associated (coming from different types of options: call and put). In this paper we compare the widely used methodology of discarding some implied volatilities and interpolating the remaining knots with cubic splines, to a fuzzy regression approach which does not require an a-priori choice of implied volatilities. To this end, we first extend some linear fuzzy regression methods to a polynomial form and we apply them to the financial problem. The fuzzy regression methods used range from the possibilistic regression method of Tanaka, Uejima and Asai [14], the least squares fuzzy regression method of Savic and Pedrycz [13] and the hybrid method of Ishibuchi and Nii [4]

Analysis and Management of the Price Volatility in the Construction Industry

The problem of price volatility as it pertains to material and labor is a major source of risk and financial distress for all the participants in the construction industry. The overarching goal of this dissertation is to address this problem from both viewpoints of risk analysis and risk management. This dissertation offers three independent papers addressing this goal. In the first paper using the Engineering News Record Construction Cost Index (ENR CCI), a predictive model is developed. The model uses General Autoregressive Conditional Heteroscedastic (GARCH) approach which facilitates both forecasting of the future values of the CCI, and capturing and quantifying its volatilities as a separate measure of risk through the passage of time. GARCH (1,1) was recognized as the best model. The maximum volatility was observed in October 2008 and results showed persistent volatility of the CCI in the case of external economic shocks. In the second paper using the same cost index (ENR CCI), the methodology of the first paper is integrated with Value at Risk concept to cautiously estimate the escalation factor in both short and long-term construction projects for avoiding cost overrun due to price volatilities and inflation. Proposed methodology was also applied to two construction projects in which the estimated escalation factors revealed satisfactory performances in terms of accuracy and reliability. Finally, the third paper addresses the price volatility from the view of risk management. It entails two objectives of identifying and ranking of potential management strategies. The former is achieved via in-depth literature review and questionnaire interviews with industry experts. The latter is done using Analytic Hierarchy Process (AHP). Quantitative risk management methods, alike those offered in foregoing papers are considered as one of the candidates in dealing with the price volatility risk. Cost, risk allocation and duration were perceived as the most significant criteria (project indicators) in construction projects. Also, Integrated Project Delivery (IPD) with respect to project duration; quantitative risk management methods with respect to the cost; and Price Adjustment Clauses (PAC) with respect to the risk allocation, were recognized as the top strategies to manage the risk of price volatilities

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Do artificial neural networks provide improved volatility forecasts:Evidence from Asian markets

This paper enters the ongoing volatility forecasting debate by examining the ability of a wide range of Machine Learning methods (ML), and specifically Artificial Neural Network (ANN) models. The ANN models are compared against traditional econometric models for ten Asian markets using daily data for the time period from 12 September 1994 to 05 March 2018. The empirical results indicate that ML algorithms, across the range of countries, can better approximate dependencies compared to traditional benchmark models. Notably, the predictive performance of such deep learning models is superior perhaps due to its ability in capturing long-range dependencies. For example, the Neuro Fuzzy models of ANFIS and CANFIS, which outperform the EGARCH model, are more flexible in modelling both asymmetry and long memory properties. This offers new insights for Asian markets. In addition to standard statistics forecast metrics, we also consider risk management measures including the value-at-risk (VaR) average failure rate, the Kupiec LR test, the Christoffersen independence test, the expected shortfall (ES) and the dynamic quantile test. The study concludes that ML algorithms provide improving volatility forecasts in the stock markets of Asia and suggest that this may be a fruitful approach for risk management.</p