Wind farm power uncertainty quantification using a mean-variance estimation method

This paper proposes an innovative optimized parametric method for construction of prediction intervals (PIs) for uncertainty quantification. The mean-variance estimation (MVE) method employs two separate neural network (NN) models to estimate the mean and variance of targets. A new training method is developed in this study that adjusts parameters of NN models through minimization of a PI-based cost functions. A simulated annealing method is applied for minimization of the nonlinear non-differentiable cost function. The performance of the proposed method for PI construction is examined using monthly data sets taken from a wind farm in Australia. PIs for the wind farm power generation are constructed with five confidence levels between 50% and 90%. Demonstrated results indicate that valid PIs constructed using the optimized MVE method have a quality much better than the traditional MVE-based PIs.

An optimized mean variance estimation method for uncertainty quantification of wind power forecasts

A statistical optimized technique for rapid development of reliable prediction intervals (PIs) is presented in this study. The mean-variance estimation (MVE) technique is employed here for quantification of uncertainties related with wind power predictions. In this method, two separate neural network models are used for estimation of wind power generation and its variance. A novel PI-based training algorithm is also presented to enhance the performance of the MVE method and improve the quality of PIs. For an in-depth analysis, comprehensive experiments are conducted with seasonal datasets taken from three geographically dispersed wind farms in Australia. Five confidence levels of PIs are between 50% and 90%. Obtained results show while both traditional and optimized PIs are hypothetically valid, the optimized PIs are much more informative than the traditional MVE PIs. The informativeness of these PIs paves the way for their application in trouble-free operation and smooth integration of wind farms into energy systems. © 2014 Elsevier Ltd. All rights reserved.

A heuristic approach to Asian hedge fund allocation

The article offers information about hedge funds, which refers to pooled investments that are privately organized and professionally managed by investment managers. It examines the statistical properties of the 70 Asian hedge funds and shows the inappropriateness of the traditional mean-variance optimizer to form optimal hedge fund portfolios. The article also introduces a practical heuristic approach using the senti-variance as a measure for downside risk, and describes the risk measures and the methodology to generate optimal hedge fund portfolio.

Least squares learning and the US Treasury bill rate

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning. © 2014 Elsevier B.V.

Is carbon emissions trading profitable?

The European Carbon Emissions Trading Scheme introduced in 2005 has led to both spot and futures market trading of carbon emissions. However, despite seven years of trading, we have no knowledge on how profitable carbon emissions trading is. In this paper, we first test whether carbon forward returns predict carbon spot returns. We find strong evidence on both in-sample and out-of-sample predictability. Based on this evidence, we forecast carbon spot returns using both carbon forward returns and a constant. We consider a mean-variance investor and a CRRA investor, and show that they have higher utility and can make more statistically significant profits by following forecasts generated from the forward returns model than from a constant returns model.

Corrigendum to “Least squares learning and the US treasury bill rate” [Econ. Syst. 38 (2014) 194–204]

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning.

Fuzzy portfolio allocation models through a new risk measure and fuzzy sharpe ratio

A new portfolio risk measure that is the uncertainty of portfolio fuzzy return is introduced in this paper. Beyond the well-known Sharpe ratio (i.e., the reward-to-variability ratio) in modern portfolio theory, we initiate the so-called fuzzy Sharpe ratio in the fuzzy modeling context. In addition to the introduction of the new risk measure, we also put forward the reward-to-uncertainty ratio to assess the portfolio performance in fuzzy modeling. Corresponding to two approaches based on TM and TW fuzzy arithmetic, two portfolio optimization models are formulated in which the uncertainty of portfolio fuzzy returns is minimized, while the fuzzy Sharpe ratio is maximized. These models are solved by the fuzzy approach or by the genetic algorithm (GA). Solutions of the two proposed models are shown to be dominant in terms of portfolio return uncertainty compared with those of the conventional mean-variance optimization (MVO) model used prevalently in the financial literature. In terms of portfolio performance evaluated by the fuzzy Sharpe ratio and the reward-to-uncertainty ratio, the model using TW fuzzy arithmetic results in higher performance portfolios than those obtained by both the MVO and the fuzzy model, which employs TM fuzzy arithmetic. We also find that using the fuzzy approach for solving multiobjective problems appears to achieve more optimal solutions than using GA, although GA can offer a series of well-diversified portfolio solutions diagrammed in a Pareto frontier.

Intraday volatility interaction between the crude oil and equity markets

This paper investigates the price volatility interaction between the crude oil and equity markets in the US using 5-min data over the period 2009-2012. Our main findings can be summarised as follows. First, we find strong evidence to demonstrate that the integration of the bid-ask spread and trading volume factors leads to a better performance in predicting price volatility. Second, trading information, such as bid-ask spread, trading volume, and the price volatility from cross-markets, improves the price volatility predictability for both in-sample and out-of-sample analyses. Third, the trading strategy based on the predictive regression model that includes trading information from both markets provides significant utility gains to mean-variance investors.

Does financial news predict stock returns? New evidence from Islamic and Non-Islamic stocks

The paper extends the time-series financial news data set constructed by Garcia (2013) and uses it to examine whether financial news predicts returns of Islamic stocks differently compared to non-Islamic (conventional) stocks. We find that they do. First, while both positive and negative worded news predict most Islamic and conventional stock returns, positive words have a larger impact on both types of stock returns. Second, shock to returns from financial news reverses only in part for some stocks. Third, for a mean-variance investor, investing in Islamic stocks is relatively more profitable than investing in the corresponding conventional stocks. Fourth, we show that profits are robust to a range of time-series risk factors, namely, market risk, size-based risk, and momentum-induced risk.

Stock return predictability and determinants of predictability and profits

We examine stock return predictability for India and find strong evidence of sectoral return predictability over market return predictability. We show that mean-variance investors make statistically significant and economically meaningful profits by tracking financial ratios. For the first time in this literature, we examine the determinants of time-varying predictability and mean-variance profits. We show that both expected and unexpected shocks emanating from most financial ratios explain sectoral return predictability and profits. These are fresh contributions to the understanding of asset pricing.

Price discovery and asset pricing

This paper tests the hypothesis that price discovery influences asset pricing. Our innovations are twofold. First, we estimate time-varying price discovery for a large number (21) of Islamic stock portfolios. Second, we test using a predictive regression model whether or not price discovery predicts stock excess returns. We find from both in-sample and out-of-sample tests that all 21 portfolio excess returns are predictable. We show that a mean-variance investor by tracking price discovery is able to devise profitable trading strategies.

Residential auction clearance rates – what do they really mean?

The residential market in Melbourne is often referred to as the ‘auction capital of the world’ with approximately 30-35% of housing transfers undertaken via the auction process, most of which are conducted on the weekend and then reported in the media the following day. The most quoted measurement of auction success is via the clearance rate which simply indicates the proportion of signed contracts of sale within the auction process. At the same time the clearance rate can have a relatively large variance where the residential market can traditionally range from very good (i.e. a high clearance rate) to very poor (i.e. a low clearance rate). The subsequent effect on the market can directly increase or decrease demand, predominantly based only on this single measure of the perceived level of auction clearance rates only.

This paper examines the concept of the auction clearance rates and the heavy reliance on the only one measure of success (i.e. the clearance rates), regardless of other variables. The emphasis is placed on the auction clearance rate as one measure of demand in the housing market but within the context of the definition of market value i.e. willing buyer-willing seller. This is supported by a discussion about other variables including the asking price, the auction process itself, marketing considerations and seasonal adjustments. The findings provide an insight into how to correctly interpret the auction clearance rate in the context of the overall supply-demand interactions. Whilst the auction process is clearly an integral part of the residential transfer process it is essential that the auction clearance rate is used with caution and also in conjunction with other variables.

Hierarchical analysis of avian re-nesting behavior: mean, across-individual, and intra-individual responses

© 2015, Springer-Verlag Berlin Heidelberg. Anti-predator behavior is a key aspect of life history evolution, usually studied at the population (mean), or across-individual levels. However individuals can also differ in their intra-individual (residual) variation, but to our knowledge, this has only been studied once before in free-living animals. Here we studied the distances moved and changes in nest height and concealment between successive nesting attempts of marked pairs of grey fantails (Rhipidura albiscapa) in relation to nest fate, across the breeding season. We predicted that females (gender that decides where the nest is placed) should on average show adaptive behavioral responses to the experience of prior predation risk such that after an unsuccessful nesting attempt, replacement nests should be further away, higher from the ground, and more concealed compared with replacement nests after successful nesting attempts. We found that, on average, females moved greater distances to re-nest after unsuccessful nesting attempts (abandoned or depredated) in contrast to after a successful attempt, suggesting that re-nesting decisions are sensitive to risk. We found no consistent across-individual differences in distances moved, heights, or concealment. However, females differed by 53-fold (or more) in their intra-individual variability (i.e., predictability) with respect to distances moved and changes in nest height between nesting attempts, indicating that either some systematic variation went unexplained and/or females have inherently different predictability. Ignoring these individual differences in residual variance in our models obscured the effect of nest fate on re-nesting decisions that were evident at the mean level.