Hybrid neural-evolutionary model for electricity price forecasting

Evolving artificial neural networks has attracted much attention among researchers recently, especially in the fields where plenty of data exist but explanatory theories and models are lacking or based upon too many simplifying assumptions. Financial time series forecasting is one of them. A hybrid model is used to forecast the hourly electricity price from the California Power Exchange. A collaborative approach is adopted to combine ANN and evolutionary algorithm. The main contributions of this thesis include: Investigated the effect of changing values of several important parameters on the performance of the model, and selected the best combination of these parameters; good forecasting results have been obtained with the implemented hybrid model when the best combination of parameters is used. The lowest MAPE through a single run is 5. 28134%. And the lowest averaged MAPE over 10 runs is 6.088%, over 30 runs is 6.786%; through the investigation of the parameter period, it is found that by including future values of the homogenous moments of the instant being forecasted into the input vector, forecasting accuracy is greatly enhanced. A comparison of results with other works reported in the literature shows that the proposed model gives superior performance on the same data set.

Low-frequency volatility of yen interest rate swap market in relation to macroeconomic risk

Using ‘low-frequency’ volatility extracted from aggregate volatility shocks in interest rate swap (hereafter, IRS) market, this paper investigates whether Japanese yen IRS volatility can be explained by macroeconomic risks. The analysis suggests that this low-frequency yen IRS volatility has strong and positive association with most of the macroeconomic risk proxies (e.g., volatility of consumer price index, industrial production volatility, foreign exchange volatility, slope of the term structure and money supply) with the exception of the unemployment rate, which is negatively related to IRS volatility. This finding is fairly consistent with the argument that the greater the macroeconomic risk the greater is the use of derivative instruments to hedge or speculate. The relationship between the macroeconomic risks and IRS volatility varies slightly across the different swap maturities but is robust to alternative volatility specifications. This linkage between swap market and macroeconomy has practical implications since market makers and hedgers use the swap rate as benchmark for pricing long-term interest rates, corporate bonds and various other securities.

Volatility spillover between New Zealand stock market returns and exchange rate changes before and after the 1997 Asian financial crisis

Researchers in the last decade have been investigating the interdependence of stock returns and exchange rate changes within the same economy. Kanas (2000) and Yang and Doong (2004) find that for the G-7 countries, in general, the volatility of the stock market spills over to the exchange rate market but that volatility spillovers from the exchange rate market to the stock market are insignificant. Chen, Naylor, and Lu (2004) find that NZ individual firm returns are significantly exposed to exchange rate changes. This study complements their work by investigating the volatility spillover between the stock market and the foreign exchange market within the NZ economy.

Volatility linkages and spillovers in stock and bond markets : some international evidence

This study investigates the transmission of market-wide volatility between the equity markets and bond markets of Japan, Germany, the U. K., and the U. S. To measure the volatility transmission, the BEKK- a decomposition approach to the multivariate GARCH (1,1) model, is used to examine the cross-market contemporaneous effect of information arrival. Our results suggest that within the domestic cross markets, the volatility transmission is undirectional from the stock market to the bond market. Evidence from international cross-market analysis is mixed, with strong evidence on volatility spillover among these international stock markets, but weak evidence between international stock and bond markets. In addition, there are significant bi-directional volatility transmissions between stock markets in Germany and the U. K., and between Germany and the U. S. The volatility transmissions among these markets suggest that the international diversification of bonds is not prevalent.

Volatility transmissions between stock and bond markets : evidence from Japan and the U.S.

This study attempts to investigate the transmission of market-wide volatility between the equity markets and bond markets of Japan and the U.S. To measure the volatility transmission, the BEKK (Baba, Engle, Kraft and Kroner, 1990) method, a decomposition approach of the multivariate GARCH (1,1) model, is used to examine the cross-market contemporaneous effect of information arrival. The time series analysis provides evidence to the long-run phenomena of causality in conditional variances of paired assets within the local and international markets. Within various pairings, some evidence of bi-directional volatility transmissions such as informational linkages have been observed. Our empirical results suggest that within the domestic cross markets, the volatility transmission is unidirectional from the stock market to the bond market. Evidence from international cross-market analysis is mixed, with strong evidence on volatility spillover among these international stock markets, but weak evidence between international stock and bond markets. In addition, there are significant directional volatility transmissions between DJI index and FTSE100 index, and between DJI index and DAX200 index. The volatility transmission between these two markets indicates that the international diversification of bonds is not prevalent.

Volatility transmission between stock and bond markets : evidence from US and Australia

This paper investigates the cross-market informational dependence between these assets under disparate interest rate conditions of the U.S and Australia. With conditional variance as a proxy for volatility, we use the BEKK – a matricular decomposition of the bivariate GARCH (1,1) model to examine the cross-market contemporaneous effect of information arrival. Applying the model to the stock and bond indices of both countries, we find evidence of volatility spillover, thereby supporting the notion of informational dependence between each market

Modeling volatility and changes in the swap spread

We investigate the determinants of changes in U.S. interest rate swap spreads using a model that explicitly allows for volatility interactions between swaps of different terms to maturity. Changes in the swap spread are found to be positively related to interest rate volatility, to changes in the default risk premium in the corporate bond market, and to changes in the liquidity premium for government securities. Swap spread changes are negatively related to changes in the level of interest rates and changes in the slope of the term structure. We also find that there is a strong and significant volatility interaction among spreads for swaps of different maturities and that the process for the conditional variance of the spread is highly persistent across all maturities.

Short term load forecasting using interval type-2 fuzzy logic systems

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy may drop due to presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with extra degrees of freedom, are an excellent tool for handling prevailing uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models appropriately approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks used in this study.

Exploring Australian housing supply volatility

This study examines the volatility series of housing supply in Australia. A Generalised Autoregressive Conditional Heteroskedasticity-in-Mean (GARCH-M) model is employed to analyse the volatility series of Australian housing supply over the study period of 1974-2010. The results show the volatility of housing starts is negatively linked to housing starts, suggesting that higher uncertainty does lower housing starts. The results also reveal that the uncertainty of housing starts is also captured by the volatilities of interest rates and construction costs. Therefore policy makers should monitor and attempt to minimise the volatility of housing supply. These steps will enhance housing construction activities and increase the availability of housing supply to potential home buyers.

Implied volatility smiles, option mispricing and net buying pressure : evidence around the global financial crisis

Using the recent global financial crisis as an exogenous setting, we examine the presence and source of implied volatility smile phenomena in Australian S&P ASX 200 index options. We find a pronounced implied volatility smile for index puts in both bull and bear markets and a smile for index calls in the bear but not bull market. Implied volatilities of out-of-the money puts tend to be upwards biased whilst those of calls tend to be downwards biased. We also find that the bias in implied volatilities yields excess returns based on unhedged and delta-neutral trading strategies, suggesting that implied volatilities are related to option mispricing. Net buying pressure from market participants appears to be a source of mispricing in the case of out-of-the-money index puts with excess demand particularly pronounced during the bull period before the global financial crisis unfolded.

Investment and oil price volatility

In this note, we consider the relationship between oil price volatility and firm returns for 560 firms listed on the New York Stock Exchange. Using daily time series data from 2000 to 2008, we find that oil price volatility increases firm returns for the majority of the firms in our sample.

Does the choice of estimator matter when forecasting returns?

While the literature concerned with the predictability of stock returns is huge, surprisingly little is known when it comes to role of the choice of estimator of the predictive regression. Ideally, the choice of estimator should be rooted in the salient features of the data. In case of predictive regressions of returns there are at least three such features; (i) returns are heteroskedastic, (ii) predictors are persistent, and (iii) regression errors are correlated with predictor innovations. In this paper we examine if the accounting of these features in the estimation process has any bearing on our ability to forecast future returns. The results suggest that it does.

Interval type-2 fuzzy logic systems for load forecasting : a comparative study

Accurate short term load forecasting (STLF) is essential for a variety of decision-making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with additional degrees of freedom, are an excellent tool for handling uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models precisely approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks and traditional type-1 Takagi-Sugeno-Kang (TSK) FLSs.