This paper provides an effective approach for forecasting return volatility via threshold heteroskedastic models of the daily asset price range, defined as the difference between the highest and lowest log asset price recorded throughout the day. We propose a general model specification, allowing the intra-day high-low price range to depend nonlinearly on past information, or an exogenous varia...

How does a boundedly rational optimizing agent make decisions? Can such an agent learn to behave rationally? We address these questions in a standard regulator environment. Our behavioral primitive is anchored to the shadow price of the state vector. The regulator forecasts the value of an additional unit of the state tomorrow, and uses this forecast to choose her control. The value of the cont...

It is very important to forecast electricity price in a deregulated electricity market for choosing the bidding strategy, and it is the most important signal for other players. It engulfs information for both customers and producers in order to maximize their profit. Thus, choosing the best method of price forecasting is a crucial task to have the most accurate forecast. In this paper the price...

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as ...

The 20thcentury has produced a rich array ofmonetary experience. The experience can be organized in several different ways. One emphasizes the role ofgold in international monetary arrangements. Early in the century, domestic monies of major trading countries were convertible into gold at a pre-established fixed price, and gold coins circulated. Currently, governments do not set the price of go...

It is very important to forecast electricity price in a deregulated electricity market for choosing the bidding strategy, and it is the most important signal for other players. It engulfs information for both customers and producers in order to maximize their profit. Thus, choosing the best method of price forecasting is a crucial task to have the most accurate forecast. In this paper the price...

ƒ In addition to accurate forecasts of the price of oil, policy-makers are interested in evaluating the risks associated with the baseline forecast to gauge the implications of alternative oil price paths for the economic outlook. A structural model of the global oil market can be used to develop risk scenarios for oil price forecasts, based on hypothetical assumptions about future demand and s...

In this paper, a neural network-driven fuzzy reasoning system for stock price forecast is proposed on the basis of improved Takagi-Sugeno reasoning model. The experimental result shows that the fuzzy neural network has such properties as fast convergence, high precision and strong function approximation ability and is suitable for real stock price prediction.

oil prices may be having a direct and indirect effect through the exchange rate on the price of agricultural commodities. in this paper, the impact of world oil prices and exchange rate shocks on prices of specific agricultural commodities including wheat, corn, soybeans and sunflowers in iran is discussed. for this purpose, vector autoregressive model with monthly data over the period 2010-199...

In this study, we try to examine whether the forecast errors obtained by the ANN models affect the breakout of financial crises. Additionally, we try to investigate how much the asymmetric information and forecast errors are reflected on the output values. In our study, we used the exchange rate of USD/TRY (USD), the Borsa Istanbul 100 Index (BIST), and gold price (GP) as our output variables o...