| 1. |
- Amilon, Henrik
(author)
-
A neural network versus Black-Scholes: A comparison of pricing and hedging performances
- 2003
-
In: Journal of Forecasting. - : Wiley. - 1099-131X .- 0277-6693. ; 22:4, s. 317-335
-
Journal article (peer-reviewed)abstract
- The Black-Scholes formula is a well-known model for pricing and hedging derivative securities. It relies, however, on several highly questionable assumptions. This paper examines whether a neural network (MLP) can be used to find a call option pricing formula better corresponding to market prices and the properties of the underlying asset than the Black-Scholes formula' The neural network method is applied to the out-of-sample pricing and delta-hedging of daily Swedish stock index call options from 1997 to 1999. The relevance of a hedge-analysis is stressed further in this paper. As benchmarks, the Black-Scholes model with historical and implied volatility estimates are used. Comparisons reveal that the neural network models outperform the benchmarks both in pricing and hedging performances. A moving block bootstrap is used to test the statistical significance of the results. Although the neural networks are superior, the results are sometimes insignificant at the 5% level. Copyright (C) 2003 John Wiley Sons, Ltd.
|
|
| 2. |
- Andersson, Eva M., 1968, et al.
(author)
-
Statistical surveillance of cyclical processes with application to turns in business cycles
- 2005
-
In: Journal of Forecasting. - : Wiley. - 1099-131X .- 0277-6693. ; 24:7, s. 465-490
-
Journal article (peer-reviewed)abstract
- On-line monitoring of cyclical processes is studied. An important application is early prediction of the next turn in business cycles by an alarm for a turn in a leading index. Three likelihood-based methods for detection of a turn are compared in detail. One of the methods is based on a hidden Markov model. The two others are based on the theory of statistical surveillance. One of these is free from parametric assumptions of the curve. Evaluations are made of the effect of different specifications of the curve and the transitions. The methods are made comparable by alarm limits, which give the same median time to the first false alarm, but also other approaches for comparability are discussed. Results are given on the expected delay time to a correct alarm, the probability of detection of a turning point within a specified time, and the predictive value of an alarm.
|
|
| 3. |
- Andersson, Michael K., et al.
(author)
-
Adjusting for information content when comparing forecast performance
- 2017
-
In: Journal of Forecasting. - : WILEY. - 0277-6693 .- 1099-131X. ; 36:7, s. 784-794
-
Journal article (peer-reviewed)abstract
- Cross-institutional forecast evaluations may be severely distorted by the fact that forecasts are made at different points in time and therefore with different amounts of information. This paper proposes a method to account for these differences when analyzing an unbalanced panel of forecasts. The method computes the timing effect and the forecaster's ability simultaneously. Monte Carlo simulation demonstrates that evaluations that do not adjust for the differences in information content may be misleading. In addition, the method is applied to a real-world dataset of 10 Swedish forecasters for the period 1999-2015. The results show that the ranking of the forecasters is affected by the proposed adjustment.
|
|
| 4. |
- Asgharian, Hossein, et al.
(author)
-
The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach
- 2013
-
In: Journal of Forecasting. - : Wiley. - 1099-131X .- 0277-6693. ; 32:7, s. 600-612
-
Journal article (peer-reviewed)abstract
- This paper applies the GARCH-MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low-frequency macroeconomic information in the GARCH-MIDAS model improves the prediction ability of the model, particularly for the long-term variance component. Moreover, the GARCH-MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle. Copyright (c) 2013 John Wiley & Sons, Ltd.
|
|
| 5. |
- Bekiros, Stelios, et al.
(author)
-
On the predictability of crude oil market: A hybrid multiscale wavelet approach
- 2020
-
In: Journal of Forecasting. - : WILEY. - 0277-6693 .- 1099-131X. ; 39:4, s. 599-614
-
Journal article (peer-reviewed)abstract
- Past research indicates that forecasting is important in understanding price dynamics across assets. We explore the potentiality of multiscale forecasting in the crude oil market by employing a wavelet multiscale analysis on returns and volatilities of Brent and West Texas Intermediate crude oil indices between January 1, 2001, and May 1, 2015. The analysis is based on a shift-invariant discrete wavelet transform, augmented by an entropy-based methodology for determining the optimal timescale decomposition under different market regimes. The empirical results show that the five-step-ahead wavelet forecast that is based on volatilities outperforms the random walk forecast, relative to the wavelet forecast that is based on returns. Optimal wavelet causality forecasting for returns is suggested across all frequencies (i.e., daily-yearly), whereas for volatilities it is suggested only up to quarterly frequencies. These results may have important implications for market efficiency and predictability of prices on the crude oil markets.
|
|
| 6. |
- Brännäs, Kurt, et al.
(author)
-
Asymmetries in conditional mean and variance : Modelling stock returns by asMA-asQGARCH
- 2004
-
In: Journal of Forecasting. - : Wiley. - 0277-6693 .- 1099-131X. ; 23:3, s. 155-171
-
Journal article (peer-reviewed)abstract
- We propose a nonlinear time series model where both the conditional mean and the conditional variance are asymmetric functions of past information. The model is particularly useful for analysing financial time series where it has been noted that there is an asymmetric impact of good news and bad news on volatility (risk) transmission. We introduce a coherent framework for testing asymmetries in the conditional mean and the conditional variance, separately or jointly. To this end we derive both a Wald and a Lagrange multiplier test. Some of the new asymmetric model's moment properties are investigated. Detailed empirical results are given for the daily returns of the composite index of the New York Stock Exchange. There is strong evidence of asymmetry in both the conditional mean and the conditional variance functions. In a genuine out-of-sample forecasting experiment the performance of the best fitted asymmetric model, having asymmetries in both conditional mean and conditional variance, is compared with an asymmetric model for the conditional mean, and with no-change forecasts. This is done both in terms of conditional mean forecasting as well as in terms of risk forecasting. Finally, the paper presents some evidence of asymmetries in the index stock returns of the Group of Seven (G7) industrialized countries.
|
|
| 7. |
- Brännäs, Kurt, et al.
(author)
-
Autoregressive-asymmetric moving average models for business-cycle data
- 1994
-
In: Journal of Forecasting. - : Wiley. - 0277-6693 .- 1099-131X. ; 13:6, s. 529-544
-
Journal article (peer-reviewed)abstract
- Much business cycle research is based on an assumption of symmetric cycles, though it is frequently argued that the downturns are steeper and more short-lived than the upturns; implying cyclical asymmetries. A new class of nonlinear autoregressive-asymmetric moving average models is introduced. These models are able to deal with symmetric as well as asymmetric phenomena. A likelihood estimation procedure and a Wald test statistic for symmetry are presented. Evidence of asymmetry is found in US real GNP growth rates.
|
|
| 8. |
- Brännäs, Kurt
(author)
-
Prediction in a duration model
- 1986
-
In: Journal of Forecasting. - : Wiley. - 0277-6693 .- 1099-131X. ; 5:2, s. 97-103
-
Journal article (peer-reviewed)
|
|
| 9. |
|
|
| 10. |
- De Rezende, Rafael B., et al.
(author)
-
Modeling and forecasting the yield curve by an extended Nelson-Siegel class of models : A quantile autoregression approach
- 2013
-
In: Journal of Forecasting. - : John Wiley & Sons. - 0277-6693 .- 1099-131X. ; 32:2, s. 111-123
-
Journal article (peer-reviewed)abstract
- This paper compares the in-sample fitting and the out-of-sample forecasting performances of four distinct Nelson-Siegel class models: Nelson-Siegel, Bliss, Svensson, and a five-factor model we propose in order to enhance the fitting flexibility. The introduction of the fifth factor resulted in superior adjustment to the data. For the forecasting exercise the paper contrasts the performances of the term structure models in association with the following econometric methods: quantile autoregression evaluated at the median, VAR, AR, and a random walk. As a pattern, the quantile procedure delivered the best results for longer forecasting horizons.
|
|
