Working Paper |
File Downloads |
Abstract Views |
Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
A new method for automated noise cancellation in electromagnetic field measurement |
0 |
0 |
0 |
21 |
0 |
0 |
5 |
132 |
A note on averaging day-ahead electricity price forecasts across calibration windows |
0 |
1 |
13 |
155 |
1 |
4 |
26 |
245 |
A note on using the Hodrick-Prescott filter in electricity markets |
0 |
1 |
1 |
114 |
1 |
2 |
10 |
287 |
A review of electricity price forecasting: The past, the present and the future |
1 |
1 |
2 |
244 |
1 |
2 |
4 |
334 |
A semiparametric factor model for electricity forward curve dynamics |
0 |
0 |
0 |
108 |
0 |
1 |
1 |
255 |
A semiparametric factor model for electricity forward curve dynamics |
0 |
0 |
0 |
69 |
0 |
0 |
1 |
177 |
A short history of the VOLAX - or how we tried to trade implied volatility (Krotka historia VOLAX-u - czyli jak probowano handlowac implikowana zmiennoscia) |
1 |
1 |
1 |
18 |
1 |
1 |
3 |
133 |
A simple model of price formation |
0 |
0 |
0 |
33 |
0 |
1 |
5 |
123 |
An empirical comparison of alternate regime-switching models or electricity spot prices |
0 |
0 |
1 |
165 |
0 |
0 |
5 |
380 |
An empirical comparison of alternate schemes for combining electricity spot price forecasts |
0 |
0 |
0 |
159 |
0 |
0 |
2 |
408 |
An introduction to simulation of risk processes |
0 |
0 |
2 |
48 |
0 |
0 |
3 |
212 |
Analysis of ROBECO data by neural networks |
0 |
0 |
0 |
8 |
1 |
1 |
3 |
82 |
Automated variable selection and shrinkage for day-ahead electricity price forecasting |
0 |
0 |
0 |
160 |
0 |
0 |
2 |
321 |
Averaging predictive distributions across calibration windows for day-ahead electricity price forecasting |
0 |
0 |
1 |
17 |
0 |
0 |
6 |
44 |
Balancing RES generation: Profitability of an energy trader |
0 |
0 |
0 |
73 |
0 |
0 |
8 |
145 |
Beating the naive: Combining LASSO with naive intraday electricity price forecasts |
0 |
0 |
1 |
76 |
1 |
3 |
4 |
119 |
Bezpieczeństwo elektroenergetyczne: Ryzyko > Zarządzanie ryzykiem > Bezpieczeństwo |
0 |
0 |
0 |
29 |
0 |
1 |
1 |
249 |
Black swans or dragon kings? A simple test for deviations from the power law |
0 |
0 |
0 |
114 |
0 |
1 |
2 |
396 |
Black swans or dragon kings? A simple test for deviations from the power law |
0 |
0 |
0 |
42 |
0 |
1 |
3 |
129 |
Black swans or dragon kings? A simple test for deviations from the power law |
0 |
0 |
0 |
69 |
1 |
1 |
2 |
178 |
Blackouts, risk, and fat-tailed distributions |
0 |
0 |
1 |
200 |
0 |
3 |
4 |
596 |
Building Loss Models |
0 |
0 |
0 |
319 |
0 |
1 |
2 |
1,429 |
Building Loss Models |
0 |
0 |
0 |
25 |
0 |
0 |
2 |
171 |
Building loss models |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
46 |
Calibration window selection based on change-point detection for forecasting electricity prices |
0 |
0 |
0 |
35 |
0 |
1 |
2 |
49 |
Carbon pricing, forward risk premiums and pass-through rates in Australian electricity futures markets |
0 |
0 |
0 |
74 |
0 |
0 |
5 |
144 |
Computationally intensive Value at Risk calculations |
0 |
0 |
0 |
29 |
0 |
0 |
0 |
140 |
Computing electricity spot price prediction intervals using quantile regression and forecast averaging |
1 |
1 |
1 |
224 |
1 |
3 |
5 |
412 |
Convenience yields and risk premiums in the EU-ETS - Evidence from the Kyoto commitment period |
0 |
0 |
0 |
75 |
0 |
0 |
2 |
191 |
Convenience yields for CO2 emission allowance futures contracts |
0 |
0 |
1 |
335 |
0 |
0 |
5 |
1,015 |
Correction to: "On the Chambers-Mallows-Stuck Method for Simulating Skewed Stable Random Variables" |
0 |
0 |
1 |
107 |
0 |
0 |
5 |
392 |
Correction to: "On the Chambers–Mallows–Stuck Method for Simulating Skewed Stable Random Variables" |
0 |
2 |
4 |
90 |
5 |
12 |
16 |
317 |
Cost-benefit analysis of a municipal waste management project: Using a survey of professional forecasters to provide reliable projections until 2035 |
0 |
1 |
15 |
15 |
0 |
3 |
38 |
38 |
Data-driven simulation modeling of the checkout process in supermarkets: Insights for decision support in retail operations |
0 |
0 |
4 |
34 |
0 |
1 |
12 |
119 |
Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks |
1 |
1 |
1 |
55 |
1 |
1 |
2 |
78 |
Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models |
0 |
3 |
9 |
237 |
3 |
8 |
20 |
457 |
Difficulty is critical: Psychological factors in modeling diffusion of green products and practices |
0 |
0 |
1 |
55 |
0 |
0 |
3 |
169 |
Diffusion and adoption of dynamic electricity tariffs: An agent-based modeling approach |
0 |
0 |
0 |
75 |
1 |
1 |
4 |
156 |
Diffusion of innovation within an agent-based model: Spinsons, independence and advertising |
0 |
0 |
1 |
186 |
0 |
6 |
9 |
443 |
Discounting of delayed payoffs (Rzecz o dyskontowaniu odroczonych wyplat) |
0 |
0 |
0 |
10 |
0 |
0 |
2 |
77 |
Distributional neural networks for electricity price forecasting |
1 |
1 |
2 |
34 |
2 |
2 |
8 |
57 |
Efficient estimation of Markov regime-switching models: An application to electricity spot prices |
0 |
0 |
0 |
371 |
0 |
0 |
5 |
847 |
Efficient estimation of Markov regime-switching models: An application to electricity wholesale market prices |
0 |
0 |
0 |
175 |
1 |
1 |
4 |
346 |
Efficient forecasting of electricity spot prices with expert and LASSO models |
0 |
0 |
1 |
52 |
0 |
1 |
4 |
87 |
Electricity Price Forecasting: The Dawn of Machine Learning |
0 |
0 |
6 |
171 |
0 |
1 |
18 |
340 |
Electricity price forecasting |
1 |
3 |
15 |
535 |
1 |
5 |
28 |
1,628 |
Electricity price forecasting |
1 |
2 |
6 |
155 |
2 |
5 |
14 |
332 |
Electricity price forecasting: A review of the state-of-the-art with a look into the future |
1 |
2 |
8 |
367 |
3 |
9 |
35 |
861 |
Energy forecasting: A review and outlook |
0 |
1 |
2 |
300 |
1 |
3 |
4 |
790 |
Energy price risk management |
0 |
0 |
0 |
60 |
0 |
0 |
4 |
225 |
Erratum to 'Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark' [Appl. Energy 293 (2021) 116983] |
0 |
0 |
4 |
50 |
0 |
1 |
19 |
116 |
Estimating long range dependence: finite sample properties and confidence intervals |
0 |
0 |
2 |
90 |
0 |
0 |
6 |
339 |
Evaluating the performance of VaR models in energy markets |
0 |
0 |
1 |
151 |
0 |
0 |
2 |
232 |
Evolution in a changing environment |
0 |
0 |
0 |
13 |
0 |
0 |
3 |
115 |
Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market |
2 |
3 |
76 |
76 |
2 |
4 |
114 |
114 |
FORECASTING SPOT ELECTRICITY PRICES WITH TIME SERIES MODELS |
0 |
1 |
3 |
1,272 |
1 |
3 |
11 |
2,495 |
FX Smile in the Heston Model |
0 |
0 |
0 |
143 |
0 |
0 |
13 |
448 |
FX Smile in the Heston Model |
0 |
0 |
0 |
34 |
0 |
0 |
3 |
189 |
FX Smile in the Heston Model |
0 |
0 |
0 |
57 |
0 |
0 |
1 |
204 |
FX smile in the Heston model |
0 |
0 |
1 |
100 |
1 |
1 |
4 |
305 |
Forecasting Electricity Prices |
1 |
3 |
10 |
48 |
2 |
11 |
31 |
106 |
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark |
0 |
0 |
1 |
38 |
1 |
1 |
3 |
75 |
Forecasting of daily electricity prices with factor models: Utilizing intra-day and inter-zone relationships |
0 |
0 |
0 |
115 |
0 |
0 |
4 |
185 |
Forecasting of daily electricity spot prices by incorporating intra-day relationships: Evidence form the UK power market |
0 |
0 |
0 |
177 |
0 |
0 |
2 |
362 |
Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models |
0 |
1 |
1 |
230 |
0 |
1 |
4 |
573 |
Forecasting the occurrence of electricity price spikes in the UK power market |
1 |
1 |
2 |
222 |
1 |
2 |
11 |
465 |
Forecasting wholesale electricity prices: A review of time series models |
0 |
0 |
0 |
127 |
0 |
1 |
3 |
303 |
Going green: Agent-based modeling of the diffusion of dynamic electricity tariffs |
0 |
0 |
2 |
139 |
0 |
3 |
15 |
278 |
Goodness-of-fit testing for regime-switching models |
0 |
0 |
0 |
140 |
0 |
2 |
2 |
243 |
Goodness-of-fit testing for the marginal distribution of regime-switching models |
0 |
0 |
0 |
55 |
0 |
0 |
2 |
158 |
Habitat momentum |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
65 |
Heavy tails and electricity prices |
0 |
1 |
2 |
33 |
0 |
1 |
7 |
167 |
Heavy tails and electricity prices: Do time series models with non-Gaussian noise forecast better than their Gaussian counterparts? |
0 |
0 |
0 |
226 |
0 |
1 |
2 |
507 |
Heavy-tailed distributions in VaR calculations |
0 |
0 |
3 |
322 |
0 |
1 |
5 |
895 |
Heavy-tails and regime-switching in electricity prices |
0 |
1 |
1 |
79 |
0 |
3 |
7 |
174 |
How effective is advertising in duopoly markets? |
0 |
0 |
0 |
9 |
0 |
1 |
4 |
80 |
How effective is advertising in duopoly markets? |
0 |
0 |
0 |
285 |
0 |
0 |
0 |
1,048 |
Hurst analysis of electricity price dynamics |
0 |
0 |
0 |
63 |
3 |
3 |
7 |
201 |
Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling |
1 |
1 |
1 |
121 |
1 |
2 |
3 |
226 |
Impact of social interactions on demand curves for innovative products |
0 |
0 |
0 |
69 |
0 |
0 |
1 |
101 |
Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models |
0 |
0 |
3 |
168 |
1 |
3 |
8 |
319 |
Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Parameter-rich models estimated via the LASSO |
0 |
1 |
1 |
54 |
0 |
2 |
3 |
114 |
Improving short term load forecast accuracy via combining sister forecasts |
0 |
0 |
0 |
234 |
0 |
0 |
2 |
441 |
Inference for Markov-regime switching models of electricity spot prices |
0 |
0 |
4 |
224 |
0 |
0 |
15 |
494 |
Interval forecasting of spot electricity prices |
0 |
0 |
0 |
31 |
0 |
0 |
2 |
120 |
Is Human Visual Activity in Simple Human-Computer Interaction Search Tasks a Lévy Flight? |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
31 |
Levy-stable distributions revisited: tail index > 2 does not exclude the Levy-stable regime |
0 |
0 |
0 |
621 |
1 |
1 |
4 |
1,458 |
Levy-stable distributions revisited: tail index > 2 does not exclude the Levy-stable regime |
0 |
0 |
0 |
106 |
0 |
1 |
9 |
545 |
Loss Distributions |
0 |
1 |
5 |
181 |
2 |
3 |
13 |
531 |
Loss functions in regression models: Impact on profits and risk in day-ahead electricity trading |
4 |
21 |
64 |
76 |
11 |
34 |
108 |
134 |
Market price of risk implied by Asian-style electricity options |
0 |
0 |
0 |
629 |
0 |
0 |
1 |
1,424 |
Measuring long-range dependence in electricity prices |
0 |
0 |
0 |
50 |
0 |
1 |
4 |
135 |
Merging quantile regression with forecast averaging to obtain more accurate interval forecasts of Nord Pool spot prices |
0 |
0 |
2 |
140 |
0 |
1 |
5 |
235 |
Modeling and forecasting electricity loads: A comparison |
0 |
0 |
0 |
1,265 |
3 |
4 |
5 |
2,858 |
Modeling and forecasting of the long-term seasonal component of the EEX and Nord Pool spot prices |
1 |
2 |
2 |
153 |
1 |
2 |
7 |
331 |
Modeling catastrophe claims with left-truncated severity distributions (extended version) |
0 |
0 |
0 |
28 |
0 |
0 |
1 |
183 |
Modeling consumer opinions towards dynamic pricing: An agent-based approach |
0 |
0 |
2 |
80 |
0 |
0 |
5 |
206 |
Modeling electricity loads in California: ARMA models with hyperbolic noise |
0 |
0 |
0 |
54 |
0 |
0 |
1 |
195 |
Modeling electricity prices with regime switching models |
0 |
0 |
0 |
1,032 |
0 |
0 |
1 |
1,887 |
Modeling electricity prices: jump diffusion and regime switching |
0 |
0 |
0 |
221 |
0 |
0 |
2 |
607 |
Modeling electricity spot prices: Regime switching models with price-capped spike distributions |
0 |
0 |
0 |
106 |
0 |
0 |
0 |
199 |
Modeling highly volatile and seasonal markets: evidence from the Nord Pool electricity market |
0 |
1 |
5 |
690 |
0 |
1 |
12 |
1,268 |
Modeling the risk process in the XploRe computing environment |
0 |
0 |
0 |
131 |
0 |
1 |
3 |
363 |
Modeling the risk process in the XploRe computing environment |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
56 |
Modelling catastrophe claims with left-truncated severity distributions (extended version) |
0 |
0 |
0 |
49 |
1 |
3 |
5 |
250 |
Modelling price spikes in electricity markets - the impact of load, weather and capacity |
0 |
1 |
6 |
206 |
1 |
3 |
13 |
475 |
Models for Heavy-tailed Asset Returns |
0 |
0 |
1 |
40 |
0 |
2 |
6 |
200 |
Models for Heavy-tailed Asset Returns |
0 |
0 |
0 |
201 |
0 |
0 |
3 |
454 |
Models for heavy-tailed asset returns |
0 |
1 |
1 |
71 |
0 |
1 |
3 |
210 |
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx |
0 |
0 |
3 |
52 |
3 |
4 |
16 |
125 |
Neural networks in day-ahead electricity price forecasting: Single vs. multiple outputs |
0 |
1 |
1 |
44 |
0 |
1 |
5 |
66 |
On detecting and modeling periodic correlation in financial data |
0 |
0 |
0 |
280 |
0 |
0 |
0 |
633 |
On the importance of the long-term seasonal component in day-ahead electricity price forecasting |
0 |
0 |
2 |
116 |
0 |
2 |
12 |
209 |
On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Part II – Probabilistic forecasting |
1 |
1 |
2 |
132 |
1 |
1 |
4 |
281 |
Origins of scaling in FX markets |
0 |
0 |
0 |
35 |
0 |
1 |
1 |
154 |
Origins of the scaling behaviour in the dynamics of financial data |
0 |
0 |
0 |
18 |
0 |
2 |
3 |
127 |
Outflow Dynamics in Modeling Oligopoly Markets: The Case of the Mobile Telecommunications Market in Poland |
0 |
0 |
1 |
25 |
0 |
0 |
2 |
107 |
Outflow Dynamics in Modeling Oligopoly Markets: The Case of the Mobile Telecommunications Market in Poland |
0 |
1 |
2 |
56 |
0 |
2 |
8 |
204 |
Outlier Treatment and Robust Approaches for Modeling Electricity Spot Prices |
0 |
0 |
2 |
242 |
0 |
1 |
7 |
687 |
Performance of the estimators of stable law parameters |
0 |
0 |
0 |
31 |
1 |
1 |
5 |
135 |
Point and interval forecasting of wholesale electricity prices: Evidence from the Nord Pool market |
0 |
0 |
0 |
199 |
0 |
0 |
0 |
648 |
PostForecasts.jl: A Julia package for probabilistic forecasting by postprocessing point predictions |
0 |
9 |
25 |
25 |
5 |
31 |
61 |
61 |
Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression |
0 |
1 |
3 |
12 |
0 |
1 |
18 |
25 |
Power markets in Poland and worldwide (Rynki energii elektrycznej w Polsce i na swiecie) |
0 |
0 |
0 |
13 |
0 |
0 |
2 |
129 |
Pricing European options on instruments with a constant dividend yield: The randomized discrete-time approach |
0 |
0 |
0 |
16 |
0 |
0 |
1 |
135 |
Principal Components Analysis in implied volatility modeling (Analiza skladowych glownych w modelowaniu implikowanej zmiennosci) |
0 |
0 |
0 |
49 |
0 |
0 |
3 |
247 |
Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts? |
0 |
0 |
2 |
157 |
2 |
4 |
14 |
304 |
Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging |
0 |
1 |
8 |
281 |
1 |
2 |
19 |
600 |
Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts |
0 |
0 |
0 |
183 |
0 |
0 |
5 |
359 |
Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts |
0 |
1 |
5 |
512 |
0 |
3 |
17 |
1,079 |
Property insurance loss distributions |
0 |
0 |
0 |
110 |
0 |
0 |
1 |
439 |
Recent advances in electricity price forecasting: A review of probabilistic forecasting |
3 |
3 |
8 |
438 |
4 |
5 |
19 |
920 |
Regime-switching models for electricity spot prices: Introducing heteroskedastic base regime dynamics and shifted spike distributions |
0 |
0 |
0 |
118 |
0 |
1 |
3 |
288 |
Regularized Quantile Regression Averaging for probabilistic electricity price forecasting |
0 |
0 |
4 |
159 |
0 |
0 |
10 |
199 |
Revisiting the relationship between spot and futures prices in the Nord Pool electricity market |
0 |
0 |
2 |
398 |
0 |
0 |
7 |
395 |
Rewiring the network. What helps an innovation to diffuse? |
0 |
0 |
0 |
111 |
0 |
0 |
3 |
100 |
Robust estimation and forecasting of the long-term seasonal component of electricity spot prices |
0 |
0 |
0 |
65 |
0 |
1 |
3 |
124 |
Robust estimation and forecasting of the long-term seasonal component of electricity spot prices |
0 |
0 |
0 |
267 |
0 |
0 |
4 |
588 |
Scaling in currency exchange: A Conditionally Exponential Decay approach |
0 |
0 |
0 |
6 |
1 |
1 |
3 |
113 |
Selection of calibration windows for day-ahead electricity price forecasting |
0 |
1 |
3 |
72 |
0 |
3 |
10 |
108 |
Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals |
0 |
1 |
4 |
159 |
0 |
1 |
6 |
314 |
Short-term electricity price forecasting with time series models: A review and evaluation |
1 |
4 |
17 |
505 |
2 |
7 |
38 |
1,306 |
Simulation modeling of epidemic risk in supermarkets: Investigating the impact of social distancing and checkout zone design |
0 |
0 |
0 |
59 |
0 |
0 |
3 |
146 |
Simulation of Risk Processes |
0 |
0 |
0 |
97 |
0 |
1 |
1 |
299 |
Simulation of risk processes |
0 |
0 |
0 |
27 |
0 |
0 |
0 |
140 |
Stable distributions |
1 |
1 |
2 |
238 |
1 |
1 |
5 |
467 |
Structure and stylized facts of a deregulated power market |
0 |
0 |
0 |
109 |
0 |
0 |
0 |
375 |
The relationship between spot and futures CO2 emission allowance prices in the EU-ETS |
0 |
0 |
4 |
324 |
0 |
1 |
10 |
1,384 |
The role of educational trainings in the diffusion of smart metering platforms: An agent-based modeling approach |
0 |
0 |
0 |
32 |
0 |
0 |
3 |
86 |
To combine or not to combine? Recent trends in electricity price forecasting |
1 |
2 |
9 |
190 |
2 |
5 |
19 |
365 |
Trading on short-term path forecasts of intraday electricity prices |
0 |
1 |
14 |
147 |
0 |
6 |
29 |
271 |
Trading on short-term path forecasts of intraday electricity prices. Part II -- Distributional Deep Neural Networks |
1 |
4 |
23 |
85 |
5 |
15 |
66 |
169 |
Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs |
0 |
0 |
0 |
97 |
0 |
0 |
2 |
215 |
Two faces of word-of-mouth: Understanding the impact of social interactions on demand curves for innovative products |
0 |
0 |
0 |
46 |
0 |
0 |
4 |
178 |
Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO |
1 |
2 |
4 |
195 |
1 |
2 |
11 |
344 |
Variance stabilizing transformations for electricity spot price forecasting |
1 |
1 |
7 |
196 |
1 |
1 |
17 |
742 |
Visualization tools for insurance risk processes |
0 |
0 |
0 |
30 |
0 |
0 |
5 |
171 |
Total Working Papers |
28 |
96 |
464 |
24,221 |
91 |
298 |
1,376 |
59,354 |
Software Item |
File Downloads |
Abstract Views |
Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
AWC_HURST: MATLAB function to compute the Hurst exponent using the Average Wavelet Coefficient (AWC) method |
1 |
5 |
21 |
955 |
2 |
6 |
38 |
2,093 |
CHRISTOF: MATLAB function to perform Christoffersen's (1998) tests of coverage |
0 |
0 |
10 |
1,076 |
0 |
1 |
23 |
2,673 |
CI_POWERTAIL: MATLAB function to test for 'dragon kings' vs. 'black swans' |
1 |
2 |
6 |
200 |
1 |
2 |
12 |
563 |
CI_WEIBULLTAIL: MATLAB function to test for 'dragon kings' in Weibull-type tails |
0 |
1 |
1 |
164 |
2 |
3 |
8 |
609 |
COR: MATLAB function to compute the correlation coefficients |
0 |
0 |
1 |
819 |
1 |
1 |
12 |
6,804 |
DESEASONALIZE: MATLAB function to remove short and long term seasonal components |
0 |
0 |
2 |
1,668 |
0 |
1 |
8 |
4,574 |
DESEASONALIZE: MATLAB function to remove short and long term seasonal components (new implementation) |
1 |
2 |
7 |
482 |
1 |
4 |
18 |
1,006 |
DFA: MATLAB function to compute the Hurst exponent using Detrended Fluctuation Analysis (DFA) |
3 |
7 |
32 |
2,969 |
9 |
21 |
74 |
7,800 |
ENERGIES_14_3249_MATLAB: MATLAB codes for computing combinations of electricity spot price forecasts as utilized in Jedrzejewski et al. (2021) Energies 14, 3249 |
0 |
1 |
2 |
29 |
1 |
3 |
7 |
88 |
ENERGIES_14_3249_PYTHON: Market data and PYTHON codes for computing electricity spot price forecasts using LASSO-estimated AR (LEAR) models as utilized in Jedrzejewski et al. (2021) Energies 14, 3249 |
0 |
2 |
12 |
135 |
1 |
6 |
44 |
394 |
ENERGIES_9_621_CODES: MATLAB codes for computing electricity spot price forecasts from "Automated variable selection and shrinkage for day-ahead electricity price forecasting" |
0 |
2 |
14 |
313 |
6 |
13 |
43 |
707 |
ENERGIES_9_621_FIGS: MATLAB codes and data for plotting figures from "Automated variable selection and shrinkage for day-ahead electricity price forecasting" |
1 |
2 |
13 |
184 |
1 |
4 |
28 |
530 |
EPFTOOLBOX: The first open-access PYTHON library for driving research in electricity price forecasting (EPF) |
0 |
0 |
24 |
140 |
3 |
16 |
101 |
661 |
E_HMM: MATLAB function to calculate Electromagnetic Field (EMF) intensity using a Hidden Markov Model (HMM) filter |
0 |
0 |
2 |
178 |
2 |
4 |
13 |
730 |
Financial Engineering Toolbox (FET) ver. 2.5 for MATLAB |
0 |
0 |
2 |
179 |
2 |
2 |
7 |
525 |
GARMANKOHLHAGEN: MATLAB function to evaluate European FX option prices in the Garman and Kohlhagen (1983) model |
0 |
0 |
1 |
236 |
0 |
0 |
2 |
899 |
GPH: MATLAB function to estimate the Hurst exponent using the Geweke-Porter-Hudak (1983) spectral estimator (periodogram regression method) |
3 |
4 |
11 |
855 |
6 |
9 |
25 |
2,094 |
HESTONFFTVANILLA: MATLAB function to evaluate European FX option prices in the Heston (1993) model using the FFT approach of Carr and Madan (1999) |
0 |
0 |
0 |
318 |
0 |
0 |
2 |
697 |
HESTONVANILLA: MATLAB function to evaluate European FX option prices in the Heston (1993) model |
0 |
0 |
0 |
145 |
0 |
0 |
1 |
407 |
HESTONVANILLAFITSMILE: MATLAB function to fit the Heston (1993) option pricing model to the FX market implied volatility smile |
0 |
0 |
1 |
184 |
0 |
1 |
4 |
552 |
HESTONVANILLALIPTON: MATLAB function to evaluate European FX option prices in the Heston (1993) model using the approach of Lipton (2002) |
0 |
0 |
2 |
104 |
0 |
1 |
6 |
384 |
HESTONVANILLASMILE: MATLAB function to compute the volatility smile implied by the Heston (1993) option pricing model |
0 |
0 |
0 |
380 |
0 |
0 |
1 |
1,122 |
HOLTWINTERS: MATLAB function to compute forecasts of the Holt-Winters exponential smoothing model |
1 |
4 |
46 |
1,070 |
3 |
14 |
109 |
3,148 |
HURST: MATLAB function to compute the Hurst exponent using R/S Analysis |
8 |
23 |
90 |
5,604 |
14 |
48 |
212 |
13,899 |
LTSCSIMPLE: MATLAB function to estimate and forecast the long-term seasonal component (LTSC) of an electricity spot price series using simple methods |
0 |
0 |
0 |
219 |
0 |
0 |
3 |
491 |
LTSCSIN: MATLAB function to estimate and forecast the long-term seasonal component (LTSC) of an electricity spot price series using sine-based methods |
1 |
1 |
1 |
170 |
2 |
2 |
12 |
394 |
LTSCWAVE: MATLAB function to estimate and forecast the long-term seasonal component (LTSC) of an electricity spot price series using wavelet-based methods |
0 |
0 |
1 |
213 |
0 |
0 |
3 |
425 |
LTSC_EXAMPLE: MATLAB example script and data for "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices" |
0 |
0 |
0 |
241 |
0 |
0 |
3 |
528 |
MFE Toolbox ver. 1.0.1 for MATLAB |
0 |
1 |
5 |
1,271 |
0 |
1 |
14 |
3,106 |
MRJD_MLE: MATLAB function to estimate parameters of a Mean-Reverting Jump-Diffusion (MRJD) process using maximum likelihood |
0 |
1 |
10 |
1,634 |
0 |
9 |
46 |
3,815 |
MRJD_PRED: MATLAB function to make a one-step ahead prediction of a Mean-Reverting Jump-Diffusion (MRJD) process |
0 |
0 |
1 |
265 |
1 |
1 |
5 |
723 |
MRJD_SIM: MATLAB function to simulate trajectories of a Mean-Reverting Jump-Diffusion (MRJD) process |
0 |
0 |
1 |
1,039 |
2 |
3 |
9 |
2,689 |
MRS2IR_EST: MATLAB function to estimate parameters of a Markov regime-switching (MRS) model with 2 independent regimes |
0 |
0 |
1 |
642 |
0 |
0 |
10 |
1,579 |
MRS2IR_SIM: MATLAB function to simulate trajectories of a Markov regime-switching (MRS) model with 2 independent regimes |
0 |
1 |
3 |
307 |
0 |
1 |
6 |
698 |
MRS2_PLOT: MATLAB function to plot calibration results for a Markov regime-switching (MRS) model with 2 regimes |
1 |
1 |
1 |
235 |
1 |
1 |
4 |
586 |
MRS3IR_EST: MATLAB function to estimate parameters of a Markov regime-switching (MRS) model with 3 independent regimes |
1 |
1 |
2 |
433 |
1 |
2 |
7 |
907 |
MRS3IR_SIM: MATLAB function to simulate trajectories of a Markov regime-switching (MRS) model with 3 independent regimes |
1 |
1 |
3 |
364 |
1 |
1 |
20 |
746 |
MRS3_PLOT: MATLAB function to plot calibration results for a Markov regime-switching (MRS) model with 3 regimes |
0 |
0 |
0 |
273 |
0 |
0 |
2 |
682 |
ORD_33_103_R_Data: R notebook and data to replicate the results presented in Nitka and Weron (2023) Operations Research and Decisions 33(3), 105-118 |
0 |
0 |
6 |
16 |
0 |
2 |
17 |
43 |
PDFHESTON: MATLAB function to evaluate the probability density function in the Heston (1993) model |
0 |
0 |
2 |
213 |
0 |
0 |
2 |
544 |
PERIODOG: MATLAB function to compute and plot the periodogram of a time series |
0 |
0 |
5 |
936 |
0 |
1 |
15 |
2,820 |
PS2R_EST: MATLAB function to estimate parameters of a 2-regime parameter switching (PS) model |
1 |
1 |
1 |
257 |
1 |
2 |
4 |
542 |
PS2R_SIM: MATLAB function to simulate trajectories of a 2-regime parameter switching (PS) model |
0 |
0 |
1 |
181 |
0 |
0 |
2 |
461 |
REMST: MATLAB function to remove trend and seasonal component using the moving average method |
0 |
0 |
2 |
1,290 |
1 |
1 |
12 |
3,618 |
RUNNINGMEDIAN: MATLAB function to compute a running median of a time series |
0 |
0 |
1 |
272 |
0 |
0 |
5 |
1,041 |
SCAR: MATLAB function to compute day-ahead predictions of the electricity spot price using the Seasonal Component AutoRegressive (SCAR) model |
0 |
0 |
0 |
175 |
0 |
0 |
4 |
378 |
SCAR_EXAMPLE: MATLAB codes and data for "On the importance of the long-term seasonal component in day-ahead electricity price forecasting" |
0 |
1 |
4 |
261 |
0 |
1 |
11 |
475 |
SIMGBM: MATLAB function to simulate trajectories of Geometric Brownian Motion (GBM) |
0 |
0 |
3 |
777 |
0 |
1 |
10 |
2,827 |
SIMGBM: MATLAB function to simulate trajectories of Geometric Brownian Motion (GBM) |
0 |
0 |
3 |
402 |
0 |
0 |
7 |
1,377 |
SIMHESTON: MATLAB function to simulate trajectories of the spot price and volatility processes in the Heston (1993) model |
0 |
0 |
0 |
510 |
0 |
1 |
3 |
1,200 |
SNDE06_EXAMPLE: MATLAB codes and data for "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models" |
0 |
0 |
0 |
146 |
1 |
2 |
4 |
283 |
STABLECULL: MATLAB function to estimate stable distribution parameters using the quantile method of McCulloch |
0 |
0 |
2 |
365 |
0 |
0 |
5 |
761 |
STABLEPDF_FFT: MATLAB function to compute the stable distribution probability density function (pdf) via FFT |
0 |
1 |
1 |
625 |
0 |
2 |
5 |
1,932 |
STABLEREG: MATLAB function to estimate stable distribution parameters using the regression method of Koutrouvelis |
0 |
0 |
1 |
359 |
0 |
2 |
8 |
922 |
STABLEREGKW: MATLAB function to estimate stable distribution parameters using the regression method of Kogon and Williams |
0 |
0 |
1 |
420 |
0 |
2 |
8 |
1,014 |
STABLERND: MATLAB function to generate random numbers from the stable distribution |
0 |
1 |
2 |
564 |
0 |
2 |
8 |
1,551 |
STF2HES: MATLAB functions for "FX smile in the Heston model" |
0 |
0 |
1 |
239 |
0 |
0 |
3 |
662 |
STF2HES_EX: MATLAB example scripts for "FX smile in the Heston model" |
0 |
0 |
2 |
132 |
0 |
0 |
12 |
481 |
The World According to Spinson (WAS): Standalone application for simulating agent-based models |
0 |
2 |
2 |
135 |
1 |
6 |
9 |
471 |
Total Software Items |
24 |
68 |
369 |
33,968 |
67 |
206 |
1,096 |
93,731 |