| 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 |
1 |
3 |
8 |
160 |
3 |
5 |
15 |
252 |
| A note on using the Hodrick-Prescott filter in electricity markets |
0 |
0 |
1 |
114 |
0 |
1 |
11 |
291 |
| A review of electricity price forecasting: The past, the present and the future |
0 |
1 |
2 |
245 |
1 |
4 |
7 |
338 |
| A semiparametric factor model for electricity forward curve dynamics |
0 |
0 |
0 |
108 |
0 |
0 |
1 |
255 |
| A semiparametric factor model for electricity forward curve dynamics |
0 |
0 |
0 |
69 |
1 |
2 |
4 |
181 |
| A short history of the VOLAX - or how we tried to trade implied volatility (Krotka historia VOLAX-u - czyli jak probowano handlowac implikowana zmiennoscia) |
0 |
0 |
1 |
18 |
0 |
2 |
4 |
135 |
| A simple model of price formation |
0 |
0 |
0 |
33 |
2 |
5 |
8 |
128 |
| An empirical comparison of alternate regime-switching models or electricity spot prices |
0 |
0 |
0 |
165 |
3 |
5 |
8 |
386 |
| 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 |
2 |
2 |
50 |
0 |
3 |
4 |
215 |
| Analysis of ROBECO data by neural networks |
0 |
0 |
0 |
8 |
1 |
1 |
3 |
83 |
| Automated variable selection and shrinkage for day-ahead electricity price forecasting |
0 |
0 |
1 |
161 |
3 |
7 |
10 |
330 |
| Averaging predictive distributions across calibration windows for day-ahead electricity price forecasting |
0 |
1 |
1 |
18 |
2 |
4 |
8 |
48 |
| Balancing RES generation: Profitability of an energy trader |
0 |
0 |
0 |
73 |
0 |
2 |
8 |
148 |
| Beating the naive: Combining LASSO with naive intraday electricity price forecasts |
0 |
4 |
4 |
80 |
3 |
12 |
18 |
134 |
| Bezpieczeństwo elektroenergetyczne: Ryzyko > Zarządzanie ryzykiem > Bezpieczeństwo |
0 |
0 |
0 |
29 |
0 |
2 |
3 |
251 |
| Black swans or dragon kings? A simple test for deviations from the power law |
0 |
1 |
1 |
115 |
3 |
6 |
10 |
404 |
| Black swans or dragon kings? A simple test for deviations from the power law |
0 |
0 |
0 |
69 |
1 |
2 |
4 |
180 |
| Black swans or dragon kings? A simple test for deviations from the power law |
0 |
0 |
0 |
42 |
1 |
2 |
6 |
133 |
| Blackouts, risk, and fat-tailed distributions |
0 |
0 |
0 |
200 |
0 |
0 |
3 |
596 |
| Building Loss Models |
0 |
0 |
0 |
25 |
1 |
1 |
4 |
173 |
| Building Loss Models |
0 |
0 |
0 |
319 |
1 |
2 |
5 |
1,432 |
| Building loss models |
0 |
0 |
0 |
7 |
2 |
5 |
6 |
52 |
| Calibration window selection based on change-point detection for forecasting electricity prices |
0 |
1 |
2 |
37 |
1 |
2 |
4 |
52 |
| Carbon pricing, forward risk premiums and pass-through rates in Australian electricity futures markets |
0 |
0 |
0 |
74 |
2 |
3 |
5 |
148 |
| Computationally intensive Value at Risk calculations |
0 |
0 |
0 |
29 |
1 |
1 |
1 |
141 |
| Computing electricity spot price prediction intervals using quantile regression and forecast averaging |
0 |
2 |
3 |
226 |
2 |
9 |
14 |
422 |
| Convenience yields and risk premiums in the EU-ETS - Evidence from the Kyoto commitment period |
0 |
0 |
0 |
75 |
2 |
3 |
5 |
195 |
| Convenience yields for CO₂ emission allowance futures contracts |
0 |
0 |
0 |
335 |
1 |
2 |
5 |
1,017 |
| Correction to: "On the Chambers-Mallows-Stuck Method for Simulating Skewed Stable Random Variables" |
0 |
0 |
1 |
107 |
1 |
2 |
6 |
394 |
| Correction to: "On the Chambers–Mallows–Stuck Method for Simulating Skewed Stable Random Variables" |
0 |
0 |
3 |
90 |
4 |
6 |
25 |
328 |
| Cost-benefit analysis of a municipal waste management project: Using a survey of professional forecasters to provide reliable projections until 2035 |
1 |
3 |
19 |
19 |
6 |
9 |
50 |
50 |
| Data-driven simulation modeling of the checkout process in supermarkets: Insights for decision support in retail operations |
2 |
2 |
4 |
36 |
4 |
7 |
15 |
127 |
| Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks |
0 |
1 |
3 |
57 |
3 |
7 |
10 |
87 |
| Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models |
2 |
3 |
10 |
241 |
6 |
13 |
28 |
472 |
| Difficulty is critical: Psychological factors in modeling diffusion of green products and practices |
0 |
0 |
1 |
55 |
1 |
3 |
7 |
173 |
| Diffusion and adoption of dynamic electricity tariffs: An agent-based modeling approach |
0 |
0 |
0 |
75 |
3 |
3 |
6 |
159 |
| Diffusion of innovation within an agent-based model: Spinsons, independence and advertising |
0 |
0 |
0 |
186 |
2 |
3 |
11 |
447 |
| Discounting of delayed payoffs (Rzecz o dyskontowaniu odroczonych wyplat) |
0 |
0 |
0 |
10 |
0 |
0 |
2 |
77 |
| Distributional neural networks for electricity price forecasting |
0 |
2 |
4 |
36 |
1 |
4 |
10 |
63 |
| Efficient estimation of Markov regime-switching models: An application to electricity spot prices |
0 |
0 |
0 |
371 |
1 |
3 |
7 |
850 |
| Efficient estimation of Markov regime-switching models: An application to electricity wholesale market prices |
0 |
0 |
0 |
175 |
2 |
5 |
6 |
351 |
| Efficient forecasting of electricity spot prices with expert and LASSO models |
1 |
1 |
2 |
53 |
6 |
9 |
13 |
96 |
| Electricity Price Forecasting: The Dawn of Machine Learning |
0 |
1 |
4 |
172 |
0 |
2 |
12 |
343 |
| Electricity price forecasting |
0 |
1 |
15 |
539 |
1 |
2 |
26 |
1,638 |
| Electricity price forecasting |
0 |
0 |
5 |
155 |
1 |
4 |
14 |
336 |
| Electricity price forecasting: A review of the state-of-the-art with a look into the future |
4 |
7 |
17 |
378 |
11 |
28 |
60 |
900 |
| Energy forecasting: A review and outlook |
0 |
0 |
2 |
300 |
0 |
9 |
15 |
801 |
| Energy price risk management |
0 |
0 |
0 |
60 |
2 |
3 |
5 |
228 |
| 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 |
52 |
3 |
4 |
25 |
128 |
| Estimating long range dependence: finite sample properties and confidence intervals |
0 |
0 |
1 |
91 |
1 |
3 |
6 |
343 |
| Evaluating the performance of VaR models in energy markets |
0 |
0 |
1 |
151 |
2 |
2 |
4 |
234 |
| Evolution in a changing environment |
0 |
0 |
0 |
13 |
2 |
2 |
6 |
118 |
| Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market |
1 |
6 |
36 |
87 |
1 |
10 |
64 |
137 |
| FORECASTING SPOT ELECTRICITY PRICES WITH TIME SERIES MODELS |
0 |
0 |
2 |
1,272 |
0 |
1 |
8 |
2,496 |
| FX Smile in the Heston Model |
0 |
0 |
0 |
57 |
1 |
1 |
2 |
205 |
| FX Smile in the Heston Model |
0 |
0 |
0 |
34 |
2 |
3 |
6 |
194 |
| FX Smile in the Heston Model |
0 |
0 |
0 |
143 |
1 |
3 |
4 |
452 |
| FX smile in the Heston model |
0 |
0 |
1 |
100 |
2 |
3 |
7 |
308 |
| Forecasting Electricity Prices |
2 |
5 |
12 |
54 |
5 |
10 |
35 |
120 |
| Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark |
0 |
0 |
2 |
39 |
1 |
3 |
8 |
80 |
| Forecasting of daily electricity prices with factor models: Utilizing intra-day and inter-zone relationships |
0 |
1 |
1 |
116 |
0 |
1 |
4 |
186 |
| Forecasting of daily electricity spot prices by incorporating intra-day relationships: Evidence form the UK power market |
0 |
0 |
0 |
177 |
2 |
3 |
6 |
366 |
| Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models |
0 |
1 |
2 |
231 |
4 |
7 |
9 |
580 |
| Forecasting the occurrence of electricity price spikes in the UK power market |
0 |
1 |
4 |
224 |
1 |
2 |
9 |
468 |
| Forecasting wholesale electricity prices: A review of time series models |
0 |
0 |
0 |
127 |
1 |
1 |
4 |
304 |
| Going green: Agent-based modeling of the diffusion of dynamic electricity tariffs |
0 |
0 |
0 |
139 |
1 |
1 |
11 |
280 |
| Goodness-of-fit testing for regime-switching models |
0 |
0 |
0 |
140 |
0 |
0 |
4 |
245 |
| Goodness-of-fit testing for the marginal distribution of regime-switching models |
0 |
0 |
0 |
55 |
0 |
0 |
3 |
159 |
| Habitat momentum |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
65 |
| Heavy tails and electricity prices |
0 |
0 |
2 |
33 |
2 |
3 |
8 |
171 |
| Heavy tails and electricity prices: Do time series models with non-Gaussian noise forecast better than their Gaussian counterparts? |
0 |
0 |
0 |
226 |
1 |
2 |
3 |
509 |
| Heavy-tailed distributions in VaR calculations |
0 |
0 |
2 |
322 |
3 |
11 |
15 |
906 |
| Heavy-tails and regime-switching in electricity prices |
0 |
0 |
1 |
79 |
5 |
7 |
14 |
183 |
| How effective is advertising in duopoly markets? |
0 |
0 |
0 |
285 |
1 |
1 |
1 |
1,049 |
| How effective is advertising in duopoly markets? |
0 |
0 |
0 |
9 |
1 |
1 |
3 |
81 |
| Hurst analysis of electricity price dynamics |
0 |
0 |
0 |
63 |
1 |
1 |
9 |
203 |
| Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling |
0 |
0 |
1 |
121 |
3 |
6 |
10 |
233 |
| Impact of social interactions on demand curves for innovative products |
0 |
1 |
1 |
70 |
1 |
3 |
5 |
105 |
| Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models |
1 |
3 |
5 |
171 |
4 |
7 |
15 |
327 |
| Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Parameter-rich models estimated via the LASSO |
0 |
1 |
2 |
55 |
2 |
5 |
8 |
120 |
| Improving short term load forecast accuracy via combining sister forecasts |
0 |
1 |
1 |
235 |
3 |
4 |
6 |
445 |
| Inference for Markov-regime switching models of electricity spot prices |
1 |
1 |
1 |
225 |
5 |
6 |
11 |
500 |
| Interval forecasting of spot electricity prices |
0 |
0 |
0 |
31 |
0 |
2 |
5 |
123 |
| 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 |
106 |
0 |
0 |
6 |
548 |
| Levy-stable distributions revisited: tail index > 2 does not exclude the Levy-stable regime |
0 |
0 |
0 |
621 |
0 |
0 |
3 |
1,460 |
| Loss Distributions |
0 |
0 |
1 |
181 |
2 |
3 |
10 |
535 |
| Loss functions in regression models: Impact on profits and risk in day-ahead electricity trading |
2 |
6 |
53 |
87 |
2 |
10 |
80 |
150 |
| Market price of risk implied by Asian-style electricity options |
0 |
0 |
0 |
629 |
0 |
1 |
1 |
1,425 |
| Measuring long-range dependence in electricity prices |
0 |
0 |
0 |
50 |
0 |
0 |
3 |
135 |
| Merging quantile regression with forecast averaging to obtain more accurate interval forecasts of Nord Pool spot prices |
0 |
1 |
3 |
142 |
2 |
5 |
13 |
244 |
| Modeling and forecasting electricity loads: A comparison |
0 |
0 |
0 |
1,265 |
2 |
2 |
7 |
2,860 |
| Modeling and forecasting of the long-term seasonal component of the EEX and Nord Pool spot prices |
0 |
1 |
3 |
154 |
1 |
3 |
7 |
334 |
| Modeling catastrophe claims with left-truncated severity distributions (extended version) |
0 |
0 |
0 |
28 |
0 |
1 |
2 |
184 |
| Modeling consumer opinions towards dynamic pricing: An agent-based approach |
0 |
0 |
2 |
80 |
0 |
0 |
5 |
207 |
| Modeling electricity loads in California: ARMA models with hyperbolic noise |
0 |
0 |
0 |
54 |
0 |
2 |
3 |
197 |
| Modeling electricity prices with regime switching models |
0 |
1 |
1 |
1,033 |
0 |
2 |
2 |
1,889 |
| Modeling electricity prices: jump diffusion and regime switching |
1 |
1 |
1 |
222 |
4 |
4 |
5 |
611 |
| Modeling electricity spot prices: Regime switching models with price-capped spike distributions |
0 |
0 |
0 |
106 |
2 |
2 |
2 |
201 |
| Modeling highly volatile and seasonal markets: evidence from the Nord Pool electricity market |
0 |
0 |
4 |
690 |
0 |
2 |
10 |
1,272 |
| Modeling the risk process in the XploRe computing environment |
0 |
0 |
0 |
131 |
1 |
1 |
4 |
364 |
| 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 |
2 |
3 |
6 |
253 |
| Modelling price spikes in electricity markets - the impact of load, weather and capacity |
1 |
3 |
5 |
209 |
5 |
11 |
16 |
486 |
| Models for Heavy-tailed Asset Returns |
1 |
1 |
1 |
41 |
5 |
7 |
12 |
207 |
| Models for Heavy-tailed Asset Returns |
1 |
1 |
1 |
202 |
3 |
5 |
7 |
460 |
| Models for heavy-tailed asset returns |
0 |
0 |
1 |
71 |
2 |
3 |
5 |
213 |
| Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx |
0 |
0 |
1 |
52 |
8 |
9 |
22 |
139 |
| Neural networks in day-ahead electricity price forecasting: Single vs. multiple outputs |
0 |
0 |
1 |
44 |
3 |
6 |
8 |
73 |
| On detecting and modeling periodic correlation in financial data |
0 |
0 |
0 |
280 |
0 |
1 |
2 |
635 |
| On the importance of the long-term seasonal component in day-ahead electricity price forecasting |
0 |
1 |
2 |
117 |
1 |
2 |
10 |
212 |
| On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Part II – Probabilistic forecasting |
0 |
1 |
2 |
133 |
2 |
4 |
7 |
285 |
| Origins of scaling in FX markets |
0 |
0 |
0 |
35 |
1 |
4 |
5 |
158 |
| Origins of the scaling behaviour in the dynamics of financial data |
0 |
0 |
0 |
18 |
0 |
1 |
4 |
128 |
| Outflow Dynamics in Modeling Oligopoly Markets: The Case of the Mobile Telecommunications Market in Poland |
0 |
0 |
1 |
56 |
0 |
0 |
6 |
204 |
| Outflow Dynamics in Modeling Oligopoly Markets: The Case of the Mobile Telecommunications Market in Poland |
0 |
0 |
1 |
25 |
0 |
0 |
2 |
107 |
| Outlier Treatment and Robust Approaches for Modeling Electricity Spot Prices |
0 |
0 |
1 |
242 |
1 |
1 |
6 |
688 |
| Performance of the estimators of stable law parameters |
0 |
0 |
0 |
31 |
0 |
2 |
4 |
137 |
| Point and interval forecasting of wholesale electricity prices: Evidence from the Nord Pool market |
0 |
0 |
0 |
199 |
0 |
3 |
3 |
651 |
| PostForecasts.jl: A Julia package for probabilistic forecasting by postprocessing point predictions |
1 |
4 |
37 |
37 |
4 |
10 |
87 |
87 |
| Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression |
0 |
2 |
4 |
14 |
1 |
3 |
13 |
29 |
| 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 |
1 |
2 |
136 |
| Principal Components Analysis in implied volatility modeling (Analiza skladowych glownych w modelowaniu implikowanej zmiennosci) |
0 |
0 |
0 |
49 |
2 |
2 |
3 |
249 |
| Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts? |
0 |
0 |
2 |
158 |
1 |
2 |
12 |
307 |
| Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging |
0 |
1 |
5 |
282 |
1 |
3 |
15 |
605 |
| Probabilistic intraday electricity price forecasting using generative machine learning |
0 |
18 |
18 |
18 |
6 |
11 |
11 |
11 |
| Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts |
1 |
2 |
2 |
185 |
2 |
5 |
9 |
365 |
| Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts |
0 |
3 |
6 |
515 |
3 |
11 |
21 |
1,090 |
| Property insurance loss distributions |
0 |
0 |
0 |
110 |
0 |
0 |
2 |
440 |
| Recent advances in electricity price forecasting: A review of probabilistic forecasting |
0 |
0 |
4 |
438 |
0 |
1 |
12 |
923 |
| Regime-switching models for electricity spot prices: Introducing heteroskedastic base regime dynamics and shifted spike distributions |
0 |
0 |
0 |
118 |
0 |
0 |
2 |
288 |
| Regularized Quantile Regression Averaging for probabilistic electricity price forecasting |
0 |
1 |
2 |
161 |
2 |
7 |
15 |
210 |
| Revisiting the relationship between spot and futures prices in the Nord Pool electricity market |
0 |
0 |
0 |
398 |
4 |
6 |
10 |
401 |
| Rewiring the network. What helps an innovation to diffuse? |
0 |
0 |
0 |
111 |
2 |
2 |
4 |
102 |
| Robust estimation and forecasting of the long-term seasonal component of electricity spot prices |
0 |
0 |
0 |
65 |
2 |
2 |
4 |
126 |
| Robust estimation and forecasting of the long-term seasonal component of electricity spot prices |
0 |
0 |
0 |
267 |
0 |
1 |
4 |
589 |
| Scaling in currency exchange: A Conditionally Exponential Decay approach |
0 |
0 |
0 |
6 |
1 |
1 |
5 |
115 |
| Selection of calibration windows for day-ahead electricity price forecasting |
0 |
1 |
2 |
73 |
3 |
4 |
12 |
114 |
| Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals |
0 |
1 |
3 |
160 |
2 |
6 |
10 |
321 |
| Short-term electricity price forecasting with time series models: A review and evaluation |
2 |
3 |
13 |
509 |
4 |
7 |
32 |
1,318 |
| Simulation modeling of epidemic risk in supermarkets: Investigating the impact of social distancing and checkout zone design |
0 |
0 |
1 |
60 |
1 |
7 |
13 |
156 |
| Simulation of Risk Processes |
0 |
1 |
1 |
98 |
1 |
3 |
4 |
302 |
| Simulation of risk processes |
0 |
0 |
0 |
27 |
1 |
3 |
4 |
144 |
| Stable distributions |
0 |
0 |
1 |
238 |
1 |
1 |
5 |
469 |
| Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF) |
0 |
4 |
5 |
5 |
2 |
3 |
4 |
4 |
| Stealing accuracy: Predicting day-ahead electricity prices with Temporal Hierarchy Forecasting (THieF) |
0 |
9 |
9 |
9 |
1 |
8 |
8 |
8 |
| Structure and stylized facts of a deregulated power market |
1 |
2 |
2 |
111 |
2 |
3 |
3 |
378 |
| The relationship between spot and futures CO2 emission allowance prices in the EU-ETS |
0 |
1 |
3 |
325 |
1 |
2 |
12 |
1,388 |
| The role of educational trainings in the diffusion of smart metering platforms: An agent-based modeling approach |
0 |
0 |
0 |
32 |
0 |
1 |
3 |
87 |
| To combine or not to combine? Recent trends in electricity price forecasting |
0 |
0 |
4 |
190 |
1 |
3 |
15 |
368 |
| Trading on short-term path forecasts of intraday electricity prices |
1 |
5 |
12 |
152 |
6 |
18 |
40 |
295 |
| Trading on short-term path forecasts of intraday electricity prices. Part II -- Distributional Deep Neural Networks |
1 |
9 |
25 |
96 |
4 |
13 |
56 |
189 |
| Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs |
0 |
0 |
0 |
97 |
1 |
1 |
2 |
216 |
| Two faces of word-of-mouth: Understanding the impact of social interactions on demand curves for innovative products |
1 |
2 |
2 |
48 |
2 |
3 |
5 |
181 |
| Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO |
0 |
1 |
4 |
196 |
1 |
2 |
9 |
346 |
| Variance stabilizing transformations for electricity spot price forecasting |
1 |
2 |
9 |
201 |
4 |
12 |
27 |
757 |
| Visualization tools for insurance risk processes |
0 |
0 |
0 |
30 |
0 |
0 |
3 |
172 |
| Total Working Papers |
31 |
146 |
459 |
24,416 |
272 |
602 |
1,648 |
60,157 |
| 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 |
3 |
4 |
18 |
961 |
8 |
12 |
35 |
2,108 |
| CHRISTOF: MATLAB function to perform Christoffersen's (1998) tests of coverage |
1 |
2 |
7 |
1,079 |
3 |
5 |
15 |
2,681 |
| CI_POWERTAIL: MATLAB function to test for 'dragon kings' vs. 'black swans' |
0 |
0 |
4 |
201 |
2 |
3 |
12 |
568 |
| CI_WEIBULLTAIL: MATLAB function to test for 'dragon kings' in Weibull-type tails |
0 |
0 |
1 |
164 |
1 |
3 |
11 |
615 |
| COR: MATLAB function to compute the correlation coefficients |
0 |
0 |
0 |
819 |
2 |
3 |
7 |
6,808 |
| DESEASONALIZE: MATLAB function to remove short and long term seasonal components |
0 |
1 |
2 |
1,669 |
0 |
2 |
6 |
4,576 |
| DESEASONALIZE: MATLAB function to remove short and long term seasonal components (new implementation) |
1 |
1 |
5 |
484 |
2 |
3 |
13 |
1,012 |
| DFA: MATLAB function to compute the Hurst exponent using Detrended Fluctuation Analysis (DFA) |
1 |
2 |
17 |
2,972 |
4 |
15 |
65 |
7,826 |
| 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 |
0 |
2 |
29 |
3 |
4 |
15 |
96 |
| 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 |
1 |
3 |
10 |
139 |
3 |
8 |
30 |
408 |
| ENERGIES_9_621_CODES: MATLAB codes for computing electricity spot price forecasts from "Automated variable selection and shrinkage for day-ahead electricity price forecasting" |
4 |
4 |
15 |
319 |
6 |
10 |
49 |
731 |
| ENERGIES_9_621_FIGS: MATLAB codes and data for plotting figures from "Automated variable selection and shrinkage for day-ahead electricity price forecasting" |
2 |
4 |
14 |
191 |
5 |
9 |
24 |
543 |
| EPFTOOLBOX: The first open-access PYTHON library for driving research in electricity price forecasting (EPF) |
0 |
3 |
18 |
147 |
6 |
17 |
89 |
699 |
| E_HMM: MATLAB function to calculate Electromagnetic Field (EMF) intensity using a Hidden Markov Model (HMM) filter |
0 |
0 |
1 |
178 |
1 |
5 |
15 |
738 |
| Financial Engineering Toolbox (FET) ver. 2.5 for MATLAB |
0 |
1 |
2 |
180 |
0 |
3 |
9 |
530 |
| GARMANKOHLHAGEN: MATLAB function to evaluate European FX option prices in the Garman and Kohlhagen (1983) model |
0 |
0 |
0 |
236 |
1 |
3 |
4 |
902 |
| GPH: MATLAB function to estimate the Hurst exponent using the Geweke-Porter-Hudak (1983) spectral estimator (periodogram regression method) |
0 |
1 |
9 |
856 |
5 |
16 |
34 |
2,113 |
| 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 |
3 |
5 |
700 |
| 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 |
1 |
1 |
185 |
0 |
3 |
6 |
556 |
| HESTONVANILLALIPTON: MATLAB function to evaluate European FX option prices in the Heston (1993) model using the approach of Lipton (2002) |
0 |
0 |
1 |
104 |
0 |
2 |
7 |
386 |
| HESTONVANILLASMILE: MATLAB function to compute the volatility smile implied by the Heston (1993) option pricing model |
0 |
0 |
0 |
380 |
0 |
0 |
1 |
1,123 |
| HOLTWINTERS: MATLAB function to compute forecasts of the Holt-Winters exponential smoothing model |
0 |
5 |
20 |
1,076 |
3 |
13 |
56 |
3,165 |
| HURST: MATLAB function to compute the Hurst exponent using R/S Analysis |
2 |
5 |
55 |
5,615 |
9 |
23 |
142 |
13,940 |
| 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 |
2 |
492 |
| LTSCSIN: MATLAB function to estimate and forecast the long-term seasonal component (LTSC) of an electricity spot price series using sine-based methods |
0 |
0 |
1 |
170 |
0 |
3 |
8 |
397 |
| 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 |
214 |
1 |
1 |
2 |
427 |
| LTSC_EXAMPLE: MATLAB example script and data for "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices" |
1 |
2 |
2 |
243 |
3 |
4 |
4 |
532 |
| MFE Toolbox ver. 1.0.1 for MATLAB |
0 |
2 |
5 |
1,273 |
2 |
6 |
15 |
3,115 |
| MRJD_MLE: MATLAB function to estimate parameters of a Mean-Reverting Jump-Diffusion (MRJD) process using maximum likelihood |
0 |
2 |
10 |
1,638 |
1 |
24 |
60 |
3,847 |
| MRJD_PRED: MATLAB function to make a one-step ahead prediction of a Mean-Reverting Jump-Diffusion (MRJD) process |
0 |
0 |
0 |
265 |
0 |
1 |
3 |
725 |
| MRJD_SIM: MATLAB function to simulate trajectories of a Mean-Reverting Jump-Diffusion (MRJD) process |
1 |
2 |
3 |
1,041 |
2 |
4 |
10 |
2,693 |
| MRS2IR_EST: MATLAB function to estimate parameters of a Markov regime-switching (MRS) model with 2 independent regimes |
0 |
0 |
1 |
642 |
4 |
5 |
7 |
1,584 |
| MRS2IR_SIM: MATLAB function to simulate trajectories of a Markov regime-switching (MRS) model with 2 independent regimes |
0 |
0 |
2 |
307 |
2 |
2 |
5 |
700 |
| MRS2_PLOT: MATLAB function to plot calibration results for a Markov regime-switching (MRS) model with 2 regimes |
0 |
0 |
1 |
235 |
0 |
1 |
3 |
587 |
| MRS3IR_EST: MATLAB function to estimate parameters of a Markov regime-switching (MRS) model with 3 independent regimes |
3 |
3 |
5 |
436 |
3 |
4 |
9 |
911 |
| MRS3IR_SIM: MATLAB function to simulate trajectories of a Markov regime-switching (MRS) model with 3 independent regimes |
1 |
4 |
6 |
368 |
5 |
9 |
25 |
755 |
| MRS3_PLOT: MATLAB function to plot calibration results for a Markov regime-switching (MRS) model with 3 regimes |
0 |
0 |
0 |
273 |
1 |
2 |
3 |
685 |
| 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 |
1 |
3 |
8 |
21 |
3 |
8 |
23 |
56 |
| PDFHESTON: MATLAB function to evaluate the probability density function in the Heston (1993) model |
0 |
0 |
0 |
213 |
1 |
1 |
2 |
546 |
| PERIODOG: MATLAB function to compute and plot the periodogram of a time series |
0 |
0 |
3 |
938 |
1 |
3 |
9 |
2,826 |
| PS2R_EST: MATLAB function to estimate parameters of a 2-regime parameter switching (PS) model |
0 |
0 |
1 |
257 |
2 |
4 |
7 |
546 |
| PS2R_SIM: MATLAB function to simulate trajectories of a 2-regime parameter switching (PS) model |
0 |
0 |
1 |
181 |
0 |
1 |
2 |
462 |
| REMST: MATLAB function to remove trend and seasonal component using the moving average method |
0 |
0 |
1 |
1,290 |
2 |
4 |
11 |
3,624 |
| RUNNINGMEDIAN: MATLAB function to compute a running median of a time series |
0 |
0 |
1 |
272 |
0 |
3 |
4 |
1,044 |
| 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 |
1 |
2 |
379 |
| SCAR_EXAMPLE: MATLAB codes and data for "On the importance of the long-term seasonal component in day-ahead electricity price forecasting" |
0 |
1 |
5 |
263 |
2 |
5 |
10 |
481 |
| SIMGBM: MATLAB function to simulate trajectories of Geometric Brownian Motion (GBM) |
0 |
0 |
1 |
777 |
1 |
5 |
10 |
2,833 |
| SIMGBM: MATLAB function to simulate trajectories of Geometric Brownian Motion (GBM) |
0 |
0 |
1 |
402 |
1 |
6 |
10 |
1,384 |
| SIMHESTON: MATLAB function to simulate trajectories of the spot price and volatility processes in the Heston (1993) model |
0 |
0 |
0 |
510 |
0 |
2 |
5 |
1,204 |
| 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 |
1 |
4 |
285 |
| STABLECULL: MATLAB function to estimate stable distribution parameters using the quantile method of McCulloch |
0 |
1 |
2 |
366 |
1 |
2 |
5 |
763 |
| STABLEPDF_FFT: MATLAB function to compute the stable distribution probability density function (pdf) via FFT |
0 |
0 |
1 |
625 |
2 |
4 |
8 |
1,938 |
| STABLEREG: MATLAB function to estimate stable distribution parameters using the regression method of Koutrouvelis |
0 |
0 |
1 |
359 |
0 |
0 |
6 |
922 |
| STABLEREGKW: MATLAB function to estimate stable distribution parameters using the regression method of Kogon and Williams |
3 |
4 |
4 |
424 |
4 |
7 |
9 |
1,021 |
| STABLERND: MATLAB function to generate random numbers from the stable distribution |
0 |
1 |
4 |
566 |
2 |
11 |
19 |
1,564 |
| STF2HES: MATLAB functions for "FX smile in the Heston model" |
0 |
0 |
0 |
239 |
0 |
0 |
1 |
663 |
| STF2HES_EX: MATLAB example scripts for "FX smile in the Heston model" |
0 |
0 |
0 |
132 |
1 |
3 |
4 |
485 |
| The World According to Spinson (WAS): Standalone application for simulating agent-based models |
0 |
0 |
2 |
135 |
1 |
1 |
8 |
472 |
| Total Software Items |
25 |
62 |
275 |
34,062 |
113 |
303 |
966 |
94,179 |