| 1. |
- Bergsteinsson, Hjörleifur G., et al.
(författare)
-
Heat load forecasting using adaptive temporal hierarchies
- 2021
-
Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619. ; 292
-
Tidskriftsartikel (refereegranskat)abstract
- Heat load forecasts are crucial for energy operators in order to optimize the energy production at district heating plants for the coming hours. Furthermore, forecasts of heat load are needed for optimized control of the district heating network since a lower temperature reduces the heat loss, but the required heat supply at the end-users puts a lower limit on the temperature level. Consequently, improving the accuracy of heat load forecasts leads to savings and reduced heat loss by enabling improved control of the network and an optimized production schedule at the plant. This paper proposes the use of temporal hierarchies to enhance the accuracy of heat load forecasts in district heating. Usually, forecasts are only made at the temporal aggregation level that is the most important for the system. However, forecasts for multiple aggregation levels can be reconciled and lead to more accurate forecasts at essentially all aggregation levels. Here it is important that the auto- and cross-covariance between forecast errors at the different aggregation levels are taken into account. This paper suggests a novel framework using temporal hierarchies and adaptive estimation to improve heat load forecast accuracy by optimally combining forecasts from multiple aggregation levels using a reconciliation process. The weights for the reconciliation are computed using an adaptively estimated covariance matrix with a full structure, enabling the process to share time-varying information both within and between aggregation levels. The case study shows that the proposed framework improves the heat load forecast accuracy by 15% compared to commercial state-of-the-art operational forecasts.
|
|
| 2. |
- Nystrup, Peter, et al.
(författare)
-
Detecting change points in VIX and S&P 500: A new approach to dynamic asset allocation
- 2016
-
Ingår i: Journal of Asset Management. - : Springer Science and Business Media LLC. - 1470-8272 .- 1479-179X. ; 17:5, s. 361-374
-
Tidskriftsartikel (refereegranskat)abstract
- The purpose of dynamic asset allocation (DAA) is to overcome the challenge that changing market conditions present to traditional strategic asset allocation by adjusting portfolio weights to take advantage of favorable conditions and reduce potential drawdowns. This article proposes a new approach to DAA that is based on detection of change points without fitting a model with a fixed number of regimes to the data, without estimating any parameters and without assuming a specific distribution of the data. It is examined whether DAA is most profitable when based on changes in the Chicago Board Options Exchange Volatility Index or change points detected in daily returns of the S&P 500 index. In an asset universe consisting of the S&P 500 index and cash, it is shown that a dynamic strategy based on detected change points significantly improves the Sharpe ratio and reduces the drawdown risk when compared with a static, fixed-weight benchmark.
|
|
| 3. |
- Nystrup, Peter, et al.
(författare)
-
Dimensionality reduction in forecasting with temporal hierarchies
- 2021
-
Ingår i: International Journal of Forecasting. - : Elsevier BV. - 0169-2070. ; 37:3, s. 1127-1146
-
Tidskriftsartikel (refereegranskat)abstract
- Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. It can be challenging to estimate the full cross-covariance matrix for a temporal hierarchy, which can easily be of very large dimension, yet it is difficult to know a priori which part of the error structure is most important. To address these issues, we propose to use eigendecomposition for dimensionality reduction when reconciling forecasts to extract as much information as possible from the error structure given the data available. We evaluate the proposed estimator in a simulation study and demonstrate its usefulness through applications to short-term electricity load and financial volatility forecasting. We find that accuracy can be improved uniformly across all aggregation levels, as the estimator achieves state-of-the-art accuracy while being applicable to hierarchies of all sizes.
|
|
| 4. |
- Nystrup, Peter, et al.
(författare)
-
Dynamic Allocation or Diversification : A Regime-Based Approach to Multiple Assets
- 2017
-
Ingår i: Journal of Portfolio Management. - : Pageant Media US. - 0095-4918 .- 2168-8656. ; 44:2, s. 62-73
-
Tidskriftsartikel (refereegranskat)abstract
- This article investigates whether regime-based asset allocation can effectively respond to changes in financial regimes at the portfolio level in an effort to provide better long-term results when compared to a static 60/40 benchmark. The potential benefit from taking large positions in a few assets at a time comes at the cost of reduced diversification. The authors analyze this trade-off in a multi-asset universe with great potential for static diversification. The regime-based approach is centered around a regime-switching model with time-varying parameters that can match financial markets’ behavior and a new, more intuitive way of inferring the hidden market regimes. The empirical results show that regime-based asset allocation is profitable, even when compared to a diversified benchmark portfolio. The results are robust because they are based on available market data with no assumptions about forecasting skills.
|
|
| 5. |
- Nystrup, Peter, et al.
(författare)
-
Dynamic Portfolio Optimization Across Hidden Market Regimes
- 2016
-
Konferensbidrag (refereegranskat)abstract
- Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The predominant approach in previous studies has been to specify in advance a static decision rule for changing the allocation based on the state of financial markets or the economy. This talk proposes the use of model predictive control to dynamically optimize a portfolio based on forecasts of the mean and variance of financial returns from a hidden Markov model with time-varying parameters. There are computational advantages to using model predictive control when estimates of future returns are updated repeatedly, since the optimal control actions are reconsidered anyway every time a new observation becomes available. Results from testing the approach on market data are presented and compared with previous, rule-based approaches. Further, imposing a trading penalty that reduces the number of trades is discussed as a way to increase the robustness of the approach.
|
|
| 6. |
- Nystrup, Peter, et al.
(författare)
-
Dynamic portfolio optimization across hidden market regimes
- 2018
-
Ingår i: Quantitative Finance. - : Informa UK Limited. - 1469-7688 .- 1469-7696. ; 18:1, s. 83-95
-
Tidskriftsartikel (refereegranskat)abstract
- Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The predominant approach in previous studies has been to specify in advance a static decision rule for changing the allocation based on the state of financial markets or the economy. In this article, model predictive control (MPC) is used to dynamically optimize a portfolio based on forecasts of the mean and variance of financial returns from a hidden Markov model with time-varying parameters. There are computational advantages to using MPC when estimates of future returns are updated every time a new observation becomes available, since the optimal control actions are reconsidered anyway. MPC outperforms a static decision rule for changing the allocation and realizes both a higher return and a significantly lower risk than a buy-and-hold investment in various major stock market indices. This is after accounting for transaction costs, with a one-day delay in the implementation of allocation changes, and with zero-interest cash as the only alternative to the stock indices. Imposing a trading penalty that reduces the number of trades is found to increase the robustness of the approach.
|
|
| 7. |
- Nystrup, Peter, et al.
(författare)
-
Feature selection in jump models
- 2021
-
Ingår i: Expert Systems with Applications. - : Elsevier BV. - 0957-4174. ; 184
-
Tidskriftsartikel (refereegranskat)abstract
- Jump models switch infrequently between states to fit a sequence of data while taking the ordering of the data into account We propose a new framework for joint feature selection, parameter and state-sequence estimation in jump models. Feature selection is necessary in high-dimensional settings where the number of features is large compared to the number of observations and the underlying states differ only with respect to a subset of the features. We develop and implement a coordinate descent algorithm that alternates between selecting the features and estimating the model parameters and state sequence, which scales to large data sets with large numbers of (noisy) features. We demonstrate the usefulness of the proposed framework by comparing it with a number of other methods on both simulated and real data in the form of financial returns, protein sequences, and text. By leveraging information embedded in the ordering of the data, the resulting sparse jump model outperforms all other methods considered and is remarkably robust to noise.
|
|
| 8. |
- Nystrup, Peter, et al.
(författare)
-
Greedy Online Classification of Persistent Market States Using Realized Intraday Volatility Features
- 2020
-
Ingår i: The Journal of Financial Data Science. - : Pageant Media US. - 2640-3943. ; 2:3, s. 25-39
-
Tidskriftsartikel (refereegranskat)abstract
- In many financial applications, it is important to classify time-series data without any latency while maintaining persistence in the identified states. The authors propose a greedy online classifier that contemporaneously determines which hidden state a new observation belongs to without the need to parse historical observations and without compromising persistence. Their classifier is based on the idea of clustering temporal features while explicitly penalizing jumps between states by a fixed-cost regularization term that can be calibrated to achieve a desired level of persistence. Through a series of return simulations, the authors show that in most settings their new classifier remarkably obtains a higher accuracy than the correctly specified maximum likelihood estimator. They illustrate that the new classifier is more robust to misspecification and yields state sequences that are significantly more persistent both in and out of sample. They demonstrate how classification accuracy can be further improved by including features that are based on intraday data. Finally, the authors apply the new classifier to estimate persistent states of the S&P 500 Index.
|
|
| 9. |
- Nystrup, Peter, et al.
(författare)
-
Hyperparameter Optimization for Portfolio Selection
- 2020
-
Ingår i: The Journal of Financial Data Science. - : Pageant Media US. - 2640-3943. ; 2:2
-
Tidskriftsartikel (refereegranskat)abstract
- Portfolio selection involves a trade-off between maximizing expected return and minimizing risk. In practice, useful formulations also include various costs and constraints that regularize the problem and reduce the risk due to estimation errors, resulting in solutions that depend on a number of hyperparameters. As the number of hyperparameters grows, selecting their value becomes increasingly important and difficult. In this article, the authors propose a systematic approach to hyperparameter optimization by leveraging recent advances in automated machine learning and multiobjective optimization. They optimize hyperparameters on a train set to yield the best result subject to market-determined realized costs. In applications to single- and multiperiod portfolio selection, they show that sequential hyperparameter optimization finds solutions with better risk–return trade-offs than manual, grid, and random search over hyperparameters using fewer function evaluations. At the same time, the solutions found are more stable from in-sample training to out-of-sample testing, suggesting they are less likely to be extremities that randomly happened to yield good performance in training.
|
|
| 10. |
- Nystrup, Peter, et al.
(författare)
-
Learning hidden Markov models with persistent states by penalizing jumps
- 2020
-
Ingår i: Expert Systems with Applications. - : Elsevier BV. - 0957-4174. ; 150
-
Tidskriftsartikel (refereegranskat)abstract
- Hidden Markov models are applied in many expert and intelligent systems to detect an underlying sequence of persistent states. When the model is misspecified or misestimated, however, it often leads to unrealistically rapid switching dynamics. To address this issue, we propose a novel estimation approach based on clustering temporal features while penalizing jumps. We compare the approach to spectral clustering and the standard approach of maximizing the likelihood function in an extensive simulation study and an application to financial data. The advantages of the proposed jump estimator include that it learns the hidden state sequence and model parameters simultaneously and faster while providing control over the transition rate, it is less sensitive to initialization, it performs better when the number of states increases, and it is robust to misspecified conditional distributions. The value of estimating the true persistence of the state process is illustrated through a simple trading strategy where improved estimates result in much lower transaction costs. Robustness is particularly critical when the model is part of a system used in production. Therefore, our proposed estimator significantly improves the potential for using hidden Markov models in practical applications.
|
|
