DeepVol:基于高频数据的波动性预测,具有扩展的因果卷曲
Fernando Moreno-Pino1,2, Stefan Zohren1,3
1Oxford-Man Institute of Quantitative Finance, University of Oxford, Oxford, UK.
Quantitative finance
|October 16, 2024
概括
DeepVol是一种新的深度学习模型,使用高频金融数据预测股票波动. 这种方法通过有效利用一天内信息来提高预测准确性,优于传统方法以更好地管理风险.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 金融计量经济学 金融计量经济学
背景情况:
- 波动性预测对于股票风险评估至关重要.
- 传统的统计模型和机器学习技术用于每日时间序列波动性预测.
- 高频的日内数据可以改善波动性预测.
研究的目的:
- 提出DeepVol,一种使用扩展因果卷积进行前一天波动性预测的新型模型.
- 利用高频率的日内数据来提高波动性预测的准确性.
- 证明深度学习在从财务时间序列中获取预测信息的有效性.
主要方法:
- 用于时间序列分析的扩展因果卷积.
- 采用了两年来NASDAQ-100的高频内日金融数据.
- 评估了DeepVol的性能与传统方法相比.
主要成果:
- 扩展卷积过器有效地从日内金融时间序列中提取相关信息.
- DeepVol成功地利用了高频数据中存在的预测信息.
- 该模型避免了日常数据模型的局限性,例如模型错误规范和手工制作的功能.
结论:
- 基于深度学习的方法DeepVol准确地从高频数据中学习全球特征.
- 与传统方法相比,拟议的模型产生了更准确的波动性预测.
- 迪普沃尔有助于产生更可靠的股票风险指标.
相关概念视频
01:24Convolution: Math, Graphics, and Discrete Signals
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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
234
01:03Prediction Intervals
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
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01:17Convolution Properties II
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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
174
01:20Convolution Properties I
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Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
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01:20Deconvolution
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
137
01:26Linear Approximation in Frequency Domain
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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
87