Compound distributions for financial returns

Afuecheta, Emmanuel; Semeyutin, Artur; Chan, Stephen; Nadarajah, Saralees; Ruiz, Diego Andrés Pérez

doi:10.1371/journal.pone.0239652

Cited by 7 publications

(3 citation statements)

References 88 publications

(104 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data set for this example is taken from the recent work of Afuecheta et al [1] in which authors proposed several candidate innovations for the GARCH model to predict the volatility of financial series. The returns data of Litecoin (LTC) for the period starting from the 24th of October 2013 to the 16th of June 2018 will be used in this example.…”

Section: Modeling Financial Volatilitymentioning

confidence: 99%

A New Formulation of Generalized Gamma: Some Results and Applications

Peng

2023

ESAIM: PS

View full text Add to dashboard Cite

We extend the 2-parameter Weibull to the generalized gamma distribution by adding a new partial parameter. The new shape parameter can be used to easily generate generalized gamma distributions with different shapes of the density function, hazard rate, and mean residual lifetimes that are useful in simulating various business processes such as manufacturing processes, and reliability systems. We derived some theoretical results and created visual presentations to demonstrate the behaviors of this new shape parameter as well. A new Monte Carlo simulation based on the new parameter was proposed to assess the discrepancy between the generalized gamma and its subfamilies. The power analysis of the proposed test was evaluated via simulation studies. We also present some numerical examples.

show abstract

Section: Modeling Financial Volatilitymentioning

confidence: 99%

A New Formulation of Generalized Gamma: Some Results and Applications

Peng

2023

ESAIM: PS

View full text Add to dashboard Cite

show abstract

“…In what follows, we find that if one is able to make a reasonable approximation of the historical asset price return variance σ 2 as an inverse gamma distribution, then the parameters of this approximate distribution will imply the parameters of a locationscale Student's t-distribution which should be representative of the distribution of ν at a particular moment in time. Note that other distributions for σ 2 are possible; see Afuecheta et al (2020) for alternative possible models of the variance σ 2 used in the financial modelling literature. However, fitting an inverse gamma distributions has proved a satisfactory approximation for our purposes.…”

Section: Compound Distributions and The Distribution Of ν In Normal G...mentioning

confidence: 99%

A Practical Algorithm to Detect Superexponential Behavior in Financial Asset Price Returns

Lynch

Mestel

2022

Int. J. Theor. Appl. Finan.

View full text Add to dashboard Cite

To assist with the detection of bubbles and negative bubbles in financial markets, a criterion is introduced to indicate whether a market is likely to be in a superexponential regime (where growth in such a regime would correspond to an asset price bubble and decline to an negative bubble) as opposed to “normal” exponential behavior typified by a constant rate of growth or decline. The criterion is founded on the Johansen–Ledoit–Sornette model of asset dynamics in a bubble and is derived from a linear fit to observed data with a nonlinear time transformation with parameters distributed uniformly in their permitted ranges. Making use of expected values rather than the underlying distribution, the criterion is straightforward and efficient to compute and can in principle be applied in real time to intra-day markets as well as longer timescales. In some circumstances, the criterion is shown to have certain predictive qualities when applied to a portfolio of stocks, and could be used as input into algorithmic trading strategies. A simple strategy is described which is based on market reversion predictions of a portfolio of stocks and which in back-testing generates notable returns.

show abstract

“…Lee et al [51] provided a review of the recent developments in the field of scale mixture distributions and an overview of certain examples. Distributions of this type are also used in the real-world data analysis [52][53][54][55][56][57].…”

Section: Introductionmentioning

confidence: 99%

Divergence-Based Segmentation Algorithm for Heavy-Tailed Acoustic Signals with Time-Varying Characteristics

Grzesiek

Gąsior

Wyłomańska

et al. 2021

Sensors

View full text Add to dashboard Cite

Many real-world systems change their parameters during the operation. Thus, before the analysis of the data, there is a need to divide the raw signal into parts that can be considered as homogeneous segments. In this paper, we propose a segmentation procedure that can be applied for the signal with time-varying characteristics. Moreover, we assume that the examined signal exhibits impulsive behavior, thus it corresponds to the so-called heavy-tailed class of distributions. Due to the specific behavior of the data, classical algorithms known from the literature cannot be used directly in the segmentation procedure. In the considered case, the transition between parts corresponding to homogeneous segments is smooth and non-linear. This causes that the segmentation algorithm is more complex than in the classical case. We propose to apply the divergence measures that are based on the distance between the probability density functions for the two examined distributions. The novel segmentation algorithm is applied to real acoustic signals acquired during coffee grinding. Justification of the methodology has been performed experimentally and using Monte-Carlo simulations for data from the model with heavy-tailed distribution (here the stable distribution) with time-varying parameters. Although the methodology is demonstrated for a specific case, it can be extended to any process with time-changing characteristics.

show abstract

Compound distributions for financial returns

Cited by 7 publications

References 88 publications

A New Formulation of Generalized Gamma: Some Results and Applications

A New Formulation of Generalized Gamma: Some Results and Applications

A Practical Algorithm to Detect Superexponential Behavior in Financial Asset Price Returns

Divergence-Based Segmentation Algorithm for Heavy-Tailed Acoustic Signals with Time-Varying Characteristics

Contact Info

Product

Resources

About