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Autoregressive conditional dynamic semivariance models with value-at-risk estimates
A variant of the autoregressive conditional heteroscedastic (ARCH) process called as autoregressive conditional dynamic semivariance process (ARCDS)...
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Bayesian Estimation of Multiple Covariate of Autoregressive (MC-AR) Model
In present scenario, handling covariate/explanatory variable with the model is one of most important factor to study with the models. The main...
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Robustness and spurious long memory: evidence from the generalized autoregressive score models
This paper employs the generalized autoregressive score (GAS) models to study the long memory and regime switching in the second comment. Via...
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Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)
This study provides a holistic and quantitative overview of over 800 mathematical methods (e.g., financial and risk models, statistical tests,...
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Modelling and Forecasting of Covid-19 Using Periodical ARIMA Models
Corona virus (Covid-19) is a great danger for whole world. World health organization (WHO) considered it an epidemic. Data collection was based on...
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Hierarchical spatial network models for road accident risk assessment
This paper addresses the critical issue of road safety and accident prevention by integrating road features, network theory, and advanced statistical...
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Minimum capital requirement and portfolio allocation for non-life insurance: a semiparametric model with Conditional Value-at-Risk (CVaR) constraint
We present an optimization problem to determine the minimum capital requirement for a non-life insurance company. The optimization problem imposes a...
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The two-component Beta-t-QVAR-M-lev: a new forecasting model
We introduce a new joint model of expected return and volatility forecasting, namely the two-component Beta- t -QVAR-M-lev (quasi-vector autoregression...
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Is Bitcoin ready to be a widespread payment method? Using price volatility and setting strategies for merchants
Bitcoin has gradually gained acceptance as a payment method that, unlike electronic payments in dollars or euros, passes through the international...
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Predicting the volatility of Bitcoin returns based on kernel regression
Nonparametric regression has become a popular method because it offers great flexibility in data modeling without requiring a precise description of...
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Modelling systemic risk of energy and non-energy commodity markets during the COVID-19 pandemic
COVID-19 led restrictions make it imperative to study how pandemic affects the systemic risk profile of global commodities network. Therefore, we...
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Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales
Existing sales forecasting models are not comprehensive and flexible enough to consider dynamic changes and nonlinearities in sales time-series at...
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Modelling extreme risk spillovers in the commodity markets around crisis periods including COVID19
In this paper, we examine extreme spillovers among the realized volatility of various energy, metals, and agricultural commodities over the period...
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Volatility forecasting: a new GARCH-type model for fuzzy sets-valued time series
In recent years, academia’s attention has gradually shifted toward non-point-valued time series volatility forecasting models in the finance big data...
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Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models
The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility...
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Cleaning the carbon market! Market transparency and market efficiency in the EU ETS
This paper revisits the informational efficiency of the EU ETS at a micro level, by introducing a novel time variant structural decomposition of...
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Analysing exchange rate volatility in India using GARCH family models
Volatility in foreign exchange market is an important issue of concern for market participants and policy makers as higher the volatility the more...
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Modeling and forecasting traffic flows with mobile phone big data in flooding risk areas to support a data-driven decision making
Floods are one of the natural disasters which cause the worst human, social and economic impacts to the detriment of both public and private sectors....
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Multi-period power utility optimization under stock return predictability
In this paper, we derive an analytical solution to the dynamic optimal portfolio choice problem in the case of an investor equipped with a power...
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Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms
The study aims at forecasting the return volatility of the cryptocurrencies using several machine learning algorithms, like neural network...
