Results 31 to 40 of about 113,493 (310)
Adding dummy variables: A simple approach for improved volatility forecasting in electricity market
Journal of Management Science and Engineering, 2023 This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity ...
Xu Gong, Boqiang Lin
doaj +1 more source
Volatility Forecast Comparison Using Imperfect Volatility Proxies [PDF]
SSRN Electronic Journal, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +3 more sources
Mathematics, 2021 The existing index system for volatility forecasting only focuses on asset return series or historical volatility, and the prediction model cannot effectively describe the highly complex and nonlinear characteristics of the stock market.
Bolin Lei, Boyu Zhang, Yuping Song
doaj +1 more source
Volatility Forecasting: Downside Risk, Jumps and Leverage Effect
Econometrics, 2016 We provide empirical evidence of volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Using recently-developed methodologies to detect jumps from high frequency price data, we estimate the ...
Francesco Audrino, Yujia Hu
doaj +1 more source
Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms
Future Business Journal, 2023 The study aims at forecasting the return volatility of the cryptocurrencies using several machine learning algorithms, like neural network autoregressive (NNETAR), cubic smoothing spline (CSS), and group method of data handling neural network (GMDH-NN ...
Farman Ullah Khan +2 more
doaj +1 more source
A general equilibrium approach to pricing volatility risk.
PLoS ONE, 2019 This paper provides a general equilibrium approach to pricing volatility. Existing models (e.g., ARCH/GARCH, stochastic volatility) take a statistical approach to estimating volatility, volatility indices (e.g., CBOE VIX) use a weighted combination of ...
Jianlei Han +4 more
doaj +1 more source
, 2017 In Sects. 2.3 and 4.2, the common volatility modelling oversights that exist in literature were highlighted. In this Chapter, we discuss the potential impact of these oversights on volatility forecasting and provide a methodology for testing the impact of these oversights on the forecasting accuracy of volatility models.
Mostafa, F, Dillon, T, Chang, E
openaire +2 more sources
Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets
Indonesian Capital Market Review, 2014 Volatility forecasting is an imperative research field in financial markets and crucial component in most financial decisions. Nevertheless, which model should be used to assess volatility remains a complex issue as different volatility models result in ...
Erie Febrian, Aldrin Herwany
doaj +1 more source
Time after time – circadian clocks through the lens of oscillator theory
FEBS Letters, EarlyView.Oscillator theory bridges physics and circadian biology. Damped oscillators require external drivers, while limit cycles emerge from delayed feedback and nonlinearities. Coupling enables tissue‐level coherence, and entrainment aligns internal clocks with environmental cues.
Marta del Olmo +2 more
wiley +1 more source
PERAMALAN VOLATILITAS SAHAM MENGGUNAKAN MODEL EXPONENTIAL GARCH DAN THRESHOLD GARCH
E-Jurnal Matematika, 2019 In financial data there is asymmetric volatility, which denotes the different movements on conditional volatility of increase and decrease financial asset returns.
SITI RAHAYU NINGSIH +2 more
doaj +1 more source