scholarly journals Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Qu ◽  
Ping Ji
Keyword(s):  
Genetic Algorithm ◽  
Stock Markets ◽  
High Frequency ◽  
Time Horizon ◽  
Realized Volatility ◽  
Heterogeneous Structure ◽  
Moving Window ◽  
Heterogeneous Structures ◽  
Chinese Stock Markets ◽  
New Framework

The heterogeneous autoregressive (HAR) models of high-frequency realized volatility are inspired by the Heterogeneous Market Hypothesis and incorporate daily, weekly and monthly realized volatilities in the volatility dynamics with a (1,5,22) time horizon structure. We build on the HAR models and propose a new framework, adaptive heterogeneous autoregressive (AHAR) models, whose time horizon structures are optimized by a genetic algorithm. Our models can be applied to markets with different heterogeneous structures, and their time horizon structures can be adjusted adaptively as the market's heterogeneous structure varies. Moving window tests with five-minute returns of the CSI 300 index indicate that the (1,5,22) structure originally proposed for American stock markets is not the best choice for Chinese stock markets, and Chinese stock markets’ heterogeneous structure does vary over time. Using four common loss functions, we find that the AHAR models outperform the corresponding HAR models in most of the forecast windows and thus are reasonable choices for volatility forecasting practices.

scholarly journals Regime-Dependent Good and Bad Volatility of Bitcoin

2020 ◽  
Vol 13 (12) ◽  
pp. 312
Author(s):  
Kislay Kumar Jha ◽  
Dirk G. Baur
Keyword(s):  
Stock Markets ◽  
High Frequency ◽  
Realized Volatility ◽  
Forecast Horizon ◽  
Volatility Asymmetry ◽  
Volatility Regimes ◽  
The Asymmetry

This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility.

Probabilistic multiscale modeling of fracture in heterogeneous materials and structures

2020 ◽  
Vol 86 (7) ◽  
pp. 45-54
Author(s):  
A. M. Lepikhin ◽  
N. A. Makhutov ◽  
Yu. I. Shokin
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Fracture Mechanics ◽  
Multiscale Modeling ◽  
Virtual Work ◽  
Numerical Models ◽  
Heterogeneous Materials ◽  
Heterogeneous Structure ◽  
Generalized Coordinates ◽  
Heterogeneous Structures

The probabilistic aspects of multiscale modeling of the fracture of heterogeneous structures are considered. An approach combining homogenization methods with phenomenological and numerical models of fracture mechanics is proposed to solve the problems of assessing the probabilities of destruction of structurally heterogeneous materials. A model of a generalized heterogeneous structure consisting of heterogeneous materials and regions of different scales containing cracks and crack-like defects is formulated. Linking of scales is carried out using kinematic conditions and multiscale principle of virtual forces. The probability of destruction is formulated as the conditional probability of successive nested fracture events of different scales. Cracks and crack-like defects are considered the main sources of fracture. The distribution of defects is represented in the form of Poisson ensembles. Critical stresses at the tops of cracks are described by the Weibull model. Analytical expressions for the fracture probabilities of multiscale heterogeneous structures with multilevel limit states are obtained. An approach based on a modified Monte Carlo method of statistical modeling is proposed to assess the fracture probabilities taking into account the real morphology of heterogeneous structures. A feature of the proposed method is the use of a three-level fracture scheme with numerical solution of the problems at the micro, meso and macro scales. The main variables are generalized forces of the crack propagation and crack growth resistance. Crack sizes are considered generalized coordinates. To reduce the dimensionality, the problem of fracture mechanics is reformulated into the problem of stability of a heterogeneous structure under load with variations of generalized coordinates and analysis of the virtual work of generalized forces. Expressions for estimating the fracture probabilities using a modified Monte Carlo method for multiscale heterogeneous structures are obtained. The prospects of using the developed approaches to assess the fracture probabilities and address the problems of risk analysis of heterogeneous structures are shown.

Forecasting Exchange Rate Realized Volatility Through Decomposition Using High-Frequency Data

2017 ◽  
Author(s):  
Rim mname Lamouchi ◽  
Russell mname Davidson ◽  
Ibrahim mname Fatnassi ◽  
Abderazak Ben mname Maatoug
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Frequency Data

Herd Behaviour: How Decisive is the Noise in the NSE and BSE Stock Markets?

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Paritosh Chandra Sinha
Keyword(s):  
Behavioral Finance ◽  
Stock Markets ◽  
High Frequency ◽  
Stock Exchange ◽  
Time Stamp ◽  
Trade Volume ◽  
Herding Behavior ◽  
Equilibrium Pricing ◽  
Indian Context ◽  
Herd Behaviour

Do investors in the stock markets act/react on true information or noise? Do they believe on their own information or simply herd? The study seeks to explore these typical research queries from the behavioral finance perspectives. In particular, it develops a new theory of herding behavior and extends the models of Banerjee (1992) and Bikhchandani, Hirshleifer, and Welch (1992). The study also empirically tests the same on the Indian context with the high frequency intraday trading data for the real trade-time or time-stamp, trade-volume, and trade-price of ten sample scripts listed for their trading in both markets - the Bombay Stock Exchange (BSE) and the National stock Exchange (NSE). The study contributes to the literature with original findings. It shows that investors in the two Indian stock markets show crowd of positive and negative herding as well significantly and there is huge noise along with information in the markets equilibrium pricing mechanism.

POWER LAW AND STRETCHED EXPONENTIAL EFFECTS OF EXTREME EVENTS IN CHINESE STOCK MARKETS

2010 ◽  
Vol 09 (02) ◽  
pp. 203-217 ◽  
Author(s):  
XIAOJUN ZHAO ◽  
PENGJIAN SHANG ◽  
YULEI PANG
Keyword(s):  
Correlation Analysis ◽  
Power Law ◽  
Extreme Events ◽  
Stock Markets ◽  
Cross Correlation ◽  
Extreme Values ◽  
Cumulative Distribution ◽  
Stretched Exponential ◽  
Cross Correlation Analysis ◽  
Chinese Stock Markets

This paper reports the statistics of extreme values and positions of extreme events in Chinese stock markets. An extreme event is defined as the event exceeding a certain threshold of normalized logarithmic return. Extreme values follow a piecewise function or a power law distribution determined by the threshold due to a crossover. Extreme positions are studied by return intervals of extreme events, and it is found that return intervals yield a stretched exponential function. According to correlation analysis, extreme values and return intervals are weakly correlated and the correlation decreases with increasing threshold. No long-term cross-correlation exists by using the detrended cross-correlation analysis (DCCA) method. We successfully introduce a modification specific to the correlation and derive the joint cumulative distribution of extreme values and return intervals at 95% confidence level.

scholarly journals Rapid observations of ocean dynamics and stratification along a steep island coast during Hurricane María

2021 ◽  
Vol 7 (20) ◽  
pp. eabf1552
Author(s):  
Olivia M. Cheriton ◽  
Curt D. Storlazzi ◽  
Kurt J. Rosenberger ◽  
Clark E. Sherman ◽  
Wilford E. Schmidt
Keyword(s):  
Puerto Rico ◽  
Water Column ◽  
High Frequency ◽  
Coastal Communities ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
Hurricane Intensity ◽  
Island Coast ◽  
New Framework ◽  
Insular Shelf

Hurricanes are extreme storms that affect coastal communities, but the linkages between hurricane forcing and ocean dynamics remain poorly understood. Here, we present full water column observations at unprecedented resolution from the southwest Puerto Rico insular shelf and slope during Hurricane María, representing a rare set of high-frequency, subsurface, oceanographic observations collected along an island margin during a hurricane. The shelf geometry and orientation relative to the storm acted to stabilize and strengthen stratification. This maintained elevated sea-surface temperatures (SSTs) throughout the storm and led to an estimated 65% greater potential hurricane intensity contribution at this site before eye passage. Coastal cooling did not occur until 11 hours after the eye passage. Our findings present a new framework for how hurricane interaction with insular island margins may generate baroclinic processes that maintain elevated SSTs, thus potentially providing increased energy for the storm.

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