scholarly journals A Multivariate Asymmetric Long Memory Conditional Volatility Model with X, Regularity and Asymptotics

2017 ◽  
Author(s):  
Manabu Asai ◽  
Michael McAleer
Keyword(s):  
Long Memory ◽  
Conditional Volatility ◽  
Volatility Model

Forecasting Volatility Using Long Memory and Comovements: An Application to Option Valuation under SFAS 123R

2010 ◽  
Vol 45 (2) ◽  
pp. 503-533 ◽  
Author(s):  
George J. Jiang ◽  
Yisong S. Tian
Keyword(s):  
Long Memory ◽  
Financial Accounting ◽  
Option Valuation ◽  
Volatility Model ◽  
Forecasting Performance ◽  
Market Factors ◽  
Performance Forecasting ◽  
Long Term Forecasting ◽  
Forecasting Volatility

AbstractHorizon-matched historical volatility is commonly used to forecast future volatility for option valuation under the Statement of Financial Accounting Standards (SFAS) 123R. In this paper, we empirically investigate the performance of using historical volatility to forecast long-term stock return volatility in comparison with a number of alternative forecasting methods. In analyzing forecasting errors and their impact on reported income due to option expensing, we find that historical volatility is a poor forecast for long-term volatility and that shrinkage adjustment toward comparable-firm volatility only slightly improves its performance. Forecasting performance can be improved substantially by incorporating both long memory and comovements with common market factors. We also experiment with a simple mixed-horizon realized volatility model and find its long-term forecasting performance to be more accurate than historical forecasts but less accurate than long-memory forecasts.

Modeling volatility of Indian exchange rates under the impact of regime shifts: A study with economic significance analysis

2018 ◽  
Vol 12 (1) ◽  
pp. 43-59
Author(s):  
Dilip Kumar
Keyword(s):  
Exchange Rates ◽  
Long Memory ◽  
Structural Breaks ◽  
Forecast Accuracy ◽  
Regime Shifts ◽  
Policy Implications ◽  
Economic Significance ◽  
Volatility Model ◽  
Modeling Volatility ◽  
The Impact

In this paper, we assess the impact of regime shifts on the long memory properties of the Indian exchange rates. We make use of Sanso, Arago and Carrion (2004) Iterated Cumulative Sum of Squares (hereafter referred as AIT-ICSS) algorithm to detect the points of structural breaks in volatility series. Our findings indicate that incorporating the impact of sudden changes in volatility in the model indeed reduces the magnitude of long memory parameter. In the case of INR/JPY, we observe a shift in characteristics from long memory to mean reversion when the impact of regime shifts is included in the volatility model. Our findings also highlight that incorporating the impact of regime shifts in the model also improves the volatility forecast accuracy. Moreover, we implement a trading strategy based on risk-averse investor and find that the volatility forecasts based on the model which incorporate the impact of structural breaks provide substantial gains in return in comparison to volatility models with no structural breaks. These findings have important policy implications for financial market participants, investors and policy makers.

Determinism and Non-linear Behaviour of Log-return and Conditional Volatility: Empirical Analysis for 26 Stock Markets

2021 ◽  
pp. 227797872199565
Author(s):  
Zouhaier Dhifaoui
Keyword(s):  
Time Series ◽  
Stochastic Volatility ◽  
Linear Prediction ◽  
Stochastic Volatility Model ◽  
Robust Estimator ◽  
Conditional Volatility ◽  
Stochastic Behaviour ◽  
Volatility Model ◽  
Non Linear ◽  
Linear Behaviour

Determinism and non-linear behaviour in log-return and conditional volatility time series of the stock market index is examined for twenty-six countries. For this goal, the principal statistical techniques used in this study are a robust estimator of correlation dimension, a normalized non-linear prediction error, and pseudo-periodic surrogate data method. The proposed approach indicates, first, the stochastic behaviour of all log-return time series. Second, the inability of local linear, ARMA, or state- dependent noise models (such as ARCH, GARCH, and EGARCH) to describe its structure for the frontier, emerging, and developed markets. The same stochastic behaviour of conditional volatility time series, estimated by the stochastic volatility model with moving average innovations, is detected. This finding proves the efficiency of the stochastic volatility model compared with some analysed types of GARCH models for all studied markets. JEL Classification: C12, C52, D53, E44

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