Testing Long memory in the exchange rates and its Implications for the adaptive market hypothesis

2022 ◽  
pp. 126871
Author(s):  
Raheel Asif ◽  
Michael Frömmel
Keyword(s):  
Exchange Rates ◽  
Long Memory

MULTIFRACTALITY AND LONG-RANGE DEPENDENCE OF ASSET RETURNS: THE SCALING BEHAVIOR OF THE MARKOV-SWITCHING MULTIFRACTAL MODEL WITH LOGNORMAL VOLATILITY COMPONENTS

2008 ◽  
Vol 11 (05) ◽  
pp. 669-684 ◽  
Author(s):  
RUIPENG LIU ◽  
T. DI MATTEO ◽  
THOMAS LUX
Keyword(s):  
Exchange Rates ◽  
Foreign Exchange ◽  
Empirical Data ◽  
Long Memory ◽  
Markov Switching ◽  
Asset Returns ◽  
Financial Data ◽  
Long Range Dependence ◽  
Foreign Exchange Rates ◽  
Good Agreement

In this paper, we consider daily financial data from various sources (stock market indices, foreign exchange rates and bonds) and analyze their multiscaling properties by estimating the parameters of a Markov-switching multifractal (MSM) model with Lognormal volatility components. In order to see how well estimated models capture the temporal dependency of the empirical data, we estimate and compare (generalized) Hurst exponents for both empirical data and simulated MSM models. In general, the Lognormal MSM models generate "apparent" long memory in good agreement with empirical scaling provided that one uses sufficiently many volatility components. In comparison with a Binomial MSM specification [11], results are almost identical. This suggests that a parsimonious discrete specification is flexible enough and the gain from adopting the continuous Lognormal distribution is very limited.

scholarly journals Behavior of Exchange Rates and Returns: Long Memory and Cointegration

2012 ◽  
Vol 121 (2B) ◽  
pp. B-121-B-127 ◽  
Author(s):  
E.M. Syczewska
Keyword(s):  
Exchange Rates ◽  
Long Memory

scholarly journals Memory properties of the forward premium: A study on South African exchange rates

2002 ◽  
Vol 5 (3) ◽  
pp. 499-510
Author(s):  
H. Boraine ◽  
P. J. Van Staden
Keyword(s):  
Exchange Rates ◽  
South African ◽  
Long Memory ◽  
Bootstrap Method ◽  
Forward Rate ◽  
Developed Economies ◽  
Forward Premium ◽  
Short Memory ◽  
Forward Rate Unbiasedness Hypothesis ◽  
South African Rand

The forward rate unbiasedness hypothesis states that the current forward rate should be an unbiased forecaster of the future spot rate. Inference has always been done under the assumption that the forward premium is a stationary short memory series. Recent empirical results have indicated that this assumption is not valid. Standard unit root tests performed on the forward premium often indicate infinite long memory. However, in recent literature fractionally integrated models have been applied for the forward premium. Empirical analysis is usually performed on exchange rates of developed economies. In this article, the South African Rand-Dollar exchange rate is considered and the focus is therefore on a developing country. A bootstrap method for determining standard errors and confidence limits is described and implemented.

LONG-MEMORY RISK PREMIA IN EXCHANGE RATES

1996 ◽  
Vol 64 (4) ◽  
pp. 421-438 ◽  
Author(s):  
J. D. BYERS ◽  
D. A. PEEL
Keyword(s):  
Exchange Rates ◽  
Long Memory ◽  
Risk Premia

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.

Sign in / Sign up

Export Citation Format

Share Document