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

scholarly journals Exchange Rates and Liquidity Risk

2020 ◽  
Vol 11 (2) ◽  
pp. 159
Author(s):  
Martin D.D. EVANS
Keyword(s):  
Risk Factors ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Foreign Currency ◽  
Liquidity Risk ◽  
Risk Premia ◽  
Time Varying ◽  
Foreign Exchange Risk ◽  
Exchange Risk

I use Forex trading data to study how risks associated with the lack of liquidity contribute to the dynamics of 17 spot exchange rates through their time-varying contributions to risk premia. I find that liquidity risk matters. All the foreign exchange risk premia compensate investors for exposure to liquidity risk; and, for many currencies, exposure to liquidity risk appears to be more important than exposure to the traditional carry and momentum risk factors. I also find that variations in the price of liquidity risk make economically important contributions to the behavior of individual foreign currency returns: they account for approximately 34%, on average, of the variability in currency returns compared to the contribution of approximately 8% from the prices of carry and momentum risk.

Exchange Rates and the Conversion of Currency-Specific Risk Premia

2007 ◽  
Vol 13 (4) ◽  
pp. 672-701
Author(s):  
Astrid Eisenberg ◽  
Markus Rudolf
Keyword(s):  
Exchange Rates ◽  
Risk Premia ◽  
Specific Risk

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.

FORECASTING FORWARD EXCHANGE RATE RISK PREMIUM IN SINGAPORE DOLLAR/US DOLLAR EXCHANGE RATE MARKET

2009 ◽  
Vol 54 (02) ◽  
pp. 283-298 ◽  
Author(s):  
KHURSHID M. KIANI
Keyword(s):  
Exchange Rates ◽  
Risk Premium ◽  
Risk Premia ◽  
Foreign Exchange Rates ◽  
Time Varying ◽  
Volatility Clustering ◽  
Significant Evidence ◽  
Gaussian Signal ◽  
Us Dollar ◽  
Noise Models

In this research, monthly forward exchange rates are evaluated for possible existence of time varying risk premia in Singapore forward foreign exchange rates against US dollar. The time varying risk premia in Singapore dollar is modeled using non-Gaussian signal plus noise models that encompass non-normality and time varying volatility. The results from signal plus noise models show statistically significant evidence of time varying risk premium in Singapore forward exchange rates although we failed to reject the hypotheses of no risk premium in the series. The results from Gaussian versions of these models are not much different and are in line with Wolff (1987) who also used the same methodology in Gaussian settings. Our results show statistically significant evidence of volatility clustering in Singapore forward exchange rates. The results from Gaussian signal plus noise models also show statistically significant evidence of volatility clustering and non-normality in Singapore forward foreign exchange rates. Additional tests on the series show that exclusion of conditional heteroskedasticity from the signal plus noise models leads to false statistical inferences.

Sign in / Sign up

Export Citation Format

Share Document