The Time-Varying Systematic Risk of Carry Trade Strategies

CFA Digest ◽  
2012 ◽  
Vol 42 (1) ◽  
pp. 49-51
Author(s):  
Andrew Boral
Keyword(s):  
Systematic Risk ◽  
Time Varying ◽  
Carry Trade

scholarly journals The Time-Varying Systematic Risk of Carry Trade Strategies

2011 ◽  
Vol 46 (4) ◽  
pp. 1107-1125 ◽  
Author(s):  
Charlotte Christiansen ◽  
Angelo Ranaldo ◽  
Paul Söderlind
Keyword(s):  
Asset Pricing ◽  
Systematic Risk ◽  
Time Varying ◽  
Uncovered Interest Parity ◽  
Pricing Model ◽  
Carry Trade ◽  
Asset Pricing Model ◽  
Pricing Errors ◽  
Interest Parity ◽  
Foreign Exchange Volatility

AbstractWe explain the currency carry trade (CT) performance using an asset pricing model in which factor loadings are regime dependent rather than constant. Empirical results show that a typical CT strategy has much higher exposure to the stock market and is mean reverting in regimes of high foreign exchange volatility. The findings are robust to various extensions. Our regime-dependent pricing model provides significantly smaller pricing errors than a traditional model. Thus, the CT performance is better explained by a time-varying systematic risk that increases in volatile markets, suggesting a partial resolution of the uncovered interest parity puzzle.

scholarly journals The Time-Varying Systematic Risk of Carry Trade Strategies

2010 ◽  
Author(s):  
Charlotte Christiansen ◽  
Angelo Ranaldo ◽  
Paul Söderlind
Keyword(s):  
Systematic Risk ◽  
Time Varying ◽  
Carry Trade

The Systematic Risk at the Crisis—A Multifractal Non-Uniform Wavelet Systematic Risk Estimation

2021 ◽  
Vol 5 (4) ◽  
pp. 135
Author(s):  
Mounir Sarraj ◽  
Anouar Ben Mabrouk
Keyword(s):  
Missing Data ◽  
Wavelet Analysis ◽  
Present Method ◽  
Risk Estimation ◽  
Systematic Risk ◽  
Political Stability ◽  
Time Varying ◽  
Political Situation ◽  
Time Varying Parameters ◽  
Market Beta

In the last decade, many factors, such as socio-political and econo-environmental ones, have led to a perturbation in the timeline of the worldwide development, and especially in countries and regions having political changes. This led us to introduce a new idea of risk estimation taking into account the non-uniform changes in markets by introducing a non-uniform wavelet analysis. We aim to explain the econo-political situation of Arab spring countries and the effect of the revolutions on the market beta. The main novelty is first the construction of a dynamic backward-forward model for missing data, and next the application of random non-uniform wavelets. The proposed procedure will be acted empirically on a sample corresponding to TUNINDEX stock as a representative index of the Tunisian market actively traded over the period from 14 January 2016 to 13 January 2021. The chosen 5-year period is important as it constitutes the first five years after the revolution and depends strongly on the socio-econo-political stability in the revolutionary countries. The results showed the efficiency of non-uniform wavelets in explaining the dynamics of the market well. They therefore may be good tools to explore important phenomena in the market such as the non-stationary aspect of financial series, non-constancy, and time-varying parameters. These facts in turn will have positive implications for investors as well as politicians in front of the evolution of the market. Besides, recommendations to extend the present method for other types of wavelets and markets will be of interest.

scholarly journals Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram

Economies ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 18
Author(s):  
Riza Demirer ◽  
Rangan Gupta ◽  
Hossein Hassani ◽  
Xu Huang
Keyword(s):  
Risk Aversion ◽  
Predictive Power ◽  
Investor Sentiment ◽  
Time Varying ◽  
Carry Trade ◽  
Currency Markets ◽  
High Quantiles ◽  
Predictive Role ◽  
The Cross ◽  
Directional Predictability

This paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models.

Estimating Systematic Risk Using Time Varying Distributions

2002 ◽  
Vol 8 (1) ◽  
pp. 59-73 ◽  
Author(s):  
Gregory Koutmos ◽  
Johan Knif
Keyword(s):  
Systematic Risk ◽  
Time Varying

scholarly journals Piecewise Constant Modeling and Tracking of Systematic Risk in Financial Market

2021 ◽  
Author(s):  
Triloke Rajbhandary
Keyword(s):  
Kalman Filter ◽  
Random Walk ◽  
Capital Market ◽  
Significant Risk ◽  
Systematic Risk ◽  
Efficient Market Hypothesis ◽  
Statistical Hypothesis ◽  
Time Varying ◽  
Statistical Hypothesis Testing ◽  
Piecewise Constant

The objective of this thesis is to study the time-varying systematic risk in capital market represented by beta. By using statistical hypothesis testing, we show that beta changes in a piecewise constant pattern in which the changes are governed by triggering economic events. This pattern of beta is different from previously modeled time-varying patterns in literature, such as random walk and mean-reverting models and is consistent with the efficient market hypothesis. We also present a new modeling technique based on Poisson process to represent piecewise constant beta. We develop a new tracking algorithm based on Kalman Filter in which Bayes' selection criteria is incorporated to track piecewise constant beta. Our simulation results show that our proposed tracking method outperforms the traditional random walk and mean reverting model based Kalman Filter tracking. Our empirical case studies also show that our method is efficient in capturing the significant risk changes which are attributed to economic events.

scholarly journals Risk-Adjusted Seasonality in Muslim Frontier Stock Markets

2021 ◽  
Author(s):  
Faheem Aslam ◽  
Ahmed Imran Hunjra ◽  
Tahar Tayachi ◽  
Peter Verhoeven ◽  
Yasir Tariq
Keyword(s):  
Stock Markets ◽  
Systematic Risk ◽  
Time Varying ◽  
Bonferroni Correction ◽  
Market Portfolio ◽  
Stock Indices ◽  
Lunar Calendar ◽  
Investment Opportunities ◽  
Day Of The Week ◽  
Return Seasonality

<p>We investigate the evidence of three risk-adjusted calendar anomalies in eight frontier markets. </p> Our sample consists of the daily closing prices of their stock indices for the period of January 2006 to September 2019. We categorize the data with respect to day-of-the-week, Lunar calendar and Islamic calendar. Using Morgan Stanley Capital International (MSCI) eight Markets Index as our proxy of the market portfolio, most of the frontier markets tested exhibit calendar seasonality. We confirm that systematic risk varies with respect to day-of-the-week, Lunar months and Islamic months. After consideration of time-varying risk and applying Bonferroni correction, few frontier markets exhibit profitable investment opportunities from calendar return anomalies for active investment managers. This study contributes to the existing literature by documenting evidence of the presence of both day-of-the-week and month-of-the-year return seasonality both for the Gregorian and Islamic calendar for frontier markets.

Sign in / Sign up

Export Citation Format

Share Document