Upside and Downside Risk Exposures of Currency Carry Trades via Tail Dependence

Ames, Matthew M.; Peters, Gareth W.; Bagnarosa, Guillaume; Kosmidis, Ioannis

doi:10.1007/978-3-319-09114-3_10

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Under the uncovered interest rates parity hypothesis (UIP), such profit opportunities should not occur recurrently or at least should not be profitable on average. However, several empirical works have already demonstrated the existence of such UIP violations and the resulting sizable profits (Ames, Peters, Bagnarosa, & Kosmidis, ; Backus, Foresi, & Telmer, ; Brunnermeier et al, ; Burnside, Eichenbaum, Kleshchelski, & Rebelo, ; Christiansen, Ranaldo, & Söderlind, ; Fama, ; Hansen & Hodrick, ; Lustig et al, ; Lustig & Verdelhan, ; Menkhoff, Sarno, Schmeling, & Schrimpf, ). Before introducing UIP we first have to define a no‐arbitrage relationship, well known in finance, which establishes the relation between spot and forward exchange rates as a function of the two nominal interest rates prevailing in the respective countries.…”

Section: Empirical Application To the Currency Portfolio Analysismentioning

confidence: 99%

“…We stress the fact that this could not be considered as the optimal carry trade portfolio as we split the optimization into two optimization subprograms conditionally on different sets of filtrations

{scriptG}_{t}^{high}

{scriptF}_{t}^{high}

, and

{scriptG}_{t}^{low}

{scriptF}_{t}^{low}

, associated with the high‐ and the low‐interest rate basket models, respectively. It is worth mentioning that this two‐step procedure was motivated by the noticeably different dependence structure behaviors for the high and the low baskets, as demonstrated in previous studies (Ames et al, , ). Considering the carry trade portfolio configuration, we focus in this section on the constrained version of the GMV optimizer for the high‐ and the low‐interest‐rate currency portfolios.…”

Section: Empirical Application To the Currency Portfolio Analysismentioning

confidence: 99%

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Understanding the interplay between covariance forecasting factor models and risk‐based portfolio allocations in currency carry trades

Ames

Bagnarosa

Peters

et al. 2018

Journal of Forecasting

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With the exception of naive methods for portfolio selection, such as the equal weighted approaches, all other methods of portfolio allocation are more or less sensitive to the quality of the inputs considered in constructing the models and risk measures utilized in the allocation framework. The extensively used factor model proposed initially by Sharpe in 1963 has provided a robust backdrop for development of relevant, micro, macro, and context‐specific or asset specific explanatory variables to be incorporated in a statistical manner as inputs to forecasting models that can then be used to obtain risk measures upon which portfolio allocations are based. However, like all statistical models, a set of statistical assumptions accompany this factor model regression framework, one of which has recently been highlighted as seemingly nonvalidated in financial data. This is, of course, the assumption such factor models make on homoskedasticity or weak‐sense covariance stationarity of the returns processes being modeled. Such factor models, therefore, have typically failed to cope with an important and ubiquitous feature of financial assets data, which often demonstrates heteroskedasticity of the returns variances and covariances. We propose a novel generalized multifactor forecasting structure utilizing a covariance regression model, which allows us to incorporate the required heteroskedasticity effects while also admitting potential dependence in the idiosyncratic error terms. We argue that such a modeling approach allows for more explicit relationships to be interpreted between the driving factors and the conditional responses of the portfolio returns. We then compare the forecasting performances of our model with the multifactor model and the time series dynamic conditional correlation model through a currency portfolio application.

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Section: Empirical Application To the Currency Portfolio Analysismentioning

confidence: 99%

{scriptG}_{t}^{high}

{scriptF}_{t}^{high}

, and

{scriptG}_{t}^{low}

{scriptF}_{t}^{low}

Section: Empirical Application To the Currency Portfolio Analysismentioning

confidence: 99%

Understanding the interplay between covariance forecasting factor models and risk‐based portfolio allocations in currency carry trades

Ames

Bagnarosa

Peters

et al. 2018

Journal of Forecasting

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, we discuss a class of the non-parametric copula model, which is a flexible model-free approach. Specifically, tail dependencies are quantified non-parametrically, as detailed in [44], via the following concordance measure for upper tail dependence:…”

Section: ) Application Of a Non-parametric Copulamentioning

confidence: 99%

“…( 24). The resulting model is expressed by a matrix notation: (44) where f describes the Gaussian process and v the white noise. Eq.…”

Section: ) Spatial Field Best Linear Unbiased Estimator (S-blue) Via ...mentioning

confidence: 99%

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Participatory Sensing Data Tweets for Micro-Urban Real-Time Resiliency Monitoring and Risk Management

et al. 2016

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Real-time urban climate monitoring provides useful information that can be utilized to help urban management personnel to monitor and adapt their precautionary measures to extreme events, including urban heatwaves. Fortunately, recently created social media platforms, such as Twitter, furnish real-time and high-resolution spatial information that may be useful for climate condition estimation. The objective of this paper was to utilize geotagged tweets (participatory sensing data) for urban temperature analysis. We first detected tweets related to heat (heat-tweets). Then, we examined the relationships between monitored temperatures and heat-tweets through a statistical model framework based on copula modeling methods. We demonstrate that there are strong relationships between heat-tweets and temperatures recorded at an intraurban scale, which are revealed by our analysis of Tokyo city and its suburbs. Subsequently, we investigated the application of heat-tweets for informing spatiotemporal Gaussian process interpolation of temperatures as an application example of heat-tweets. We utilized a combination of spatially sparse weather monitoring sensor data, which comprise infrequently available moderate resolution imaging spectroradiometer remote sensing data and spatially and temporally dense lower quality geotagged Twitter data. A spatial best linear unbiased estimation technique was applied. The results suggest that tweets provide the useful auxiliary information for urban climate assessment.

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