Understanding Carry Trade Risks Using Bayesian Methods: A Comparison with Other Portfolio Risks from Currency, Commodity and Stock Markets
type: CreativeWork
description: The purpose of this dissertation is to understand the risks embedded in Carry Trades. For this, we use a broad range of stochastic volatility (SV) models, estimate them using Bayesian techniques via Markov chain Monte Carlo methods, and analyze various risk measures using these estimation results. Many researchers have tried to explain the risk factors deriving Carry returns with standard risk models (factor models, Sharp ratios etc.). However, the high negative conditional skewness of Carry Trades hints the existence of jumps and shows that they have non normal returns, suggesting looking only at first two moments such as sharp ratios or using standard risk models are not enough to understand their risks. Therefore, we investigate Carry risks by delving into its SV and jump components and separate out their effects for a more thorough analysis. We also compare these results with other market portfolios (S&P 500, Fama HML, Momentum, Gold, AUD/USD, Euro/USD, USD/JPY, DXY, Long Rate Carry and Delta Short Rate Carry) to be able to judge the riskiness of Carry relative to other investment alternatives. We then introduce a new model diagnostic method, which overcomes the flaws of the previous methods used in the literature. This is important since model selection is a central question in SV literature, and although various methods were suggested earlier, they do not provide a reliable measure of fit. Using this new diagnostic method, we select the best-fitted SV model for each portfolio and use their estimation results to carry out the risk analysis. We find that the extremes of volatility, direct negative impact of volatilities on returns, percent of overall risk due to jumps considering both returns and vols, and negative skewness are all more pronounced for Carry Trades than for other portfolios. This shows that Carry risks are more complicated than other portfolios. Hence, we are able to remove a layer from the Carry risks by analyzing its jump and SV components in m…
abstract: The purpose of this dissertation is to understand the risks embedded in Carry Trades. For this, we use a broad range of stochastic volatility (SV) models, estimate them using Bayesian techniques via Markov chain Monte Carlo methods, and analyze various risk measures using these estimation results. Many researchers have tried to explain the risk factors deriving Carry returns with standard risk models (factor models, Sharp ratios etc.). However, the high negative conditional skewness of Carry Trades hints the existence of jumps and shows that they have non normal returns, suggesting looking only at first two moments such as sharp ratios or using standard risk models are not enough to understand their risks. Therefore, we investigate Carry risks by delving into its SV and jump components and separate out their effects for a more thorough analysis. We also compare these results with other market portfolios (S&P 500, Fama HML, Momentum, Gold, AUD/USD, Euro/USD, USD/JPY, DXY, Long Rate Carry and Delta Short Rate Carry) to be able to judge the riskiness of Carry relative to other investment alternatives. We then introduce a new model diagnostic method, which overcomes the flaws of the previous methods used in the literature. This is important since model selection is a central question in SV literature, and although various methods were suggested earlier, they do not provide a reliable measure of fit. Using this new diagnostic method, we select the best-fitted SV model for each portfolio and use their estimation results to carry out the risk analysis. We find that the extremes of volatility, direct negative impact of volatilities on returns, percent of overall risk due to jumps considering both returns and vols, and negative skewness are all more pronounced for Carry Trades than for other portfolios. This shows that Carry risks are more complicated than other portfolios. Hence, we are able to remove a layer from the Carry risks by analyzing its jump and SV components in m…
author: Gunes, Damla
language: English
keywords: Finance
date published: 2012
date modified: 2026-07-24T01:44:18Z
provider: Columbia University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Columbia University

PropertyValue
property ID: DOI
value: 10.7916/D87M0FZB