Credit derivatives are among the most criticized financial instruments in the current credit crises. Given their short history, finance professionals are still researching to discover effective ways to reduce the mark-to-market (MTM) volatility in credit derivatives, especially in turbulent market conditions. Many credit portfolios have been struggling to find out appropriate tools and techniques to help them navigate the current credit crises and hedge mark-to-market volatility in their portfolios. In this study we provide a tool kit to help reduce the pricing fluctuations in structured credit portfolios utilizing data analysis and statistical methods. In Chapter One we provide a snapshot of credit derivatives market by summarizing different types of credit derivatives; including single-name credit default swaps (CDS), market credit indices, bespoke portfolios, market index tranches, and bespoke tranches (synthetic CDOs). In Chapter Two we illustrate a method to calculate a stable hedge ratio (beta) by combining industry practices and statistical techniques. Choosing an appropriate hedge ratio is critical for funds that desire to hedge mark-to-market volatility. Many credit portfolios suffered 40%-80% market value losses in 2008 and 2009 due to the mark-to-market volatility in their long positions. In this chapter we introduce ten different betas in order to hedge a long bespoke portfolio by liquid market indices. We measure the effectives of these betas by two measures: Stability and mark-to-market volatility reduction. Among all betas we present, we deduct that the following betas are appropriate to be used as hedge ratios: Implied Beta, Quarterly Regression Beta on Spread Levels, Yearly Regression Betas on Spread Levels, Up Beta, and Down Beta. In Chapter Three we analyze the risk factors that impact the MTM volatility in CDS tranches; namely Spread Risk, Correlation Risk, Dispersion Risk, and Curve Risk. We focus our analysis in explaining the risks in the equity tranche as this is the riskiest tranche in the capital structure. We show that all four risks introduced are critical in explaining MTM volatility in equity tranches. We also perform multiple regression analysis to show the correlations between different risk factors. We show that, when combined, spread, correlation, and dispersion risks are the most important risk factors in analyzing MTM fluctuations in equity tranche. Curve risk can be used as an add-on risk to further explain local instances. After understanding various risk factors that impact the MTM changes in equity tranche, we put this knowledge to work to analyze two instances in 2008 in which we experienced significant spread widening in equity tranche. Both examples show that a good understanding of the risks that drive MTM changes in CDS tranches is critical in making informed trading decisions. In Chapter Four we focus on two topics: Portfolio Stratification and Index Selection. While portfolio stratification helps us better understand the composition of a portfolio, index selection shows us which indices are more suitable in hedging long bespoke positions. In stratifying a portfolio we define Class-A as the widest credits, Class-B as the middle tier, and Class-C as the tightest credits in a credit portfolio. By portfolio stratification we show that Class-A has significant impact on the overall portfolio. We use five different risk measures to analyze different properties of the three classes we introduce. The risk measures are Sum of Spreads (SOS), Sigma/Mu, Basis Point Volatility (BPVOL), Skewness, and Kurtosis. For all risk measures we show that there is high correlation between Class-A and the whole portfolio. We also show that it is critical to monitor the risks in Class-A to better understand the spread moves in the overall portfolio. In the second part of Chapter Four, we perform analysis to find out which credit index should be used in hedging a long bespoke portfolio. We compare four credit indices for their ability to track the bespoke portfolio on spread levels and on spread changes. Analysis show that CDX.HY and CDX IG indices fits the best to hedge our sample bespoke portfolio in terms of spread levels and spread changes, respectively. Finally, we perform multiple regression analysis using backward selection, forward selection, and stepwise regression methods to find out if we should use multiple indices in our hedging practices. Multiple regression analysis show that CDX.HY and CDX.IG are the best candidates to hedge the sample bespoke portfolio we introduced.