Currency Total Return Swaps: Valuation and Risk Factor Analysis

in Quantitative Finance (in press)

Currency total return swaps (CTRS) are hybrid derivatives instruments that allow to simultaneously hedge against credit and currency risks. We develop a structural credit risk model to evaluate CTRS ... [more ▼]

Currency total return swaps (CTRS) are hybrid derivatives instruments that allow to simultaneously hedge against credit and currency risks. We develop a structural credit risk model to evaluate CTRS premia. Empirical test on a sample of 23,005 price observations from 59 underlying issuers yields an average percentage error of around 10%. This indicates that, beyond interest rate risk, firm-specific factors are major drivers of the variations in the valuation of these instruments. Regression analysis of residuals shows that exchange rate determinants account for up to 40% of model pricing errors — indicating that a currency risk premium affects the CTRS price significantly but only marginally, which confirms the prevalence of credit risk in the pricing of CTRS. [less ▲]

Higher-moment risk exposures in hedge funds

Scientific conference (2013, January 16)

Evaluating Portfolio Performance: Reconciling Asset Selection and Market Timing

in Baker, H. Kent; Filbeck, Georges (Eds.) Portfolio Theory and Management (2013)

This chapter examines the performance measurement of mutual funds when both security selection and market timing management skills are considered. In an option replication approach, linear and quadratic ... [more ▼]

This chapter examines the performance measurement of mutual funds when both security selection and market timing management skills are considered. In an option replication approach, linear and quadratic coefficients of the Treynor and Mazuy regression are combined to assess performance in presence of market timing. This new correction has the potential to overcome the “artificial timing” bias and delivers particularly encouraging results on a sample of 1,413 U.S. mutual funds selected for an empirical analysis. Unlike alternative approaches proposed in the literature, most of positive market timers seem to be rewarded for the convexity they add to their portfolio, while negative market timers are penalized, and a correlation between abnormal performance and the convexity parameter is found. [less ▲]

Excess Return Forecast Using a Dynamic Asset Class Factor Model

E-print/Working paper (2012)

We propose a Dynamic Hierarchical Factor Model using Asset classes to predict mutual funds excess returns. We use different forecast combination schemes of bivariate model considering each asset class ... [more ▼]

We propose a Dynamic Hierarchical Factor Model using Asset classes to predict mutual funds excess returns. We use different forecast combination schemes of bivariate model considering each asset class factor in isolation. Primary analysis highlights the importance to account for asset class specific variations together with between classes or common variations. Further refinements of the a priori repartition are however in order. Forecasting performance of the model outperforms the historical mean benchmark both in terms of MSPE and utility based criteria. A forecasting exercise matching more closely real-time conditions must be undertaken to validate these initial results. [less ▲]

Higher-moment risk exposures in hedge funds

Conference (2012, December)

Risk Horizon and Expected Market Returns

E-print/Working paper (2012)

The paper proposes an equilibrium asset pricing model that accounts of the incomplete information on returns distribution and investors' preferences. Only moments up to order four of unknown unconditional ... [more ▼]

The paper proposes an equilibrium asset pricing model that accounts of the incomplete information on returns distribution and investors' preferences. Only moments up to order four of unknown unconditional distribution can be observed, and the model does not impose that portfolio diversi fication or moments preference should hold. Using Chebyshev-type of inequalities, an intuitive risk measure (risk horizon) is introduced with reference to the speed of convergence of a security's mean return to its expectations. By an arbitrage argument, this risk measure is related to the horizon of treasury securities in a system of equations that allows the calibration of the model parameters using term structure information. In particular, the expected return on the market portfolio can be endogenously estimated inside this system. The model calibration on U.S. market data provides plausible parameters estimates and interesting cyclical patterns in the time series of the expected return. The empirical relevance of these estimates is examined with tests of statistical and economic predictive ability for stock excess returns. The results provide signi ficant evidence on the added value of the estimates when compared to popular predictors found in the literature (see a.o. Lettau and Ludvigson, 2001; Rapach and Wohar, 2006; Goyal and Welch, 2008). [less ▲]

Measuring operational risk in financial institutions

in Applied Financial Economics (2012), 22(18), 1553-1569

The scarcity of internal loss databases tends to hinder the use of the advanced approaches for operational risk measurement (Advanced Measurement Approaches (AMA)) in financial institutions. As there is a ... [more ▼]

The scarcity of internal loss databases tends to hinder the use of the advanced approaches for operational risk measurement (Advanced Measurement Approaches (AMA)) in financial institutions. As there is a greater variety in credit risk modelling, this article explores the applicability of a modified version of CreditRisk+ to operational loss data. Our adapted model, OpRisk+, works out very satisfying Values-at-Risk (VaR) at 95% level as compared with estimates drawn from sophisticated AMA models. OpRisk+ proves to be especially worthy in the case of small samples, where more complex methods cannot be applied. OpRisk+ could therefore be used to fit the body of the distribution of operational losses up to the 95%-percentile, while Extreme Value Theory (EVT), external databases or scenario analysis should be used beyond this quantile. [less ▲]

Is the KIID sufficient to associate portfolios to investor profiles?

in Bankers, Markets, Investors [=BMI] (2012), (118), 14-22

With the Key Investor Information Document (KID), the new UCITS IV framework brings a useful standardized and simplified scheme to explain the risk of mutual funds to non-professional investors. The ... [more ▼]

With the Key Investor Information Document (KID), the new UCITS IV framework brings a useful standardized and simplified scheme to explain the risk of mutual funds to non-professional investors. The Synthetic Risk and Reward Indicator (SRRI) methodology defines how to assess a volatility equivalent for each type of funds, and recognizes the specificities of various types of investment vehicles in the process. The SRRI rests upon two key principles: (i) risk-volatility mapping: the level of risk can be adequately translated by the volatility of returns; and (ii) reward to volatility: there must be a positive connection between the level of risk borne by the individual investor and the associated reward in terms of returns. We show that the SRRI methodology does not guarantee that these two principles are respected in practice. By forcing any type of risk to be translated into a volatility estimate, the approach overlooks investor’s heterogeneity in the definition of risk. The SRRI synthetic approach is powerless to adequately reflect the trade-off between normal and extreme risks the way it is perceived by individual investors. It also ignores that fund returns are not necessarily only related to volatility. We show that the KID does not replace a proper investment profiling system. The analysis of investor profiles is a necessary complement to the KID in order to provide adequate advice to investors. We provide an approach, based on the linear-exponential utility function, that enables the financial advisor to address the heterogeneity of investors when defining the risk of an investment portfolio. [less ▲]

Higher-Moment Risk Exposures in Hedge Funds

Conference (2012, April)

The paper singles out the key roles of US equity skewness and kurtosis in the determination of the market premia embedded in Hedge Fund returns. We propose a conditional higher-moment asset pricing model ... [more ▼]

The paper singles out the key roles of US equity skewness and kurtosis in the determination of the market premia embedded in Hedge Fund returns. We propose a conditional higher-moment asset pricing model with location, trading and higher-moment factors in order to describe the dynamics of the Equity Hedge (Market Neutral, Short Selling and Long/Short strategies), Event Driven, Relative Value, and Funds of Hedge Funds styles. The volatility, skewness and kurtosis implied in the US options markets are used by Hedge Fund managers as instruments to anticipate market movements. Managers should adjust their market exposure in response to variations in the implied higher moments. We show that higher-moment premia improve a conditional asset pricing model both in terms of explanatory power (R-squares and Schwarz criterion) and specification errors across all Hedge Fund styles. [less ▲]

Measuring operational risk in financial institutions

in Applied Financial Economics (2012), 22(18), 1553-1569

The scarcity of internal loss databases tends to hinder the use of the advanced approaches for operational risk measurement (Advanced Measurement Approaches (AMA)) in financial institutions. As there is a ... [more ▼]

The scarcity of internal loss databases tends to hinder the use of the advanced approaches for operational risk measurement (Advanced Measurement Approaches (AMA)) in financial institutions. As there is a greater variety in credit risk modelling, this article explores the applicability of a modified version of CreditRisk+ to operational loss data. Our adapted model, OpRisk+, works out very satisfying Values-at-Risk (VaR) at 95% level as compared with estimates drawn from sophisticated AMA models. OpRisk+ proves to be especially worthy in the case of small samples, where more complex methods cannot be applied. OpRisk+ could therefore be used to fit the body of the distribution of operational losses up to the 95%-percentile, while Extreme Value Theory (EVT), external databases or scenario analysis should be used beyond this quantile. [less ▲]