Silvia Stanescu

Commercial Real Estate Risk Management with Derivatives

Helping managers to gauge the interaction between commercial property prices and interest rates for commercial property and CMBS portfolios.

An improved method for pricing and hedging long dated American options

The majority of quasi-analytic pricing methods for American options are efficient near maturity but are prone to larger errors when time-to-maturity increases. We introduce a new methodology to increase the accuracy of almost any existing quasi-analytic approach in pricing long-maturity American options. The new methodology, called the “extension-method”, relies on an approximation of the optimal exercise price near the beginning of the contract combined with existing pricing approaches so that the maturity range for which small errors are attainable is extended. Our method retains the quasi-analytic nature of the methods it improves. Generic quasi-analytic formulae for the price of an American put as well as for its hedging parameter are derived. Our scenarios-based numerical study indicates that our method considerably improves both the pricing and the hedging performance of a number of established approaches for a wide range of maturities. The superiority of this approach is illustrated with real financial data by considering S&P 100TM LEAPS® options traded from January 2008 to May 2015.

Analytic Moments For GARCH Processes

Conditional returns distributions generated by a GARCH process, which are important for many problems in market risk assessment and portfolio optimization, are typically generated via simulation. This paper extends previous research on analytic moments of GARCH returns distributions in several ways: we consider a general GARCH model -- the GJR specification with a generic innovation distribution; we derive analytic expressions for the first four conditional moments of the forward return, of the forward variance, of the aggregated return and of the aggregated variance -- corresponding moments for some specific GARCH models largely used in practice are recovered as special cases; we derive the limits of these moments as the time horizon increases, establishing regularity conditions for the moments of aggregated returns to converge to normal moments; and we demonstrate empirically that some excellent approximate predictive distributions can be obtained from these analytic moments, thus precluding the need for time-consuming simulations.

Analytic Approximations to GARCH Aggregated Returns Distributions with Applications to VaR and ETL

It is widely accepted that some of the most accurate predictions of aggregated asset returns are based on an appropriately specified GARCH process. As the forecast horizon is greater than the frequency of the GARCH model, such predictions either require time-consuming simulations or they can be approximated using a recent development in the GARCH literature, viz. analytic conditional moment formulae for GARCH aggregated returns. We demonstrate that this methodology yields robust and rapid calculations of the Value-at-Risk (VaR) generated by a GARCH process. Our extensive empirical study applies Edgeworth and Cornish-Fisher expansions and Johnson SU distributions, combined with normal and Student t, symmetric and asymmetric (GJR) GARCH processes to returns data on different financial assets; it validates the accuracy of the analytic approximations to GARCH aggregated returns and derives GARCH VaR estimates that are shown to be highly accurate over multiple horizons and significance levels.

Investment Strategies with VIX and VSTOXX Futures

This study examines historical data on S&P500 and EURO STOXX 50, VIX and VSTOXX, VIX and VSTOXX futures, to reveal linkages between these important series that can be used by equity investors to generate alpha and protect their investments during turbulent times. A comparative portfolio performance analysis in the U.S. and the E.U. zone reveals that over time the best investment strategy for a stock investor is to add both bonds and volatility futures to their portfolio. We also reveal a long-short cross border statistical arbitrage strategy pairing volatility index futures that can generate profits using forecasts produced by suitable GARCH models.