Is gold a safe haven for the dynamic risk of foreign exchange?

This paper uses the panel data of 15 countries from 2009 to 2020 to construct the time-varying parameter panel vector error correction model for testing the hypothesis of dynamic hedging characteristics of gold on exchange rate. As the existing literature has never considered that the foreign exchange risk hedged by gold is dynamic, this study can fill the research gap in this area. The empirical results show that: First, gold can partly hedge against the depreciation of the currency in the long run; second, gold is unable to hedge against the risk of the exchange rate when considering dynamic hedging effects in the short run; third, when facing unexpected shocks, the impulse response shows that the gold returns have reversible reactions compared to exchange rate fluctuations; therefore, gold can regard as a safe haven for foreign exchange markets; Finally, the government, as well as investors should always be concerned about these dynamic risks and formulate effective hedging strategies to control the currency uncertainty.

EFFICIENT DYNAMIC HEDGING FOR LARGE VARIABLE ANNUITY PORTFOLIOS WITH MULTIPLE UNDERLYING ASSETS

A variable annuity (VA) is an equity-linked annuity that provides investment guarantees to its policyholder and its contributions are normally invested in multiple underlying assets (e.g., mutual funds), which exposes VA liability to significant market risks. Hedging the market risks is therefore crucial in risk managing a VA portfolio as the VA guarantees are long-dated liabilities that may span decades. In order to hedge the VA liability, the issuing insurance company would need to construct a hedging portfolio consisting of the underlying assets whose positions are often determined by the liability Greeks such as partial dollar Deltas. Usually, these quantities are calculated via nested simulation approach. For insurance companies that manage large VA portfolios (e.g., 100k+ policies), calculating those quantities is extremely time-consuming or even prohibitive due to the complexity of the guarantee payoffs and the stochastic-on-stochastic nature of the nested simulation algorithm. In this paper, we extend the surrogate model-assisted nest simulation approach in Lin and Yang [(2020) Insurance: Mathematics and Economics, 91, 85–103] to efficiently calculate the total VA liability and the partial dollar Deltas for large VA portfolios with multiple underlying assets. In our proposed algorithm, the nested simulation is run using small sets of selected representative policies and representative outer loops. As a result, the computing time is substantially reduced. The computational advantage of the proposed algorithm and the importance of dynamic hedging are further illustrated through a profit and loss (P&L) analysis for a large synthetic VA portfolio. Moreover, the robustness of the performance of the proposed algorithm is tested with multiple simulation runs. Numerical results show that the proposed algorithm is able to accurately approximate different quantities of interest and the performance is robust with respect to different sets of parameter inputs. Finally, we show how our approach could be extended to potentially incorporate stochastic interest rates and estimate other Greeks such as Rho.

Pricing and Hedging American-Style Options with Deep Learning

In this paper we introduce a deep learning method for pricing and hedging American-style options. It first computes a candidate optimal stopping policy. From there it derives a lower bound for the price. Then it calculates an upper bound, a point estimate and confidence intervals. Finally, it constructs an approximate dynamic hedging strategy. We test the approach on different specifications of a Bermudan max-call option. In all cases it produces highly accurate prices and dynamic hedging strategies with small replication errors.

Dynamic hedging of prices of Natural Gas in Mexico

Los precios de venta de primera mano del gas natural (GN) en México tuvieron una relación dinámica, pero con retrasos, con los precios internacionales de los futuros de GN durante el periodo de enero de 2012 a junio de 2017. A partir de una estrategia de cobertura en la que se emplean futuros de GN y utilizando un modelo MGARCH VCC para estimar las variaciones condicionales con retrasos de 20 y 40 días de los precios de los futuros, se muestra cómo se comportan las coberturas dinámicas de GN, suponiendo precios teóricos futuros del dólar estadounidense en pesos mexicanos. A través de una prueba retrospectiva, se halló que las predicciones de las razones de cobertura óptima mejoran con períodos cortos de pronóstico y períodos cercanos de observación. El modelo de cobertura dinámica propuesto puede extenderse a otros mercados de combustibles. Se destaca la importancia de la cobertura de los precios del GN dado el tamaño del mercado y la magnitud del riesgo al que se encuentran expuestos los participantes.

Price risk management by using dynamic hedging based on advanced Black–Scholes model

Price Risk Management plays an important role in Commodity trading and corporate purchasing or Sales plan. Futures are used to hedge the price risk which is linear, while options are used for the nonlinear one. This paper proposes an evaluation method of dynamic hedging strategy for corporate hedging commodity price risk based on advanced Black–Scholes Model. By using the inverse replication method, we get the dynamic hedging strategy which uses futures to replicate options. Finally, we apply the dynamic hedging strategy for corporate purchases and sales to either lower purchase cost or maintain the sales price.