			Home About us Contact

Historical Simulation (historical + simulation)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	57%

Selected Abstracts

Backtesting Derivative Portfolios with Filtered Historical Simulation (FHS)

EUROPEAN FINANCIAL MANAGEMENT, Issue 1 2002
Giovanni Barone-Adesi
Filtered historical simulation provides the general framework to our backtests of portfolios of derivative securities held by a large sample of financial institutions. We allow for stochastic volatility and exchange rates. Correlations are preserved implicitly by our simulation procedure. Options are repriced at each node. Overall results support the adequacy of our framework, but our VaR numbers are too high for swap portfolios at long horizons and too low for options and futures portfolios at short horizons. [source]

Non parametric VaR Techniques.

ECONOMIC NOTES, Issue 2 2001
Myths, Realities
VaR (value-at-risk) estimates are currently based on two main techniques: the variance-covariance approach or simulation. Statistical and computational problems affect the reliability of these techniques. We illustrate a new technique , filtered historical simulation (FHS) , designed to remedy some of the shortcomings of the simulation approach. We compare the estimates it produces with traditional bootstrapping estimates. (J.E.L.: G19). [source]

Backtesting Derivative Portfolios with Filtered Historical Simulation (FHS)

Impact of climate change on runoff from a mid-latitude mountainous catchment in central Japan

HYDROLOGICAL PROCESSES, Issue 10 2009
Yoshinori Shinohara
Abstract Hydrologic balance in high-altitude, mid-latitude mountain areas is important in terms of the water resources available to associated lowlands. This study examined how current and historical shifts in precipitation (P) patterns and concurrent increases in temperature (T) affected runoff (Q) and other hydrologic components in a mid-latitude mountain catchment of central Japan, using a combination of long-term data and a simplified hydrologic model, along with their stochastic treatment. The availability of intensive meteorological and hydrological data from the period 1997,2001 allowed the derivation of key relationships for the current climate that tie the forcing term to the parameters or state variables. By using the data recorded in the period 1965,2001, the force for driving the historical simulation was generated. Based on this model and historical shifts in P and T, the probability density functions of Q (pdf(Q)) was computed. A main novelty in this study is that such a stochastic representation, which is useful for considering the influence of projected shifts in environmental factors on the hydrologic budget, was provided. Despite the large increase in the rate of T in winter and spring, pdf(Q) in spring and summer varied appreciably during the time studied mainly because of an increase in snowmelt. An interannual change in whole-year Q was robust to shifts in T because while Q in spring increased, in summer it decreased, implying a crucial effect of global warming on mountain hydrologic regimes is change in the timing of Q. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Portfolio management using value at risk: A comparison between genetic algorithms and particle swarm optimization

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 7 2009
V. A. F. Dallagnol
In this paper, it is shown a comparison of the application of particle swarm optimization and genetic algorithms to portfolio management, in a constrained portfolio optimization problem where no short sales are allowed. The objective function to be minimized is the value at risk calculated using historical simulation where several strategies for handling the constraints of the problem were implemented. The results of the experiments performed show that, generally speaking, the methods are capable of consistently finding good solutions quite close to the best solution found in a reasonable amount of time. In addition, it is demonstrated statistically that the algorithms, on average, do not all consistently achieve the same best solution. PSO turned out to be faster than GA, both in terms of number of iterations and in terms of total running time. However, PSO appears to be much more sensitive to the initial position of the particles than GA. Tests were also made regarding the number of particles needed to solve the problem, and 50 particles/chromosomes seem to be enough for problems up to 20 assets. © 2009 Wiley Periodicals, Inc. [source]

Hedging and value at risk

THE JOURNAL OF FUTURES MARKETS, Issue 4 2006
Richard D. F. Harris
In this article, it is shown that although minimum-variance hedging unambiguously reduces the standard deviation of portfolio returns, it can increase both left skewness and kurtosis; consequently the effectiveness of hedging in terms of value at risk (VaR) and conditional value at risk (CVaR) is uncertain. The reduction in daily standard deviation is compared with the reduction in 1-day 99% VaR and CVaR for 20 cross-hedged currency portfolios with the use of historical simulation. On average, minimum-variance hedging reduces both VaR and CVaR by about 80% of the reduction in standard deviation. Also investigated, as an alternative to minimum-variance hedging, are minimum-VaR and minimum-CVaR hedging strategies that minimize the historical-simulation VaR and CVaR of the hedge portfolio, respectively. The in-sample results suggest that in terms of VaR and CVaR reduction, minimum-VaR and minimum-CVaR hedging can potentially yield small but consistent improvements over minimum-variance hedging. The out-of-sample results are more mixed, although there is a small improvement for minimum-VaR hedging for the majority of the currencies considered. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:369,390, 2006 [source]

"It's All About Perspective": Using Simulations in Multicultural Teaching

HISTORY COMPASS (ELECTRONIC), Issue 5 2006
Kat Williams
Many feminist pedagogues create a classroom climate that transforms the student from a passive beneficiary of knowledge to an active participant in the classroom community. This objective is accomplished in many different ways and while there is no way to ensure student participation in the classroom and not one technique that works for every situation, the common thread is the rejection of traditional, passive forms of learning in favor of alternative, active teaching methods. History is not experienced the same way by all people , it is not a seamless narrative or a single story, but a series of competing voices. To demonstrate my use of historical simulations in the classroom this article focuses on World War II and the alternative perspectives, including women's baseball, to which students are exposed and ultimately represent in the simulation. [source]