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Full-Text Articles in Physical Sciences and Mathematics
Pricing Asian Options: Volatility Forecasting As A Source Of Downside Risk, Adam T. Diehl
Pricing Asian Options: Volatility Forecasting As A Source Of Downside Risk, Adam T. Diehl
Undergraduate Economic Review
Asian options are a class of derivative securities whose payoffs average movements in the underlying asset as a means of hedging exposure to unexpected market behavior. We find that despite their volatility smoothing properties, the price of an Asian option is sensitive to the choice of volatility model employed to price them from market data. We estimate the errors induced by two common schemes of forecasting volatility and their potential impact upon trading.
Recent Periods Of Financial Turbulence On The Russian Stock Market And Their Effect On Price Correlation And Value At Risk, Alexander Logoveev, Gregory Cherinko
Recent Periods Of Financial Turbulence On The Russian Stock Market And Their Effect On Price Correlation And Value At Risk, Alexander Logoveev, Gregory Cherinko
Undergraduate Economic Review
The aim of this article is to observe and analyze the recent periods of financial turbulence on the Russian stock market and determine their influence on the correlation coefficients between asset prices and the Value at Risk measure for a portfolio. Our task was to describe the previously observed phenomenon of correlation enlargement during times of financial crises deemed in our research as separate Black Swans. Based on up-to-date financial data analysis we determined correlation trends that can be useful in risk management and applied the Value at Risk method.
Top Of The Order: Modeling The Optimal Locations Of Minor League Baseball Teams, W. Coleman Conley
Top Of The Order: Modeling The Optimal Locations Of Minor League Baseball Teams, W. Coleman Conley
Undergraduate Economic Review
Over the last twenty-five years, minor league baseball franchises have defined firm mobility. Revisiting the work of Michael C. Davis (2006), I construct a logistic regression model to predict which cities house minor league baseball teams. Six variables are tested for inclusion in the model, including population, income level, the number of major-league professional sports teams in a city, five-year population change, and distance from the closest professional team. Based on the model's predicted probabilities, cities are ranked in order of highest probability of having a team at each of the different levels from Class A to Class AAA.