Who doesn’t have portfolio risk? Let’s see how to quantify it quickly and accurately.
Take an in-depth, statistical approach to managing risk, utilizing modern, robust tools.
Round out your portfolio: equity, fixed income, alternatives, you name it.
Professionals in (or looking to transition into) these fields:Asset Management Portfolio Management Risk Management Portfolio Optimization
… including anyone providing services to asset managers.
Weak employment figures just came out.
Does this portend a decline in corporate profits and falling equity prices? Or does it signal potential intervention from the central bank, and thus rising equity prices?
This in-depth intro to credit risk covers techniques for modeling credit transition matrices, default probabilities, credit portfolio risk, and prepayment models.
Everything is done in Excel/VBA.
Explore Value at Risk (VaR) modeling for stocks, bonds, forward contracts, futures contracts, swaps and options.
We review Historical Simulation, Weighted Historical Simulation, and Monte Carlo Simulation, along with several ways to measure the impact of a potential trade on portfolio VaR.
We cover Markowitz’ formula in detail, along with CAPM, the Capital Market Line, and the Security Market Line.
With Excel Solver, we can put this all together to derive the efficent frontier from a portfolio of stocks.
Our approach is to teach you how to fish, rather than give you a fish.
We don't give a one-way lecture where you memorize every cell and formula.
We nudge you toward uncovering answers on your own by leading with the right questions.
The end result? Longer-term knowledge retention that will last an entire career.
I really felt that WST was world class and would recommend it to anyone starting a new career on Wall Street. In particular, the strength of the program is that it concentrates on how analytical work is actually conducted in real life rather than the academic approach of some other competitors.
This course provides an in-depth introduction to credit derivatives modeling. Techniques for calibrating the LIBOR curve are introduced. Alternative approaches to modeling default probabilities are considered, including Merton’s model, reduced-form models and the hazard rate model. The basic properties of credit derivatives are covered in detail, along with pricing models. Strategies for hedging credit risk with these derivatives are discussed in detail. Correlation products are covered, including Collateralized Debt Obligations (CDOs) and single-tranche CDOs. These are priced with Monte Carlo simulation while hedging strategies are developed. All models are implemented in Excel/VBA.
This course provides a detailed overview of volatility and correlation models. First, estimate volatility from historical data then, implied volatility is defined and derived from option prices using root-finding methods. Dive deeper into the term structure of volatilities and volatility surface are analyzed in great detail. Learn how to price exotic options and the corresponding Greeks from the volatility surface. An overview of hedging and trading strategies using volatility derivatives is given; these include VIX options and futures and OTC derivatives, such as variance and volatility swaps. Several techniques for estimating correlation are covered, along with an overview of correlation derivatives. Trading and hedging strategies with correlation products are explored in detail. All models are implemented using Excel and the Visual Basic for Applications (VBA) programming language.
This course is designed to provide an intensive introduction to fixed income markets and interest rate derivatives. The course presents several alternative measures of interest rates: yield to maturity, spot rates, forward rates and discount factors; techniques for pricing bonds with these measures are covered. The measurement and management of interest rate risk is then explored in depth.
This course provides an analysis of the term structure of interest rates and interest rate derivatives pricing models. Several different types of interest rate derivatives are covered, including interest rate futures and forwards, interest rate swaps and interest rate options. The uses of these derivatives for hedging and trading purposes is explored in depth. Black’s model is applied to the pricing of European interest rate options. Equilibrium models of the term structure of interest rates are introduced and implemented in Excel. These models are used to price zero-coupon bonds and coupon bonds.
The drawbacks of equilibrium term structure models are considered. No-arbitrage models of the term structure are explored in depth, including the lognormal model, Black-Derman-Toy (BDT) and Hull-White. The comparative strengths and weaknesses of these models are considered. The BDT model is implemented in VBA as a binomial interest rate tree. The model is then used to price European, American and Exotic interest rate options.
The course presents an overview of exchange rates, foreign exchange risk and strategies for pricing and hedging with foreign exchange derivatives. The basic features of the foreign exchange markets are introduced, along with several international parity conditions. The key properties of foreign exchanges forwards, futures, swaps and options are covered in detail; pricing models are introduced for each type of derivative along with hedging strategies.
Several models are introduced for pricing foreign exchange options and are implemented in VBA. These models are used to compute the Greeks and implement sophisticated hedging strategies. The properties of exotic foreign exchange options are covered; these are priced with stochastic volatility option pricing models.
This course is an intensive introduction to option trading strategies and the Greeks. Several examples of spreads and combinations are covered in detail; strategies that combine options with other types of assets are also explained in depth. These include covered calls, protective puts and collars. In addition to these strategies, the use of options to synthetically replicate other types of positions is also explored.
Several option risk measures, known as the Greeks, are covered in detail: delta, gamma, theta, vega and rho. The properties of the Greeks are analyzed, while models for computing the Greeks are derived from the Black-Scholes model using Excel. The importance of the Greeks in trading strategies is shown with numerous examples.
This course introduces the Monte Carlo simulation approach to pricing derivative securities. Several different techniques for simulating random numbers are described; the risk-neutral framework for pricing derivative securities is covered in detail. The properties of Brownian Motion and Geometric Brownian Motion are explored, along with alternative stochastic processes that may be used to price derivatives. The simulation of European option prices and the Greeks is implemented in the VBA programming language.
The Longstaff-Schwartz approach to pricing American options is covered in depth. The properties of several types of exotic derivatives are explained in detail. The prices of these derivatives are obtained from Monte Carlo simulation and compared with the results obtained from closed-form models. Several techniques for reducing the computational time of Monte Carlo simulation are explored, such as low-discrepancy sequences, control variates and antithetic variables.
Economics - if not dismal, the “science” can certainly be frustrating. Ask yourself, do weak employment figures portend a decline in corporate profits and falling equity prices, or does it signal potential intervention from the central bank and rising equity prices? Exasperating, right?
The application of economic data to real world investment decisions often requires a secondary and even tertiary analysis of its meaning. Said differently, using economic data in the real world is more a “sentiment game” than a mathematical formula. What is a sentiment game? Keynes would describe it as a newspaper beauty contest, but more technically it’s a strategic interaction between multiple players seeking to ascertain not necessarily their interpretation of a given set of information, but the interpretation and reaction of the other players in the game.
This Global Macroeconomics course examines the practice of interpreting economic information in a way that is helpful to decision makers. We address key theoretical concepts including basic macroeconomics, the business and debt cycles, monetary and fiscal policy, and international trade; but also leave the ivory tower to examine actual economic releases and discuss not what “should” happen but what does or can happen.
The course is broadly divided into two sections: Core Concepts and Key Economic Indicators & Data Series. The Core Concepts section of the course covers introductory economic theories and models that are required background information for economic analysis. This is done through an explanation of content followed by a real world example taken from a leading financial news source. The second portion of the course looks at key economic data series including among others, employment figures, price levels, monetary policy measures, and business/consumer activity measures. We use recent economic data to make it more applicable to current investment decisions and avoid the obfuscation that often accompanies older data sets.
Students should walk away with a better understanding of basic economic theory, how it translates into real world application, and knowledge about the distribution of and meaning behind important economic indicators. This is perfect for investment decision makers looking to integrate economic analysis into their decision making process or more experienced “economists” looking for a review of key concepts.
This course provides an in-depth introduction to credit risk. Techniques for modeling credit transition matrices are covered in great detail, while several statistical techniques for modeling default probabilities and correlations are explored in depth. Methodologies for modeling credit portfolio risk are covered, including the asset value approach and the structural approach. Prepayment models are developed for Mortgage-Backed Securities (MBS). All models are developed in Excel/VBA.
This course provides an overview of Value at Risk (VaR) modeling for a wide array of financial assets, including stocks, bonds, forward contracts, futures contracts, swaps and options. The key statistical assumptions underlying the VaR methodology are explored; several different models for computing VaR are implemented in Excel. The Delta-Normal approach is used to compute VaR for bonds, stocks and linear derivative securities, such as forwards, futures and swaps, as well as calls and puts. The Delta-Gamma approach is introduced as an alternative to computing VaR for options; this approach can capture the non-linear behavior of an option but at the cost of greater computational complexity.
Full valuation approaches to computing VaR are covered in great detail; these have the advantage of being independent of any distributional assumptions about financial assets. These approaches include Historical Simulation, Weighted Historical Simulation and Monte Carlo Simulation. Several portfolio VaR measures are demonstrated; these are designed to measure the impact of a potential trade on portfolio VaR. These measures are known as Marginal VaR, Incremental VaR and Component VaR.
The course concludes with a discussion of the strengths and weaknesses of the VaR methodology, with a consideration of several alternative possible approaches.
This course provides an overview of portfolio modeling. The course reviews several key components of portfolio math, such as standard deviation, correlation and covariance, as well as optimization techniques. Markowitz’ formula for measuring portfolio risk is covered in detail. The equivalent matrix representation is introduced, along with Excel’s matrix algebra functions.
The Capital Asset Pricing Model (CAPM) framework is used to introduce several key concepts, such as beta and the efficient frontier. Excel Solver is used to derive the efficient frontier from a portfolio of stocks. Beta is estimated using linear regression analysis. The Capital Market Line and Security Market Line are derived, showing the relationship between risk and return in equity markets. The Sharpe Ratio is introduced as a measure of relative risk.
This course presents an overview of the Basel Accords and how they have evolved since their debut in 1988. The three-pillar structure is explained in great detail, with a focus on the measurement of capital requirements under Pillar 1. The Value at Risk methodology is covered in depth as a technique for computing market risk capital requirements. The key features of the approaches to computing credit risk capital are covered: the Standardized Approach, the Foundation Internal Ratings Based Approach and the Advanced Internal Ratings Based Approach.
The three approaches to computing operational risk capital are explored in detail: the Basic Indicator Approach, the Standardized Approach and the Advanced Measurement Approach. The new features of Basel III are explained, including changes to the measurement of Tier 1 and Tier 2 capital, updates to the calculation of credit risk capital and a more advanced approach to measuring liquidity risk.