Forecasting Financial Risk Indicators

by Don Alexander, MBA

Associate, RSD Solutions Inc.

www.RSDsolutions.com

info@RSDsolutions.com

 

Financial volatility is a crucial input for risk management, asset pricing and portfolio management and has important economic repercussions as evidenced in the recent financial crisis.  It is important to understand from a risk management standpoint the key drivers of volatility.

 

A recent working paper (March 2012) at the BIS, "A Comprehensive Look at Financial Volatility Prediction by Economic Variables", by Charlotte Christiansen, Malik Schmeling & Andreas Schrimpf looks at the use economic and financial variables as a predictor of volatility. Christiansen et al investigate if asset return volatility is predictable by macroeconomic and financial variables. The main goal of the study is to shed light on the economic sources of financial volatility. 

 

Their approach is distinct due to its comprehensiveness and extends recent research in several directions: First, they employ a long-term sample period and use forecast methodology to handle a large set of potential predictors, Second, they include multiple asset classes (equities, foreign exchange, bonds, and commodities), Third, they employ a comprehensive set of predictive variables which goes beyond existing studies in the literature on the economic drivers of volatility, and Fourth, the authors use comprehensive model selection and forecast combination procedures to assess whether economic variables are useful and robust predictors of financial volatility.  

 

The authors find that there is significant information contained in economic variables that helps in predicting future volatility for all four asset classes under study. Importantly, this predictive content by economic variables goes beyond the information contained in the history of the time series of realized volatility. 

 

The results are also supportive of financial volatility predictability by macroeconomic and financial variables in a realistic out-of-sample setting. The variables that are the most robust predictors of volatility are those that have sensible economic interpretations. In particular, variables that proxy for credit risk and funding liquidity (illiquidity) consistently show up significant forecast variable of volatility across several asset classes. Variables capturing time-varying risk premia (such as valuation ratios for equities, or interest rate differentials in foreign exchange) also perform well as significant indicators of volatility. 

 

In contrast to these financial predictors, variables that proxy for macroeconomic conditions, are much less informative about future volatility. Thus, the results suggest that channels that emphasize the effects of leverage, credit risk and funding illiquidity as well as time-variation of risk premia are the most promising candidates for understanding the economic drivers of financial volatility.

 

A key requirement for risk management is the understanding of volatility, but it also provides other information.  This may include uncovering linkages between price movements in financial markets and underlying risk factors or business cycle variables.  There is growing evidence showing that risks associated with volatility are priced into most asset and derivative markets. 

 

For more on this, follow the link:  www.bis.org/index.htm?ht=Research

 

 

Credit Risk and Capital: A Basic Primer - Executive Summary

This is a summary of an article by David Finnie of RSD Solutions LLC. The full article can be found in the "Resources" section of RSD Solutions' web site (www.RSDsolutions.com).

Building an integrated risk management capability has been of paramount interest for many financial institutions over the past decade or so. Increasingly, non-financial firms have begun to see the value of this framework. Financial institutions are looking for an ability to capture all of their risks in a fungible, useful format. A format that allows them to understand, as fully as possible, their total risk profile, their risk composition and their risk return performance. Non-financial firms can use the same framework to acchttp://www2.blogger.com/img/gl.link.gifomplish the same understanding and, importantly, to determine how much capital is required to maintain debt ratings and shareholder expectations and to allow the successful execution of the firm’s organizational strategies.

The framework to accomplish integrated risk management and risk-based capital management is an economic capital framework. The Credit Risk and Capital Basic Primer, available on the RSD Solutions website (), outlines the approaches and methodology to develop the capital needed to support the credit risk activities of the firm.

To build a fully developed economic capital framework, a risk capital framework capturing credit, market and operational risk is needed. For non-financial firms, this is necessary but insufficient since many of their capital needs are derived from non-risk factors. For this reason, it is also necessary to deal with infrastructure needs, business model attributes and strategic needs among others.

Credit and Counterparty Risk is the potential for loss due to the failure of a borrower, endorser, guarantor or counterparty to repay a loan or honor another predetermined financial obligation.

Credit Risk Capital is needed to provide protection for Unexpected Losses (ULs). Expected Losses, those losses that would be expected given the portfolio of obligations held by the firm, need to be built into the pricing of those obligations. A simple example – a high risk bond provides a higher interest rate than a low risk bond. Unexpected Losses are the variances around the Expected Losses. These losses are covered by the firm’s capital.

The development of credit risk capital depends on the three key concepts needed to determine Expected Loss – Exposure at Default, Probability of Default and Loss Given Default. To bring this to Unexpected Loss, the volatilities of these variables and the correlation of the assets within the credit portfolio are also needed.

Probability of Default is the critical driver of capital in this model and is expressed in the capital equation as the cumulative distribution function for a standard normal random variable (refer to The Credit Risk and Capital Basic Primer, available on the RSD Solutions website) which includes the inverse cumulative distribution function for a standard normal random variable. The complex set of equations boils down to some very simple relationships:

• Diversity matters – both in terms of the diversity within the portfolio (i.e. the direct relationship between individual obligors) as well as the relationship of the portfolio to general economic conditions.
• The loss distribution is generally characterized as “fat-tailed” (or leptokurtotic) which means that to reach a given confidence level, one must move out further along the distribution than for a normal distribution.
• The capital result is heavily dependent upon the risk of the underlying obligor or portfolio of obligors – each level of risk entails a different distribution of losses and, as the risk increases, the distribution becomes increasingly “fat-tailed” – the capital requirement increases at an increasing rate as risk deteriorates.

As the Credit Risk Capital Primer concludes, the amount of capital required to support credit operations can be managed through standard credit decision frameworks and through effective credit facility pricing.