Financial asset returns, direction-of-change forecasting, and volatility dynamics

We consider three sets of phenomena that feature prominently - and separately - in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis

How Relevant is Volatility Forecasting for Financial Risk Management?

It depends. If volatility fluctuates in a forecastable way, then volatility forecasts are useful for risk management; hence the interest in volatility forecastability in the risk management literature. Volatility forecastability, however, varies with horizon, and different horizons are relevant in different applications. Existing assessments are plagued by the fact that they are joint assessments of volatility forecastability and an assumed model, and the results vary not only with the horizon, but also with the model. To address this problem, we develop a model-free procedure for measuring volatility forecastability across horizons. Perhaps surprisingly, we find that volatility forecastability decays quickly with horizon. Volatility forecastability, although clearly of relevance for risk management at the very short horizons relevant for, say, trading desk management, may not be important for risk management more generally.e conclude in Section VI by discussing some limitations of our analysis, and offer some recommendations for implementation.

Horizon Problems and Extreme Events in Financial Risk Management

Central to the ongoing development of practical financial risk management methods is recognition of the fact that asset return volatility is often forecastable. Although there is no single horizon relevant for financial risk management, most would agree that in many situations the relevant horizon is quite long, certainly longer than a few days. This fact creates some tension, because although short-horizon asset return volatility is clearly highly forecastable, much less is known about long-horizon volatility forecastability, which we examine in this paper. We begin by assessing some common model-based methods for converting short-horizon volatility into long-horizon volatility; we argue that such conversions are problematic even when done properly. Hence we develop and apply a new model-free methodology to assess the forecastability of volatility across horizons and find, surprisingly, that forecastability decays rapidly as the horizon lengthens. We conclude that for managing risk at horizons longer than a few weeks, attention given to direct estimation of extreme event probabilities may be more productive than attention given to modeling volatility dynamics, and we proceed to assess the potential of extreme value theory for estimating extreme event probabilities.

Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

We consider three sets of phenomena that feature prominently and separately in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis.

Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

We consider three sets of phenomena that feature prominently - and separately - in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis.Conditional Mean Dependence, Conditional Volatility Dependence, Sign Dependence, VIX

Horizon problems and extreme events in financial risk management

This paper was presented at the conference "Financial services at the crossroads: capital regulation in the twenty-first century" as part of session 3, "Issues in value-at-risk modeling and evaluation." The conference, held at the Federal Reserve Bank of New York on February 26-27, 1998, was designed to encourage a consensus between the public and private sectors on an agenda for capital regulation in the new century.Risk ; Forecasting

MACROECONOMIC CAUSES OF VOLATILITY IN THE EURO AREA’S AGGREGATE STOCK RETURN

The purpose of this paper is to determine whether macroeconomic and financial variables Granger cause time varying volatility in aggregate stock return of the Euro Area. Using the daily data from 2005-2013 monthly realized volatility is calculated as the sum of squared daily returns over the respective month for the Euro Stoxx, the Euro Stoxx 50 and the Euro Stoxx Optimized Banks indices. These three indices area used as proxies for the aggregate market, blue chip companies and banking industry, respectively, in the Euro area. The entire sample period is further divided into three sub-sample periods: pre-crash period is from January 2005 to October, 2007, market crash period is from November, 2007 to February, 2009 and post-crash period is from March, 2009 to December, 2013. The sample periods’ selection is motivated to capture the effects of business cycle and the recent financial crisis of 2007-2009. Nine macroeconomic and financial variables used in this paper are: bank leverage, consumption growth, credit growth, commercial paper to treasury spread (CP), expected GDP growth, GDP growth, term spread, volatility of inflation and industrial production. The In-sample analysis shows that the forecastability of macro variables varies through time and business cycle. Their predictability is higher during the crisis of 2007-2009 and when the bull or the bear market condition is considered in isolation. The pattern of Granger casualty during the bull market differs from that of the bear market. The blue chip index is found to be more sensitive to the changes in macro variables than the broad market index. However, the set of macro variables affecting the banking sector and their predictability pattern are different from the other two indices those represent the overall market. The most successful out-of-sample forecasting approaches involve simple combinations of macro variables, namely median and trimmed mean of individual forecasting variablesfi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

What is wrong with the quantitative standards for market risk?

The purpose of this paper is to evaluate the quantitative standards laid down under the second Basel Accords for the implementation of internal market risk models by banks. The paper surveys available research to evaluate the standards. The standards don’t prescribe a VaR method despite evidence that volatility of financial returns is conditional and financial returns are fat tailed. The requirement of a minimum historical period also runs contrary to the finding that volatility is time varying and clustered resulting in banks being able to use weighting schemes conservatively only. The minimum horizon of ten days requires use of a scaling rule that is not accurate. The 99% confidence level requirement increases the inaccuracy when using a normal assumption on fat tailed data. The minimum updation period and minimum historical period requirements effectively smooth the market risk charge over and above the smoothing by the requirement of averaging VaR resulting in unresponsive market risk charges. The regulatory back testing framework is based on unconditional coverage and doesnot penalize clustered VaR exceptions. Key Words: Basel accord, GARCH, Historical simulation, Market risk, Value-at-risk, Volatility, Conditional volatility, Back testing

Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models

We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular emphasis of this paper is on assessing the performance of long memory time series models in comparison to their short-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions of up to 100 days ahead. In most respects, the long memory models (ARFIMA, FIGARCH and the recently introduced multifractal models) dominate over GARCH and ARMA models. However, while FIGARCH and ARFIMA also have a number of cases with dramatic failures of their forecasts, the multifractal model does not suffer from this shortcoming and its performance practically always improves upon the na?ve forecast provided by historical volatility. As a somewhat surprising result, we also find that, for FIGARCH and ARFIMA models, pooled estimates (i.e. averages of parameter estimates from a sample of time series) give much better results than individually estimated models. --Forecasting,Long memory models,Volume,Volatility