Equity Tail Risk in the Treasury Bond Market
Mirco Rubin (EDHEC) and Dario Ruzzi (Bank of Italy)
December 23, 2020
This paper quantifies the effects of equity tail risk on the US government bond market. We estimate equity tail risk as the option-implied stock market volatility that stems from large negative jumps as in Bollerslev, Todorov and Xu (2015), and assess its value in reduced-form predictive regressions for Treasury returns and an affine term structure model for interest rates. We document that the left tail volatility of the stock market significantly predicts one-month-ahead excess returns on Treasuries both in- and out-of-sample. The incremental value of employing equity tail risk as a return forecasting factor can be of economic importance for a mean-variance investor trading bonds. The estimated term structure model shows that equity tail risk is priced in the US government bond market. Consistent with the theory of flight-to-safety, we find that Treasury prices increase and funds flow from equities into bonds when the perception of tail risk is higher. Our results concerning the predictive power and pricing of equity tail risk extend to major government bond markets in Europe.
Optimize your Investment Portfolio in Bearish Markets
Mazin A. M. Al Janabi (EGADE Business School)
May 7, 2021
Since the 2008-2009 global financial crisis, VaR (Value-at-Risk) techniques have become critical tools for monitoring and predicting the market risk and liquidity of financial assets. These financial risk modeling techniques, which have been recognized by the Bank for International Settlements (BIS) or the Basel Committee on capital adequacy and bank regulations, measure and prevent any potential losses that arise, not only from securities’ price changes and the interdependence between the different types of assets (stocks, currencies, interest rates or commodities), but also from their negative tail co-movements in bearish market conditions. In the event of a financial crisis or market downturn, adequate liquidity risk modeling is advisable. In fact, the main advantage of VaR models is their focus on downside risk (i.e., the impact of the results of negative tails) and their direct interpretation in monetary terms. Nevertheless, particularly in times of financial turbulence, traditional VaR models do not properly consider nonlinear dependence between portfolio assets and become inefficient in illiquid market scenarios. Despite the advances in measurement models, obtaining precise market liquidity risk estimations and applying them to optimize portfolios continues to be a challenge for financial institutions.
Modeling and Forecasting Macroeconomic Downside Risk
Davide Delle Monache (Bank of Italy), et al.
25 May 2021
We document a substantial increase in downside risk to US economic growth over the last 30 years. By modelling secular trends and cyclical changes of the predictive density of GDP growth, we find an accelerating decline in the skewness of the conditional distributions, with significant, procyclical variations. Decreasing trend-skewness, which turned negative in the aftermath of the Great Recession, is associated with the long-run growth slowdown started in the early 2000s. Short-run skewness fluctuations imply negatively skewed predictive densities ahead of and during recessions, often anticipated by deteriorating financial conditions, while positively skewed distributions characterize expansions. The model delivers competitive out-of-sample (point, density and tail) forecasts, improving upon standard benchmarks, due to the strong signals of increasing downside risk provided by current financial conditions.
Portfolio Optimization Constrained by Performance Attribution
Yuan Hu and W. Brent Lindquist (Texas Tech University)
March 9, 2021
This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize expected tail loss and investigate both asset allocation (AA) and the selection effect (SE) as hard constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index; the benchmark is an equi-weighted portfolio of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures maximum drawdown, Sharpe ratio, and Rachev ratio. The results suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE, with SE constraints producing the larger performance enhancement.
Managing Tail Risk Part I: Option-Based Hedging
Emlyn Flint (Peresec), et al.
April 1, 2021
There is nothing like a market crash to focus the mind on the importance of risk management and, more specifically, tail risk management. Because tail events are generally systemic in nature and are characterised by elevated correlations and liquidity squeezes, effective tail risk management is not just a diversification exercise but rather requires explicit and specialised strategies. The natural question that arises then is how do you create an optimal tail risk management strategy? Most studies identify four categories of tail risk management strategy: option-based hedging, asset allocation, dynamic trading and defensive equity. Within each category, we then find a number of strategies available to investors for managing tail risk. Unfortunately, it remains an open question as to which strategy works best for a given investment scenario, or if there even exists a universally optimal strategy to begin with. We attempt to answer this question by analysing a range of commonly used tail risk management strategies in a South African market setting. However, given the breadth of the four available strategy categories, we split our research on this topic into two parts. In this Part I, we firstly examine and quantify the tail risk inherent in South African markets. This is done through a review the long-term history of equity and bond market drawdowns. Thereafter, we discuss nine core principles that are applicable to all candidate strategies and that define good tail risk management. Finally, we provide a comprehensive analysis of option-based tail hedging strategies. The concepts of defensive, offensive, active and indirect tail hedging are discussed at length and examples of each are implemented on historical market data.
A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting
Chao Wang and Richard Gerlach (University of Sydney)
June 1, 2021
In this paper, an innovative threshold measurement equation is proposed to be employed in a Realized-GARCH framework. The proposed framework employs a nonlinear threshold regression specification to consider the leverage effect and model the contemporaneous dependence between the observed realized measures and hidden volatility. A Bayesian Markov Chain Monte Carlo method is adapted and employed for the model estimation and forecasting, with its validity assessed via a simulation study. The usefulness of the proposed measurement equation in a Realized-GARCH model has been evaluated via a comprehensive empirical study, by forecasting the 1% and 2.5% Value-at-Risk and Expected Shortfall on six market indices. The proposed framework is shown to be capable of producing competitive tail risk forecasting results, compared to the original Realized-GARCH. Especially, the proposed model is favoured during the high volatility 2008 Global Financial Crisis period.
Quantile Risk-Return Trade-Off
Nektarios Aslanidis (Universitat Rovira Virgili)
May 12, 2021
We investigate the risk-return trade-off on the US and European stock markets. We investigate the non-linear risk-return trade-off with a special eye to the tails of the stock returns using quantile regressions. We first consider the US stock market portfolio. We find that the risk-return trade-off is significantly positive at the upper tail (0.9 quantile), where the upper tail is large positive excess returns. The positive trade-off is as expected from asset pricing models. For the lower tail (0.1 quantile), that is for large negative stock returns, the trade-off is significantly negative. And for the median (0.5 quantile), the risk-return trade-off is insignificant. These results are recovered for the US industry portfolios as well as for Eurozone stock market portfolios.
Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno