Scenario Generation for Long-Run Interest Rate Risk Assessment

Engle, Robert F.; Roussellet, Guillaume; Siriwardane, Emil

doi:10.2139/ssrn.2828236

Cited by 3 publications

(10 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This modeling approach should readily accommodate the balance between stability of a key regulatory parameter (i.e., the UFR) and sensitivity to nearer-term economic and financial developments. Still, we acknowledge that there are other methods for modeling, projecting, and assessing interest rate risk in the long run; see Engle et al (2017) as well as the Smith-Wilson modeling approach adapted by EIOPA and reviewed by Gourieroux and Monfort (2015). We leave a comparison with these alternatives for future research.…”

Section: Resultsmentioning

confidence: 99%

“…Accurate models of yield curve forecasts are valuable, but accurate risk measurement is also important for managing these positions. As clearly noted by Engle et al (2017), mean or median forecasts are not the only objects of interest; measures of extreme outcomes-such as value-at-risk (VaR) estimates that gauge the extent of possible tail outcomes-are also of importance to portfolio managers.…”

Section: Motivationmentioning

confidence: 99%

“…Although the distribution of the generated scenarios could be evaluated in a more formal and comprehensive way as in Engle et al (2017), the key takeaway for the analysis in this paper is that, thanks to the estimated high persistence of the level factor, the DNS model is able to generate quite a wide range of future interest rate environments despite its Gaussian dynamics. Thus, it is not clear that stochastic yield volatility needs to be incorporated into the model to achieve simulated outcomes that are adequately stressful with a reasonable likelihood as also noted by .…”

Section: Interest Rate Scenarios Using the Us Treasury Datamentioning

confidence: 99%

See 2 more Smart Citations

Extrapolating Long-Maturity Bond Yields for Financial Risk Measurement

Christensen¹,

López²,

Mussche³

2019

ERWP

View full text Add to dashboard Cite

Insurance companies and pension funds have liabilities far into the future and typically well beyond the longest maturity bonds trading in fixed-income markets. Such longlived liabilities still need to be discounted, and yield curve extrapolations based on the information in observed yields can be used. We use dynamic Nelson-Siegel (DNS) yield curve models for extrapolating risk-free yield curves for Switzerland, Canada, France, and the U.S. We find slight biases in extrapolated long bond yields of a few basis points. In addition, the DNS model allows the generation of useful financial risk metrics, such as ranges of possible yield outcomes over projection horizons commonly used for stress-testing purposes. Therefore, we recommend using DNS models as a simple tool for generating extrapolated yields for long-term interest rate risk management.JEL Classification: E43, E47, G12, G22, G28

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Interest Rate Scenarios Using the Us Treasury Datamentioning

confidence: 99%

See 1 more Smart Citation

Extrapolating Long-Maturity Bond Yields for Financial Risk Measurement

Christensen¹,

López²,

Mussche³

2019

ERWP

View full text Add to dashboard Cite

show abstract

“…Unlike most ESGs proposed in the existing actuarial literature, we rely on a regime variable-monetary policy-to give our model more structure. Indeed, monetary policy impacts multiple facets of the economy such as the inflation, the short rate and stock returns (see, e.g., Engle et al, 2017, for the impact on the short rate dynamics and Ioannidis and Kontonikas, 2008, for the effects on stock returns). We rely on observable instead of latent regimes because they allow for a better interpretation of our model variables and are easier to deal with from an estimation perspective.…”

Section: Professionmentioning

confidence: 99%

On Complex Economic Scenario Generators: Is Less More?

Bégin¹

2021

ASTIN Bull.

View full text Add to dashboard Cite

This article proposes a complex economic scenario generator that nests versions of well-known actuarial frameworks. The generator estimation relies on the Bayesian paradigm and accounts for both model and parameter uncertainty via Markov chain Monte Carlo methods. So, to the question is less more?, we answer maybe, but it depends on your criteria. From an in-sample fit perspective, on the one hand, a complex economic scenario generator seems better. From the conservatism, forecasting and coverage perspectives, on the other hand, the situation is less clear: having more complex models for the short rate, term structure and stock index returns is clearly beneficial. However, that is not the case for inflation and the dividend yield.

show abstract

“…A large part of finance literature and applications by practitioners, regarding the term structure of non-defaultable securities, is concerned with using available security prices to estimate the fair market prices of other non-observable securities: "this is important because fixed-income securities and their derivatives trade only occasionally, and so must be priced based on other securities that do trade" (Engle, Roussellet, & Siriwardane, 2017).…”

Section: Introductionmentioning

confidence: 99%

A Segmented and Observable Yield Curve for Colombia

Castro-Iragorri

Peña

Rodríguez

2021

Journal of Central Banking Theory and Practice

View full text Add to dashboard Cite

Following (Almeida, Ardison, Kubudi, Simonsen, & Vicente, 2018) we implement a segmented three factor Nelson-Siegel model for the yield curve using daily observable bond prices and short term interbank rates for Colombia. The flexible estimation for each segment (short, medium, and long) provides an improvement over the classical Nelson-Siegel approach in particular in terms of in-sample and out-of-sample forecasting performance. A segmented term structure model based on observable bond prices provides a tool closer to the needs of practitioners in terms of reproducing the market quotes and allowing for independent local shocks in the different segments of the curve.

show abstract

Scenario Generation for Long-Run Interest Rate Risk Assessment

Cited by 3 publications

References 1 publication

Extrapolating Long-Maturity Bond Yields for Financial Risk Measurement

Extrapolating Long-Maturity Bond Yields for Financial Risk Measurement

On Complex Economic Scenario Generators: Is Less More?

A Segmented and Observable Yield Curve for Colombia

Contact Info

Product

Resources

About