Long-term scenario modeling for investors: a practitioners - PowerPoint PPT Presentation

Long-term scenario modeling for investors: a practitioner’s perspective Hens Steehouwer, teehouwer, Nove vember mber 3 2020

Topics 1. Models 2. Scenario approach 3. Horizons and frequencies 4. The frequency domain 5. Long-term mean assumptions (poll 1) 6. Climate risk scenarios (poll 2) 7. Summary and Q&A 2

INQUIRE EUROPE 30th anniversary Some personal memories Guus Boender Program Committee 2008 2008 Jens Langewand Michael Damm 3

Topics 1. Models 2. Scenario approach 3. Horizons and frequencies 4. The frequency domain 5. Long-term mean assumptions (poll 1) 6. Climate risk scenarios (poll 2) 7. Summary and Q&A 4

INQU Investment RE Risk – return – horizon – inflation – cash flows – OBJECTIVES liquidity – liabilities – solvency – ESG – … Financial – economic – monetary policy – Philips curve – demographics – r * / low rates – UNCERTAINTY COVID-19 – climate – … Asset allocation – rebalancing – matching – DECISIONS overlays – options – factors – ESG – … 5

IN QUantitative IRE  To answer complex questions, such as how to control the spread of a virus, we use models of reality to perform analysis that support us in making the right decisions for the future. Stevens, H. (2020), “ Why outbreaks like coronavirus spread exponentially, and how to flatten the curve ”, The Washington Post, March 14 2020. https://www.washingtonpost.com/graphics/2020/world/corona-simulator/  Also making investment decisions to achieve objectives in the future is complex and relies heavily on models.  Models help us to quantify, analyze and compare the potential consequences of various decisions under different sets of assumptions. In doing so we learn in a structured way, which decisions work well and which decisions might not be effective. https://www.ortecfinance.com/en/insights/whitepaper- 6 and-report/financial-models-and-the-corona-crisis

Dices and planets  All economic and financial models are based on assumptions and cannot match the level of accuracy of models from the natural sciences  Essential to use models in a sensible way: validate assumptions, include parameter uncertainty, avoid sampling noise, perform sensitivity analysis, combine strengths of different approaches etc. 7

Realistic assumptions Realistic ≠ Prudent Realistic ≠ Consensus Realistic ≠ Intuitive Realistic ≠ Rational Realistic ≠ Simple Diebold, F.X. and C. Li (2006), “Forecasting the Term Structure of Government Bond Yields”, Journal of Econometrics , 130, 337-364. 8

Topics 1. Models 2. Scenario approach 3. Horizons and frequencies 4. The frequency domain 5. Long-term mean assumptions (poll 1) 6. Climate risk scenarios (poll 2) 7. Summary and Q&A 9

Scenario approach “ A scenario is a possible evolution of the future consistent with a clear set of assumptions ” Bunn and Salo (1993) “ A scenario is a description of a possible future state of an organization’s environment considering possible developments of relevant interdependent factors in the environment ” Brauers and Weber (1988)  Flexibility  realistic  Understanding  decisions (!) 10

The academic version “ The dynamic behavior of a pension plan is clearly dominated by rules and methodology which are discontinuous and non-linear function of its financial condition. The task of developing a closed-form solution to evaluate the potential state of a pension plan following a series of stochastic investment and inflation experiences would be extremely difficult, if not impossible. To date, the only approach that has proven feasible is the application of Monte Carlo Simulation, wherein an investment and inflation scenario is generated by random draws based on the expected probability distribution of year to year investment and inflation behavior. In order to develop an accurate assessment of the range of potential uncertainties, it is necessary to repeat this simulation process by generating dozens or hundreds possible scenarios, consistent with statistical expectations. ” Kingsland , L. (1982), “Combining financial and actuarial risk: Simulation analysis. Projecting the financial condition of a pension plan using simulation analysis”, Journal of Finance , vol. 37 11

From scenarios to decision making Example: Insurance company under Solvency II Probability available capital < required capital 1. More efficient strategies 2. More effective strategies 2.5 1. Asset Allocation only 3. Strategy decision 20 10% 0% 5% 5 2. Duration only 7.5 12.5 10 3. Asset Allocation × Duration 10 Expected market value funding ratio 12

Approaches for constructing scenarios  Historical averages: Use historical averages as expectations for the future 1. Future extremely unlikely to unfold in this way 2. Suggests level of certainty that is not there in reality  Historical simulation: Simple way of dealing with uncertain future but also drawbacks: 1. History only one very specific realization of all that might have happened 2. Ignores economic and financial market conditions that prevail today  By hand: Create scenarios by formulating what might happen in the future based on specific narratives of possible events.  Pros: simple, out-of-the-box, narratives, risk awareness, stress testing  Cons: incomplete picture of uncertainties, risk-return tradeoff 13

Model based approach ASSUME  MODEL  CALIBRATE  GENERATE and VALIDATE Jun 2016 – Sep 2020 Jun 2016 – Sep 2020 70% Model performance 60% Interval Observations 50% Expected Realized 1% probability 40% 95 - 100% 5% 4% 75 - 95% 20% 15% 30% 25 - 75% 50% 62% 20% 5 - 25% 20% 15% 4% 0 - 5% 5% 3% 10% Observations: 6440 0% Out-of-sample forecasting performance of official monthly versions of Ortec 5% 20% 50% 20% 5% 20% Finance scenarios since launch per end of June 2016 until September 2020 based on 1, 3 and 12 month forecasts of financial market variables such as 3M and 10Y Expected Realized government bond yields, 3M and 10Y IG and HY spreads, equity total return 30/09/2020 indices, commodities and exchange rates 50% Likelihoods  Risk-return tradeoffs 20% 4% 1% Monthly asset only portfolio value and risk according to a typical investment strategy of a European pension fund 14

Topics 1. Models 2. Scenario approach 3. Horizons and frequencies 4. The frequency domain 5. Long-term mean assumptions (poll 1) 6. Climate risk scenarios (poll 2) 7. Summary and Q&A 15

Horizon and frequencies 2: Correlations of US cumulative returns across the investment horizon (in years) 1 1: Tail correlations in the left part of return distributions 1 1.0 0.7 0.9 0.6 0.8 0.5 Correlation Correlation 0.7 0.4 0.6 0.3 0.5 0.2 0.4 0.1 0.3 0.2 0.0 0.00 0.10 0.20 0.30 0.40 0.50 0 5 10 15 20 25 30 Left tail of the distribution Horizon in years EQ US - EUR - monthly EQ US - EUR - annual CPI - Houses CPI - Equities EQ US - HY US - monthly EQ US - GSCI - monthly Equities - Houses Equities - Interest rates 1 Normal implied correlations based on Ortec Finance Economic Scenario Generator (ESG): correlation of a 1 Based on Ortec Finance Economic Scenario Generator (ESG): correlations between cumulative (annualized) US bivariate Normal distribution which corresponds to the measured Tail Dependence Coefficient (TDC) per quantile CPI, geometric US equity returns, geometric US house price returns and the 10-year US Government yield, (threshold) in the left part of the distributions, where the TDC corresponds to the probability that one margin calculated on investment horizons of 1 to 30 years. exceeds a threshold under the condition that the other margin exceeds a threshold. See e.g. Frahm et al. (2005). 16

How trend – cycle decompositions can help – 1: Trend – cycle decomposition of long-term UK government bond yield time-series 18% 18% 16% 16% Secular stagnation 14% 14% Savings glut 12% 12% Debt super-cycle 10% 10% Climate ... 8% 8% 6% 6% 4% 4% 2% 2% Long-term UK government bond yield Trend 0% 0% 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 3% Business cycle 3% Monetary policy Momentum Fiscal policy 2% 2% Return reversal Covid- 19 … Alfa … 1% 1% 0% 0% -1% -1% -2% -2% Cycle Irregular -3% -3% 1970 1980 1990 2000 2010 2020 1990 2000 2010 2020 17 Sources: Bank of England, Bloomberg and Ortec Finance

Returns of different frequencies Trend – cycle decomposition of US equities total return index – 9.0 1.5 8.0 1.0 7.0 6.0 0.5 5.0 0.0 4.0 3.0 -0.5 2.0 -1.0 1.0 10Y return (log) Trend Index (log) 0.0 -1.5 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 0.8 0.3 0.6 0.2 0.4 0.1 0.2 0.0 0 -0.2 -0.1 -0.4 -0.2 -0.6 1M return (log) Irregular 1Y return (log) Cycle -0.8 -0.3 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 18 Sources: Goetzmann et al. (2001), Shiller, Bloomberg and Ortec Finance

How trend – cycle decompositions can help Historical time series Trend – cycle decomposition Trend model Cycle model Irregular model Scenarios/Confidence bands/Forecasts  Consistent, efficient and realistic scenario modeling 19