1 / 21

GDP as determinants of public spending in research and developments (Hammadou et al., 2014)

"The impact of external market conditions on R&D valuation“ (by Lambert, Moreno and Platania) Discussed by Diego Ronchetti (University of Groningen). The literature has stressed (i) the importance of economic environment for R&D spending.

Download Presentation

GDP as determinants of public spending in research and developments (Hammadou et al., 2014)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. "The impact of external market conditions on R&D valuation“(by Lambert, Moreno and Platania)Discussed byDiego Ronchetti(University of Groningen)

  2. The literature has stressed (i) the importance of economic environment for R&D spending • GDP as determinants of public spending in research and developments (Hammadou et al., 2014) • Public funding on innovative projects (Lanahan and Feldman, 2017) • Productivity growth and R&D expenses (e.g. Kancs an Siliverstovs, 2016)

  3. and (ii) how and when to launch a project (entirely or in parts) • Staged investments (Majd and Pindyck, 1987) • Optimal timing of investment (McDonald and Siegel, 1986; Posner and Zuckerman, 1990) • Project’s market value (Berk et al. 2004) • Technical risk (Pindyck, 1993) • Cost of completion and expenditures (McDonald and Siegel, 1986; Pindyck, 1993) This paper proposes a project valuation method that accounts for changing economic environment

  4. R&D project consisting of 2 phases R&D phase [0: tML) Idiosyncratic (technical, technological) risk (Tech risk) tML= Market launch date Market phase [tML:T] Systematic (economic) risk (Econ risk)

  5. R&D phase [Tech risk] Poisson number of “test failures” K during development process If K > 0, null expected project value -> Tech risk premium Cash-flow discounting for failure risk

  6. Market phase [Econ risk] f(t) = Fourier series Example: Φ = Econ risk-state vector (Φ = Φ[j] when f(t) has j terms) (discrete state vector) with PMF Examples in the paper: Business cycle indicators, VIX pj fixed to individual (or analysts’) expectation -> Econ risk premium Cash-flow discounting for econ risk

  7. Market phase [Econ risk] f(t) = Fourier series representing econ risk Ct = net cash-flow stream after project approval {Wt} = Brownian motion as idiosyncratic risk-state variable process It = investment structure during R&D phase

  8. Market phase [Econ risk] The project can be abandoned at each time during market phase: Abandon option value obtained by backward recursion: Both project value and embedded abandon option value depend on tML -> other things being equal, lower project value and higher option value if project launched just before a recession

  9. Few points about the use of this valuation method for a R&D project

  10. Econ risk: how do we determine its “true” nature? In the paper the “econ risk” for the net cash-flow stream after project approval derives from “external forces” Examples: Business Cycle [BC] indicators, VIX The selection of the “relevant external force” matters. Does the net cash-flow stream of the project follow more closely a financial cycle or a BC? They may differ

  11. Econ risk: f(t) specification The duration of BCs varies. For example, in US: (data: National Bureau of Economic Research) Cyclical period of 5.35 years How long will the next cycle last? What about using "prediction sets“? July 1990 – March 2001

  12. Other things being equal,the optimal project launch is just before a boom However, delaying project launch implies • Foregone patent protection • Risk of the introduction of a substitute good • Risk that a competitor conquers an important market share • Risk that the product is bypassed by a new product development -> Project value is affected by both market and competitors conditions What about discounting for these risks?

  13. Diffusive process for the cash-flow What about extreme net cash-flow shocks? What about jumps?

  14. "Can a Mimicking Synthetic Equity Structure Dominate the Risk Return Profile of Corporate Bonds?“(by Nouvellon and Pirotte)Discussed byDiego Ronchetti(University of Groningen)

  15. This paper compares returns for - Corporate Bond Index (CBI)- Synthetic Equity Structure (SES) CBI • Weighted average of corporate bond prices • Constituents: bonds with a precise credit rating (AAA, AA, A, BBB, BB) • Investment: by entering a fund (aiming at) tracking the CBI • Contingent Claim Analysis: CBI as an option on the value A of the total assets of a representative firm with a precise credit rating -> Default occurs when A crosses a default barrier • Data: bond spreads from Merrill Lynch indexes -> Spreads implied probability of default (PD)

  16. This paper compares returns for - Corporate Bond Index (CBI)- Synthetic Equity Structure (SES) SES replicating the CBI • Strategy to get CBI exposure without holding CBI itself • Constituents: equity index derivatives • Investment: by entering pooled fund structures holding the derivatives • SES as an option on an equity index with value S -> Structure stops occurs when S crosses a barrier • The index is the EURO STOXX 50 -> Model-implied PD

  17. Results of the paper on- Corporate Bond Index (CBI)- Synthetic Equity Structure (SES) The authors find the for highly rated bonds we can have • Lower SES PD w.r.t. CBI • Higher SES yield w.r.t. CBI The authors conclude that for highly rated bonds (and corresponding low PD) SES has a better “spread/default” profile than CBI

  18. Differences between CBI and SES • The SES barrier is not continuous (the index level is indeed observed just at few dates): the index level could go below and above the barrier level at intermediate times • Default barrier for CBI is continuous. The debt structure/repayment schedule for the chosen “bond index” may be continuous -> SES is safer as its PD is lower To keep the data: Why don’t consider the equity index values below the SES barrier as an “absorbing state” (looking back at each observation time)? To keep the SES: Why don’t take a single firm with (a) a precise repayment schedule matching the SES observation dates, and (b) without any credit need between two following observation dates?

  19. Differences between CBI and SES • The CBI recovery amount is often paid years after default • The SES recovery amount is paid instantaneously -> SES pays more (that is, earlier) (Other things being equal) Why don’t discount the cash-flows by an "expected elapsed time" between default and recovery amount payment date?

  20. Differences between CBI and SES • The SES’s underlying is an index build from equities of firms with distinct credit ratings • CBI is an index build from bonds with a chosen credit rating Isn’t “unfair” to compare SES with CBI from highly rated bonds? Why don’t create an index with equities of firms with a given credit rating?

  21. Differences between CBI and SES What about the “average” liquidity for • a bond of a given credit rating (and then the CBI) • and that one of the assets necessary to implement the SES? -> No liquidity premium is now considered Why don’t include proxies for the liquidity of the assets when you discount the payoffs? Why don’t try to explain the “anomalous” spread/default SES profile by liquidity proxies?

More Related