Browsing by Author "Ronkainen, Vesa"

Sort by: Order: Results:

Now showing items 1-5 of 5
  • Ronkainen, Vesa; Koskinen, Lasse; Koskela, Laura (2008)
    In the EU the supervision of the insurance industry is expected to step into the new Solvency II framework within some years. The new framework will mean a fundamental update for both valuation and solvency requirements. Instead of just offering a standard formula for calculating the solvency capital requirement, in Solvency II insurance companies will be encouraged to develop internal models that are expected to be able to assess numerous effects which would not be easily quantified using the “one fits all” standard approach. However, to develop an internal model that will satisfy the approval criteria is a major project, during which the model builders and implementers will be faced with serious challenges.
  • Koskela, Laura; Ronkainen, Vesa; Puustelli, Anne (2008)
    Vakuutusvalvonta. Reports 1
    Raportissa tutustumme eräisiin yleisiin stokastisiin osake- ja korkomalleihin päähuomion ollessa pitkäntähtäimen simulaatioissa, jotka ovat tyypillistä mm. henki- ja eläkevakuuttamiselle. Pohdimme käytännön mallintamisen eri vaiheita Solvenssi II -projektin sisäisten mallien näkökulmasta.
  • Kaliva, Kasimir; Koskinen, Lasse; Ronkainen, Vesa (2007)
    There is a major trend in the insurance sector towards arbitrage-free valuation of insurance liabilities and assets. The assumption of no-arbitrage is fundamental in financial modelling. This paper surveys assumptions of arbitragefree modelling and studies their consequences for the use of internal model in insurance. The model uncertainty arises as a particularly severe problem under the assumption that the conditions of arbitrage-free complete market theory do not hold and all participants in the market are not fully rational. We argue that the approximation errors of these idealistic assumptions are generally larger in insurance applications than elsewhere in the financial sector. Hence, the model uncertainty plays a particularly important role in the use of internal models. This should be taken into account in the development of the models and in risk management practice. Finally, we present some known Bayesian methods that might be useful for managing the model risk.
  • Ronkainen, Vesa; Alho, Juha (Finanssivalvonta, 2009)
    Finanssivalvonta. Tutkimukset 1/2009
    In this paper we develop a model for equity returns that is aimed at long-term forecasting and risk management applications. We first analyse the yearly S&P 500 total return index data and review some common models for equity returns. Subsequently we develop a Gamma Jump Random Walk model for equity returns and estimate it through the Maximum Likelihood and Markov Chain Monte-Carlo methods. In the final section we present simulations of the model. Avainsanat/Nyckelord/Keywords: Equity return shocks, jump random walk, risk management
  • Ronkainen, Vesa (2012)
    Suomen Pankki. E 44
    1 Introduction 11 1.1 Motivation 11 1.2 Pension insurance and riskmanagement 12 1.3 Solvency II 15 1.4 Value-at-Risk (VaR) 18 1.5 Insurancemodeling 19 2 Equity index model 23 2.1 Data on equity returns 23 2.2 Model specification and preliminary estimation 29 2.3 Parameter uncertainty via Markov Chain Monte-Carlo 35 2.4 Simulation of future equity returns 38 3 Bond index model 44 3.1 Mediumtermbond index data 45 3.2 Reviewof interest ratemodeling approaches 48 3.3 Model specification and estimation 51 3.4 Parameter uncertainty 57 4 Mortality model 66 4.1 Introduction 66 4.2 Data 67 4.3 Review of the Lee-Cartermodel 69 4.4 Parameter uncertainty in the Lee-Carter model 73 4.5 Gender-specificmortality 77 4.6 The local bilinearmodel 83 5 Dependence modeling 88 5.1 Introduction 88 5.2 Model structure 90 5.3 Model specification 92 5.4 Simulation 94 6 Pension insurance applications 94 6.1 Introduction 94 6.2 Annuity premium and risk analysis for a cohort aged 65 95 6.3 Annuity premium and risk analysis for multiple cohorts 106 6.4 Annuities fromthe customer's point of view 109 7 Discussion 113 8 Appendix 124 8.1 Model implementation example 124